DA6813-Final Project-Employee Attrition-Analysis & Top5 reasons
Sitharam Chilakapati
10/10/2020
Libraries
# Import Libraries
# Easy coding, splicing
library(tidyverse)
library(dplyr)
# Plots, Themes, Grids
library(ggplot2)
library(ggthemes)
library(cowplot)
# Correlation chart
library(PerformanceAnalytics)
# ML Models
library(caret)Dataset - Structure
# Read the input dataset CSV file
# Dataframe for analyis
empAttrn <- read.csv("../Data/HR-Employee-Attrition.csv")
# Take a copy for running models
df_attrn <- empAttrn
# Structure of our dataset
str(empAttrn)## 'data.frame': 1470 obs. of 35 variables:
## $ Age : int 41 49 37 33 27 32 59 30 38 36 ...
## $ Attrition : Factor w/ 2 levels "No","Yes": 2 1 2 1 1 1 1 1 1 1 ...
## $ BusinessTravel : Factor w/ 3 levels "Non-Travel","Travel_Frequently",..: 3 2 3 2 3 2 3 3 2 3 ...
## $ DailyRate : int 1102 279 1373 1392 591 1005 1324 1358 216 1299 ...
## $ Department : Factor w/ 3 levels "Human Resources",..: 3 2 2 2 2 2 2 2 2 2 ...
## $ DistanceFromHome : int 1 8 2 3 2 2 3 24 23 27 ...
## $ Education : int 2 1 2 4 1 2 3 1 3 3 ...
## $ EducationField : Factor w/ 6 levels "Human Resources",..: 2 2 5 2 4 2 4 2 2 4 ...
## $ EmployeeCount : int 1 1 1 1 1 1 1 1 1 1 ...
## $ EmployeeNumber : int 1 2 4 5 7 8 10 11 12 13 ...
## $ EnvironmentSatisfaction : int 2 3 4 4 1 4 3 4 4 3 ...
## $ Gender : Factor w/ 2 levels "Female","Male": 1 2 2 1 2 2 1 2 2 2 ...
## $ HourlyRate : int 94 61 92 56 40 79 81 67 44 94 ...
## $ JobInvolvement : int 3 2 2 3 3 3 4 3 2 3 ...
## $ JobLevel : int 2 2 1 1 1 1 1 1 3 2 ...
## $ JobRole : Factor w/ 9 levels "Healthcare Representative",..: 8 7 3 7 3 3 3 3 5 1 ...
## $ JobSatisfaction : int 4 2 3 3 2 4 1 3 3 3 ...
## $ MaritalStatus : Factor w/ 3 levels "Divorced","Married",..: 3 2 3 2 2 3 2 1 3 2 ...
## $ MonthlyIncome : int 5993 5130 2090 2909 3468 3068 2670 2693 9526 5237 ...
## $ MonthlyRate : int 19479 24907 2396 23159 16632 11864 9964 13335 8787 16577 ...
## $ NumCompaniesWorked : int 8 1 6 1 9 0 4 1 0 6 ...
## $ Over18 : Factor w/ 1 level "Y": 1 1 1 1 1 1 1 1 1 1 ...
## $ OverTime : Factor w/ 2 levels "No","Yes": 2 1 2 2 1 1 2 1 1 1 ...
## $ PercentSalaryHike : int 11 23 15 11 12 13 20 22 21 13 ...
## $ PerformanceRating : int 3 4 3 3 3 3 4 4 4 3 ...
## $ RelationshipSatisfaction: int 1 4 2 3 4 3 1 2 2 2 ...
## $ StandardHours : int 80 80 80 80 80 80 80 80 80 80 ...
## $ StockOptionLevel : int 0 1 0 0 1 0 3 1 0 2 ...
## $ TotalWorkingYears : int 8 10 7 8 6 8 12 1 10 17 ...
## $ TrainingTimesLastYear : int 0 3 3 3 3 2 3 2 2 3 ...
## $ WorkLifeBalance : int 1 3 3 3 3 2 2 3 3 2 ...
## $ YearsAtCompany : int 6 10 0 8 2 7 1 1 9 7 ...
## $ YearsInCurrentRole : int 4 7 0 7 2 7 0 0 7 7 ...
## $ YearsSinceLastPromotion : int 0 1 0 3 2 3 0 0 1 7 ...
## $ YearsWithCurrManager : int 5 7 0 0 2 6 0 0 8 7 ...Rows - n - 1470 Columns - p+1 - 35 (including response variable Attrition)
Class distribution
# Build Employee Attrition plots
options(repr.plot.width=8, repr.plot.height=4)
attritions_number <- empAttrn %>% group_by(Attrition) %>% summarise(Count=n()) %>%
ggplot(aes(x=Attrition, y=Count)) + geom_bar(stat="identity", fill="orange", color="grey40") + theme_bw() +
geom_text(aes(x=Attrition, y=0.01, label= Count),
hjust=0.5, vjust=-3, size=4,
colour="black", fontface="bold",
angle=360) + labs(title="Employee Attrition (Count)", x="Employee Attrition",y="Count") + theme(plot.title=element_text(hjust=0.5))## `summarise()` ungrouping output (override with `.groups` argument)attrition_percentage <- empAttrn %>% group_by(Attrition) %>% summarise(Count=n()) %>%
mutate(pct=round(prop.table(Count) * 100,2)) %>%
ggplot(aes(x=Attrition, y=pct)) + geom_bar(stat="identity", fill = "dodgerblue", color="grey40") +
geom_text(aes(x=Attrition, y=0.01, label= sprintf("%.2f%%", pct)),
hjust=0.5, vjust=-3, size=4,
colour="black", fontface="bold") + theme_bw() + labs(x="Employee Attrition", y="Percentage") +
labs(title="Employee Attrition (%)") + theme(plot.title=element_text(hjust=0.5))## `summarise()` ungrouping output (override with `.groups` argument)plot_grid(attritions_number, attrition_percentage, align="h", ncol=2)
Multi-Variate Analysis
1. Income and Job Satisfaction Analysis by Department
• How is employee distribution by each department?
Lets look at the employee distribution by department.
# Build Employee Distribution plots by Department
options(repr.plot.width=8, repr.plot.height=4)
# Employee counts by Department
emp_by_Dept <- empAttrn %>% group_by(Department) %>% summarise(Count=n()) %>%
ggplot(aes(x=Department, y=Count)) + geom_bar(stat="identity", fill="orange", color="grey40") + theme_bw() +
geom_text(aes(x=Department, y=0.01, label= Count),
hjust=0.5, vjust=-3, size=4,
colour="black", fontface="bold",
angle=360) + labs(title="Employees by Dept (Count)", x="Department",y="Count") + theme(plot.title=element_text(hjust=0.5))## `summarise()` ungrouping output (override with `.groups` argument)# Employee distribution by Department
emp_by_dept_pct <- empAttrn %>% group_by(Department) %>% summarise(Count=n()) %>%
mutate(pct=round(prop.table(Count) * 100,2)) %>%
ggplot(aes(x=Department, y=pct)) + geom_bar(stat="identity", fill = "dodgerblue", color="grey40") +
geom_text(aes(x=Department, y=0.01, label= sprintf("%.2f%%", pct)),
hjust=0.5, vjust=-3, size=4,
colour="black", fontface="bold") + theme_bw() + labs(x="Department", y="Percentage") +
labs(title="Employees by Dept (%)") + theme(plot.title=element_text(hjust=0.5))## `summarise()` ungrouping output (override with `.groups` argument)plot_grid(emp_by_Dept, emp_by_dept_pct, align="h", ncol=2)
R&D has the most number of employees in the dataset. Lets see if attrition follows the same pattern.
• How is attrition by each department and within each department?
# Attrition by department and within each Department
options(repr.plot.width=8, repr.plot.height=4)
# Create a dataframe by filtering the data for Dept and Attrition grouped by them. Then take percentage of it
options(repr.plot.width=15, repr.plot.height=5)
attr.Dept <- empAttrn %>% select(Department, Attrition) %>% group_by(Department, Attrition) %>% summarize(amount=n()) %>% mutate(pct=round(prop.table(amount)*100,0)) %>% arrange(pct)## `summarise()` regrouping output by 'Department' (override with `.groups` argument)# Overall Attrition by Department
yes.Attr.Dept <- attr.Dept %>% filter(Attrition == "Yes")
yes.Attr.Dept$pct <- NULL
yes.Attr.Dept %>% mutate (perc = round((amount/sum(yes.Attr.Dept$amount))*100,0)) -> yes.Attr.Dept
attritionBtDept <- yes.Attr.Dept %>%
ggplot(aes(x=Department, y=perc, fill=Department, color=Department)) +
geom_bar(stat="identity") + coord_flip() +
geom_label(aes(label=paste0(perc, "%"), fill = Department), colour = "white", fontface = "italic") +
labs(x="Department", y="Attrition (%)", title="Attrition by Department", subtitle="Overall")+ theme_wsj() +
theme(legend.position="none", plot.title=element_text(hjust=1.0,vjust=0.5,size=14),plot.subtitle=element_text(hjust=-0.5,vjust=0.5, size=11, face="italic"))
# Attrition within each department
attritionWithinDepts <- attr.Dept %>%
ggplot(aes(x=fct_reorder(Department,pct), y=pct, fill=Attrition, color=Attrition)) +
geom_bar(stat="identity") + facet_wrap(~Attrition) + coord_flip() +
geom_label(aes(label=paste0(pct, "%"), fill = Attrition), colour = "white", fontface = "italic") + scale_fill_manual(values=c("#2EF688", "#F63A2E")) + scale_color_manual(values=c("#09C873","#DD1509")) +
labs(x="", y="Attrition (%)", title="Attrition within Departments", subtitle="Percentage (%) with in each Dept")+ theme_wsj() +
theme(legend.position="none", plot.title=element_text(hjust=1.0, vjust=0.5,size=14), plot.subtitle=element_text(hjust=1.0, vjust=0.5,size=11, face="italic"))
plot_grid(attritionBtDept, attritionWithinDepts, align="h", axis = "b", ncol=2, rel_widths = c(1.5, 1.5))
It is implicit that R&D has highest percentage of employees. The above chart confirms that the attrition is also high in R&D. However, though R&D is highest in overall attrition in the given dataset, the percentage of attrition within R&D is less compared to that of Sales and HR.
• What is the mean monthly income and median income by department?
# Income analysis
# Calculate the Mean and Median monthly income of people by Attrition grouped by department
# Calculate Mean Income by Dept
mean.income.dept <- empAttrn %>% select(Department, MonthlyIncome, Attrition) %>% group_by(Attrition, Department) %>%
summarize(avg.inc.dept=mean(MonthlyIncome)) %>%
ggplot(aes(x=reorder(Department, avg.inc.dept), y=avg.inc.dept, fill=Attrition)) + geom_bar(stat="identity", position="dodge") + facet_wrap(~Attrition) +
theme_minimal() + theme(axis.text.x = element_text(angle = 90), plot.title=element_text(hjust=0.5)) +
scale_fill_manual(values=c("lightgreen", "tomato2")) +
labs(y="Mean Income", x="Department", title="Mean Income by Department \n and Attrition Status") +
geom_text(aes(x=Department, y=0.01, label= paste0("$ ", round(avg.inc.dept,2))),
hjust=-0.5, vjust=0, size=3,
colour="black", fontface="bold",
angle=90)## `summarise()` regrouping output by 'Attrition' (override with `.groups` argument)# Calculate Median Income by Dept
median.income.dept <- empAttrn %>% select(Department, MonthlyIncome, Attrition) %>% group_by(Attrition, Department) %>%
summarize(med.inc.dept=median(MonthlyIncome)) %>%
ggplot(aes(x=reorder(Department, med.inc.dept), y=med.inc.dept, fill=Attrition)) + geom_bar(stat="identity", position="dodge") + facet_wrap(~Attrition) +
theme_minimal() + theme(axis.text.x = element_text(angle = 90), plot.title=element_text(hjust=0.5)) +
scale_fill_manual(values=c("lightgreen", "tomato2")) +
labs(y="Median Income", x="Department", title="Median Income by Department \n and Attrition Status") +
geom_text(aes(x=Department, y=0.01, label= paste0("$ ", round(med.inc.dept,2))),
hjust=-0.5, vjust=0, size=3,
colour="black", fontface="bold",
angle=90)## `summarise()` regrouping output by 'Attrition' (override with `.groups` argument)plot_grid(mean.income.dept, median.income.dept, align="h", axis = "bt", ncol=2, rel_widths = c(1.5, 1.5))
First of all we can clearly say that the data is right skewed as mean income is greater than median income and this could potentially be due to outliers.
We can see huge differences in income by each department and attrition status. It is clear that the average/median income of people who left is quite less compared to those who didn’t. Also, the inference applies equally to all 3 departments. Lets confirm this trend using Job satisfaction based on median income.
• How is the job satisfaction based on income?
empAttrn$JobSatisfaction <- as.factor(empAttrn$JobSatisfaction)
empAttrn$JobSatisfaction = factor(empAttrn$JobSatisfaction,
levels = c('1', '2', '3','4'),
labels = c('Low', 'Medium', 'High','Very High'))
med.Inc.By.JS <- empAttrn %>% select(JobSatisfaction, MonthlyIncome, Attrition) %>% group_by(JobSatisfaction, Attrition) %>% summarize(medIncByJS=median(MonthlyIncome)) %>%
ggplot(aes(x=fct_reorder(JobSatisfaction, -medIncByJS), y=medIncByJS, color=Attrition)) +
geom_point(size=3) +
geom_segment(aes(x=JobSatisfaction,
xend=JobSatisfaction,
y=0,
yend=medIncByJS)) + facet_wrap(~Attrition) +
labs(title="Job Satisfaction levels based on Income?",
subtitle="by Attrition",
y="Median Income",
x="Job Satisfaction Level") +
theme(axis.text.x = element_text(angle=50, vjust=0.5), plot.title=element_text(hjust=0.5), strip.background = element_blank(),strip.text = element_blank()) +
coord_flip() + theme_clean() + scale_color_manual(values=c("#58FA58", "#FA5858")) +
geom_text(aes(x=JobSatisfaction, y=0.01, label= paste0("$ ", round(medIncByJS,1))),
hjust=-0.5, vjust=-0.5, size=4,
colour="black", fontface="italic",
angle=360)## `summarise()` regrouping output by 'JobSatisfaction' (override with `.groups` argument)med.Inc.By.JS
It seems the lower the job satisfaction (Low & Medium) the wider the gap by attrition status in the income levels.
So, we could conclude that Income and Job satisfaction has correlation with Attrition. Lower the Median income and lower the job satisfaction, higher the Attrition.
2. Gender Analysis:
• How is employee distribution by Gender?
Lets look at the employee distribution by Gender.
# Build Employee Distribution plots by Gender
# Employee counts by Gender
emp_by_Gender <- empAttrn %>% group_by(Gender) %>% summarise(Count=n()) %>%
ggplot(aes(x=Gender, y=Count)) + geom_bar(stat="identity", fill="orange", color="grey40") + theme_bw() +
geom_text(aes(x=Gender, y=0.01, label= Count),
hjust=0.5, vjust=-3, size=4,
colour="black", fontface="bold",
angle=360) + labs(title="Employees by Gender (Count)", x="Gender",y="Count") + theme(plot.title=element_text(hjust=0.5))## `summarise()` ungrouping output (override with `.groups` argument)# Employee distribution by Gender
emp_by_gender_pct <- empAttrn %>% group_by(Gender) %>% summarise(Count=n()) %>%
mutate(pct=round(prop.table(Count) * 100,2)) %>%
ggplot(aes(x=Gender, y=pct)) + geom_bar(stat="identity", fill = "dodgerblue", color="grey40") +
geom_text(aes(x=Gender, y=0.01, label= sprintf("%.2f%%", pct)),
hjust=0.5, vjust=-3, size=4,
colour="black", fontface="bold") + theme_bw() + labs(x="Gender", y="Percentage") +
labs(title="Employees by Gender (%)") + theme(plot.title=element_text(hjust=0.5))## `summarise()` ungrouping output (override with `.groups` argument)plot_grid(emp_by_Gender, emp_by_gender_pct, align="h", ncol=2)
The gender distribution is 40-60 between Female-Male.
• How is attrition by Gender and within Gender?
# Attrition by Gender and within each Gender
# Create a dataframe by filtering the data for Gender and Attrition grouped by them. Then take percentage of it
attr.Gender <- empAttrn %>% select(Gender, Attrition) %>% group_by(Gender, Attrition) %>% summarize(amount=n()) %>% mutate(pct=round(prop.table(amount)*100,0)) %>% arrange(pct)## `summarise()` regrouping output by 'Gender' (override with `.groups` argument)# Overall Attrition by Gender
yes.Attr.Gender <- attr.Gender %>% filter(Attrition == "Yes")
yes.Attr.Gender$pct <- NULL
yes.Attr.Gender %>% mutate (perc = round((amount/sum(yes.Attr.Gender$amount))*100,0)) -> yes.Attr.Gender
attritionByGender <- yes.Attr.Gender %>%
ggplot(aes(x=Gender, y=perc, fill=Gender, color=Gender)) +
geom_bar(stat="identity") + coord_flip() +
geom_label(aes(label=paste0(perc, "%"), fill = Gender), colour = "white", fontface = "italic") +
labs(x="Gender", y="Attrition (%)", title="Attrition by Gender", subtitle="Overall")+ theme_wsj() +
theme(legend.position="none", plot.title=element_text(hjust=1.0,vjust=0.5,size=14),plot.subtitle=element_text(hjust=0.5,vjust=0.5, size=11, face="italic"))
# Attrition within each Gender
attritionWithinGender <- attr.Gender %>%
ggplot(aes(x=fct_reorder(Gender,pct), y=pct, fill=Attrition, color=Attrition)) +
geom_bar(stat="identity") + facet_wrap(~Attrition) + coord_flip() +
geom_label(aes(label=paste0(pct, "%"), fill = Attrition), colour = "white", fontface = "italic") + scale_fill_manual(values=c("#2EF688", "#F63A2E")) + scale_color_manual(values=c("#09C873","#DD1509")) +
labs(x="", y="Attrition (%)", title="Attrition within Genders", subtitle="Percentage (%) with in each Gender")+ theme_wsj() +
theme(legend.position="none", plot.title=element_text(hjust=1.0, vjust=0.5,size=14), plot.subtitle=element_text(hjust=1.0, vjust=0.5,size=11, face="italic"))
plot_grid(attritionByGender, attritionWithinGender, align="h", axis = "b", ncol=2, rel_widths = c(1.5, 1.5))
It seems overall attrition in males is higher than the female and also with in the distribution.
• What is the average salary by gender? Are there any gender disparities in income?
inc.gender <- empAttrn %>% ggplot(aes(x=Gender, y=MonthlyIncome, color=Gender, fill=Gender)) + geom_boxplot() +
scale_fill_manual(values=c("#F5A9F2", "#5882FA")) + scale_color_manual(values=c("#FE2EF7", "#5858FA")) +
coord_flip() + labs(title="Overall Income by Gender")
inc.gender
The median income for both genders are pretty close. However, as expected and seen before in department, the income distributions are skewed to the right where mean is greater than median.
# Income analysis
# Calculate the Mean and Median monthly income of people by Attrition grouped by Gender
# Calculate Mean Income by Dept
mean.income.gender <- empAttrn %>% select(Gender, MonthlyIncome, Attrition) %>% group_by(Attrition, Gender) %>%
summarize(avg.inc.gender=mean(MonthlyIncome)) %>%
ggplot(aes(x=reorder(Gender, avg.inc.gender), y=avg.inc.gender, fill=Attrition)) + geom_bar(stat="identity", position="dodge") + facet_wrap(~Attrition) +
theme_minimal() + theme(axis.text.x = element_text(angle = 90), plot.title=element_text(hjust=0.5)) +
scale_fill_manual(values=c("lightgreen", "tomato2")) +
labs(y="Mean Income", x="Gender", title="Mean Income by Gender \n and Attrition Status") +
geom_text(aes(x=Gender, y=0.01, label= paste0("$ ", round(avg.inc.gender,2))),
hjust=-0.5, vjust=0, size=3,
colour="black", fontface="bold",
angle=90)## `summarise()` regrouping output by 'Attrition' (override with `.groups` argument)# Calculate Median Income by Dept
median.income.gender <- empAttrn %>% select(Gender, MonthlyIncome, Attrition) %>% group_by(Attrition, Gender) %>%
summarize(med.inc.gender=median(MonthlyIncome)) %>%
ggplot(aes(x=reorder(Gender, med.inc.gender), y=med.inc.gender, fill=Attrition)) + geom_bar(stat="identity", position="dodge") + facet_wrap(~Attrition) +
theme_minimal() + theme(axis.text.x = element_text(angle = 90), plot.title=element_text(hjust=0.5)) +
scale_fill_manual(values=c("lightgreen", "tomato2")) +
labs(y="Median Income", x="Gender", title="Median Income by Gender \n and Attrition Status") +
geom_text(aes(x=Gender, y=0.01, label= paste0("$ ", round(med.inc.gender,2))),
hjust=-0.5, vjust=0, size=3,
colour="black", fontface="bold",
angle=90)## `summarise()` regrouping output by 'Attrition' (override with `.groups` argument)plot_grid(mean.income.gender, median.income.gender, align="h", axis = "bt", ncol=2, rel_widths = c(1.5, 1.5))
It is evident that the employees who quit have less income than the ones who didn’t, irrespective of the gender. However, this could potentially be because of skewed income distribution noticed above.
• What is the age distribution? Are there any gender disparities in age?
# Build density plots for Gender distribution by Age
# Calculate the mean Set the data text for the labels for each gender
mean.age = round(mean(empAttrn$Age),2)
mean.male = round(mean((empAttrn %>% filter(Gender == 'Male'))$Age),2)
mean.female = round(mean((empAttrn %>% filter(Gender == 'Female'))$Age),2)
dat_text <- data.frame(
label = c("Mean = 37.33 \n Years Old", "Mean = 36.65 \n Years Old"),
Gender = c("Female", "Male")
)
# overall distribution by age
overall.age.dist <- empAttrn %>% select(Gender, Age) %>% filter(!is.na(Age)) %>%
ggplot(data=empAttrn, mapping=aes(x=Age)) + geom_density(color="darkblue", fill="lightblue") +
geom_vline(aes(xintercept=mean(Age)), color="red", linetype="dashed", size=1) +
theme_clean() + theme(plot.title=element_text(hjust=0.5)) +
labs(title="Age Distribution") + labs(x="Overall Age") +
annotate("text", label = "Mean = 36.92 Years Old", x = 50, y = 0.03, color = "black")
# gender distribution by age
gender.dist <- empAttrn %>% select(Gender, Age) %>% filter(Gender == 'Male' | Gender== "Female") %>%
filter(!is.na(Age)) %>% group_by(Gender) %>%
ggplot(aes(x=Age)) + geom_density(aes(fill=Gender), alpha=0.8, show.legend=FALSE) + facet_wrap(~Gender) +
theme_clean() + theme(plot.title=element_text(hjust=0.5)) +
geom_vline(aes(xintercept=mean(Age)), color="red", linetype="dashed", size=1) +
labs(title="Age Distribution by Gender") + scale_fill_manual(values=c("#F781F3", "#819FF7")) +
geom_text(
data = dat_text,
mapping = aes(x = 45, y = -0.0001, label = label),
hjust = -0.1,
vjust = -1
)
plot_grid(overall.age.dist, gender.dist, nrow=2)
empAttrn## Age Attrition BusinessTravel DailyRate Department
## 1 41 Yes Travel_Rarely 1102 Sales
## 2 49 No Travel_Frequently 279 Research & Development
## 3 37 Yes Travel_Rarely 1373 Research & Development
## 4 33 No Travel_Frequently 1392 Research & Development
## 5 27 No Travel_Rarely 591 Research & Development
## 6 32 No Travel_Frequently 1005 Research & Development
## 7 59 No Travel_Rarely 1324 Research & Development
## 8 30 No Travel_Rarely 1358 Research & Development
## 9 38 No Travel_Frequently 216 Research & Development
## 10 36 No Travel_Rarely 1299 Research & Development
## 11 35 No Travel_Rarely 809 Research & Development
## 12 29 No Travel_Rarely 153 Research & Development
## 13 31 No Travel_Rarely 670 Research & Development
## 14 34 No Travel_Rarely 1346 Research & Development
## 15 28 Yes Travel_Rarely 103 Research & Development
## 16 29 No Travel_Rarely 1389 Research & Development
## 17 32 No Travel_Rarely 334 Research & Development
## 18 22 No Non-Travel 1123 Research & Development
## 19 53 No Travel_Rarely 1219 Sales
## 20 38 No Travel_Rarely 371 Research & Development
## 21 24 No Non-Travel 673 Research & Development
## 22 36 Yes Travel_Rarely 1218 Sales
## 23 34 No Travel_Rarely 419 Research & Development
## 24 21 No Travel_Rarely 391 Research & Development
## 25 34 Yes Travel_Rarely 699 Research & Development
## 26 53 No Travel_Rarely 1282 Research & Development
## 27 32 Yes Travel_Frequently 1125 Research & Development
## 28 42 No Travel_Rarely 691 Sales
## 29 44 No Travel_Rarely 477 Research & Development
## 30 46 No Travel_Rarely 705 Sales
## 31 33 No Travel_Rarely 924 Research & Development
## 32 44 No Travel_Rarely 1459 Research & Development
## 33 30 No Travel_Rarely 125 Research & Development
## 34 39 Yes Travel_Rarely 895 Sales
## 35 24 Yes Travel_Rarely 813 Research & Development
## 36 43 No Travel_Rarely 1273 Research & Development
## 37 50 Yes Travel_Rarely 869 Sales
## 38 35 No Travel_Rarely 890 Sales
## 39 36 No Travel_Rarely 852 Research & Development
## 40 33 No Travel_Frequently 1141 Sales
## 41 35 No Travel_Rarely 464 Research & Development
## 42 27 No Travel_Rarely 1240 Research & Development
## 43 26 Yes Travel_Rarely 1357 Research & Development
## 44 27 No Travel_Frequently 994 Sales
## 45 30 No Travel_Frequently 721 Research & Development
## 46 41 Yes Travel_Rarely 1360 Research & Development
## 47 34 No Non-Travel 1065 Sales
## 48 37 No Travel_Rarely 408 Research & Development
## 49 46 No Travel_Frequently 1211 Sales
## 50 35 No Travel_Rarely 1229 Research & Development
## 51 48 Yes Travel_Rarely 626 Research & Development
## 52 28 Yes Travel_Rarely 1434 Research & Development
## 53 44 No Travel_Rarely 1488 Sales
## 54 35 No Non-Travel 1097 Research & Development
## 55 26 No Travel_Rarely 1443 Sales
## 56 33 No Travel_Frequently 515 Research & Development
## 57 35 No Travel_Frequently 853 Sales
## 58 35 No Travel_Rarely 1142 Research & Development
## 59 31 No Travel_Rarely 655 Research & Development
## 60 37 No Travel_Rarely 1115 Research & Development
## 61 32 No Travel_Rarely 427 Research & Development
## 62 38 No Travel_Frequently 653 Research & Development
## 63 50 No Travel_Rarely 989 Research & Development
## 64 59 No Travel_Rarely 1435 Sales
## 65 36 No Travel_Rarely 1223 Research & Development
## 66 55 No Travel_Rarely 836 Research & Development
## 67 36 No Travel_Frequently 1195 Research & Development
## 68 45 No Travel_Rarely 1339 Research & Development
## 69 35 No Travel_Frequently 664 Research & Development
## 70 36 Yes Travel_Rarely 318 Research & Development
## 71 59 No Travel_Frequently 1225 Sales
## 72 29 No Travel_Rarely 1328 Research & Development
## 73 31 No Travel_Rarely 1082 Research & Development
## 74 32 No Travel_Rarely 548 Research & Development
## 75 36 No Travel_Rarely 132 Research & Development
## 76 31 No Travel_Rarely 746 Research & Development
## 77 35 No Travel_Rarely 776 Sales
## 78 45 No Travel_Rarely 193 Research & Development
## 79 37 No Travel_Rarely 397 Research & Development
## 80 46 No Travel_Rarely 945 Human Resources
## 81 30 No Travel_Rarely 852 Research & Development
## 82 35 No Travel_Rarely 1214 Research & Development
## 83 55 No Travel_Rarely 111 Sales
## 84 38 No Non-Travel 573 Research & Development
## 85 34 No Travel_Rarely 1153 Research & Development
## 86 56 No Travel_Rarely 1400 Research & Development
## 87 23 No Travel_Rarely 541 Sales
## 88 51 No Travel_Rarely 432 Research & Development
## 89 30 No Travel_Rarely 288 Research & Development
## 90 46 Yes Travel_Rarely 669 Sales
## 91 40 No Travel_Frequently 530 Research & Development
## 92 51 No Travel_Rarely 632 Sales
## 93 30 No Travel_Rarely 1334 Sales
## 94 46 No Travel_Frequently 638 Research & Development
## 95 32 No Travel_Rarely 1093 Sales
## 96 54 No Travel_Rarely 1217 Research & Development
## 97 24 No Travel_Rarely 1353 Sales
## 98 28 No Non-Travel 120 Sales
## 99 58 No Travel_Rarely 682 Sales
## 100 44 No Non-Travel 489 Research & Development
## 101 37 Yes Travel_Rarely 807 Human Resources
## 102 32 No Travel_Rarely 827 Research & Development
## 103 20 Yes Travel_Frequently 871 Research & Development
## 104 34 No Travel_Rarely 665 Research & Development
## 105 37 No Non-Travel 1040 Research & Development
## 106 59 No Non-Travel 1420 Human Resources
## 107 50 No Travel_Frequently 1115 Research & Development
## 108 25 Yes Travel_Rarely 240 Sales
## 109 25 No Travel_Rarely 1280 Research & Development
## 110 22 No Travel_Rarely 534 Research & Development
## 111 51 No Travel_Frequently 1456 Research & Development
## 112 34 Yes Travel_Frequently 658 Research & Development
## 113 54 No Non-Travel 142 Human Resources
## 114 24 No Travel_Rarely 1127 Research & Development
## 115 34 No Travel_Rarely 1031 Research & Development
## 116 37 No Travel_Rarely 1189 Sales
## 117 34 No Travel_Rarely 1354 Research & Development
## 118 36 No Travel_Frequently 1467 Sales
## 119 36 No Travel_Rarely 922 Research & Development
## 120 43 No Travel_Frequently 394 Sales
## 121 30 No Travel_Frequently 1312 Research & Development
## 122 33 No Non-Travel 750 Sales
## 123 56 Yes Travel_Rarely 441 Research & Development
## 124 51 No Travel_Rarely 684 Research & Development
## 125 31 Yes Travel_Rarely 249 Sales
## 126 26 No Travel_Rarely 841 Research & Development
## 127 58 Yes Travel_Rarely 147 Research & Development
## 128 19 Yes Travel_Rarely 528 Sales
## 129 22 No Travel_Rarely 594 Research & Development
## 130 49 No Travel_Rarely 470 Research & Development
## 131 43 No Travel_Frequently 957 Research & Development
## 132 50 No Travel_Frequently 809 Sales
## 133 31 Yes Travel_Rarely 542 Sales
## 134 41 No Travel_Rarely 802 Sales
## 135 26 No Travel_Rarely 1355 Human Resources
## 136 36 No Travel_Rarely 216 Research & Development
## 137 51 Yes Travel_Frequently 1150 Research & Development
## 138 39 No Travel_Rarely 1329 Sales
## 139 25 No Travel_Rarely 959 Sales
## 140 30 No Travel_Rarely 1240 Human Resources
## 141 32 Yes Travel_Rarely 1033 Research & Development
## 142 45 No Travel_Rarely 1316 Research & Development
## 143 38 No Travel_Rarely 364 Research & Development
## 144 30 No Travel_Rarely 438 Research & Development
## 145 32 No Travel_Frequently 689 Sales
## 146 30 No Travel_Rarely 201 Research & Development
## 147 30 No Travel_Rarely 1427 Research & Development
## 148 41 No Travel_Frequently 857 Research & Development
## 149 41 No Travel_Rarely 933 Research & Development
## 150 19 No Travel_Rarely 1181 Research & Development
## 151 40 No Travel_Frequently 1395 Research & Development
## 152 35 No Travel_Rarely 662 Sales
## 153 53 No Travel_Rarely 1436 Sales
## 154 45 No Travel_Rarely 194 Research & Development
## 155 32 No Travel_Frequently 967 Sales
## 156 29 No Non-Travel 1496 Research & Development
## 157 51 No Travel_Rarely 1169 Research & Development
## 158 58 No Travel_Rarely 1145 Research & Development
## 159 40 No Travel_Rarely 630 Sales
## 160 34 No Travel_Frequently 303 Sales
## 161 22 No Travel_Rarely 1256 Research & Development
## 162 27 No Non-Travel 691 Research & Development
## 163 28 No Travel_Rarely 440 Research & Development
## 164 57 No Travel_Rarely 334 Research & Development
## 165 27 No Non-Travel 1450 Research & Development
## 166 50 No Travel_Rarely 1452 Research & Development
## 167 41 No Travel_Rarely 465 Research & Development
## 168 30 No Travel_Rarely 1339 Sales
## 169 38 No Travel_Rarely 702 Sales
## 170 32 No Travel_Rarely 120 Research & Development
## 171 27 No Travel_Rarely 1157 Research & Development
## 172 19 Yes Travel_Frequently 602 Sales
## 173 36 No Travel_Frequently 1480 Research & Development
## 174 30 No Non-Travel 111 Research & Development
## 175 45 No Travel_Rarely 1268 Sales
## 176 56 No Travel_Rarely 713 Research & Development
## 177 33 No Travel_Rarely 134 Research & Development
## 178 19 Yes Travel_Rarely 303 Research & Development
## 179 46 No Travel_Rarely 526 Sales
## 180 38 No Travel_Rarely 1380 Research & Development
## 181 31 No Travel_Rarely 140 Research & Development
## 182 34 No Travel_Rarely 629 Research & Development
## 183 41 Yes Travel_Rarely 1356 Sales
## 184 50 No Travel_Rarely 328 Research & Development
## 185 53 No Travel_Rarely 1084 Research & Development
## 186 33 No Travel_Rarely 931 Research & Development
## 187 40 No Travel_Rarely 989 Research & Development
## 188 55 No Travel_Rarely 692 Research & Development
## 189 34 No Travel_Frequently 1069 Research & Development
## 190 51 No Travel_Rarely 313 Research & Development
## 191 52 No Travel_Rarely 699 Research & Development
## 192 27 No Travel_Rarely 894 Research & Development
## 193 35 Yes Travel_Rarely 556 Research & Development
## 194 43 No Non-Travel 1344 Research & Development
## 195 45 No Non-Travel 1195 Research & Development
## 196 37 No Travel_Rarely 290 Research & Development
## 197 35 No Travel_Frequently 138 Research & Development
## 198 42 No Non-Travel 926 Research & Development
## 199 38 No Travel_Rarely 1261 Research & Development
## 200 38 No Travel_Rarely 1084 Research & Development
## 201 27 No Travel_Frequently 472 Research & Development
## 202 49 No Non-Travel 1002 Research & Development
## 203 34 No Travel_Frequently 878 Research & Development
## 204 40 No Travel_Rarely 905 Research & Development
## 205 38 Yes Travel_Rarely 1180 Research & Development
## 206 29 Yes Travel_Rarely 121 Sales
## 207 22 No Travel_Rarely 1136 Research & Development
## 208 36 No Travel_Frequently 635 Research & Development
## 209 40 No Non-Travel 1151 Research & Development
## 210 46 No Travel_Rarely 644 Research & Development
## 211 32 Yes Travel_Rarely 1045 Sales
## 212 30 No Non-Travel 829 Research & Development
## 213 27 No Travel_Frequently 1242 Sales
## 214 51 No Travel_Rarely 1469 Research & Development
## 215 30 Yes Travel_Rarely 1005 Research & Development
## 216 41 No Travel_Rarely 896 Sales
## 217 30 Yes Travel_Frequently 334 Sales
## 218 29 Yes Travel_Rarely 992 Research & Development
## 219 45 No Non-Travel 1052 Sales
## 220 54 No Travel_Rarely 1147 Sales
## 221 36 No Travel_Rarely 1396 Research & Development
## 222 33 No Travel_Rarely 147 Research & Development
## 223 37 No Travel_Frequently 663 Research & Development
## 224 38 No Travel_Rarely 119 Sales
## 225 31 No Non-Travel 979 Research & Development
## 226 59 No Travel_Rarely 142 Research & Development
## 227 37 No Travel_Frequently 319 Sales
## 228 29 No Travel_Frequently 1413 Sales
## 229 35 No Travel_Frequently 944 Sales
## 230 29 Yes Travel_Rarely 896 Research & Development
## 231 52 No Travel_Rarely 1323 Research & Development
## 232 42 No Travel_Rarely 532 Research & Development
## 233 59 No Travel_Rarely 818 Human Resources
## 234 50 No Travel_Rarely 854 Sales
## 235 33 Yes Travel_Rarely 813 Research & Development
## 236 43 No Travel_Rarely 1034 Sales
## 237 33 Yes Travel_Rarely 465 Research & Development
## 238 52 No Non-Travel 771 Sales
## 239 32 No Travel_Rarely 1401 Sales
## 240 32 Yes Travel_Rarely 515 Research & Development
## 241 39 No Travel_Rarely 1431 Research & Development
## 242 32 No Non-Travel 976 Sales
## 243 41 No Travel_Rarely 1411 Research & Development
## 244 40 No Travel_Rarely 1300 Research & Development
## 245 45 No Travel_Rarely 252 Research & Development
## 246 31 No Travel_Frequently 1327 Research & Development
## 247 33 No Travel_Rarely 832 Research & Development
## 248 34 No Travel_Rarely 470 Research & Development
## 249 37 No Travel_Rarely 1017 Research & Development
## 250 45 No Travel_Frequently 1199 Research & Development
## 251 37 Yes Travel_Frequently 504 Research & Development
## 252 39 No Travel_Frequently 505 Research & Development
## 253 29 No Travel_Rarely 665 Research & Development
## 254 42 No Travel_Rarely 916 Research & Development
## 255 29 No Travel_Rarely 1247 Sales
## 256 25 No Travel_Rarely 685 Research & Development
## 257 42 No Travel_Rarely 269 Research & Development
## 258 40 No Travel_Rarely 1416 Research & Development
## 259 51 No Travel_Rarely 833 Research & Development
## 260 31 Yes Travel_Frequently 307 Research & Development
## 261 32 No Travel_Frequently 1311 Research & Development
## 262 38 No Non-Travel 1327 Sales
## 263 32 No Travel_Rarely 128 Research & Development
## 264 46 No Travel_Rarely 488 Sales
## 265 28 Yes Travel_Rarely 529 Research & Development
## 266 29 No Travel_Rarely 1210 Sales
## 267 31 No Travel_Rarely 1463 Research & Development
## 268 25 No Non-Travel 675 Research & Development
## 269 45 No Travel_Rarely 1385 Research & Development
## 270 36 No Travel_Rarely 1403 Research & Development
## 271 55 No Travel_Rarely 452 Research & Development
## 272 47 Yes Non-Travel 666 Research & Development
## 273 28 No Travel_Rarely 1158 Research & Development
## 274 37 No Travel_Rarely 228 Sales
## 275 21 No Travel_Rarely 996 Research & Development
## 276 37 No Non-Travel 728 Research & Development
## 277 35 No Travel_Rarely 1315 Research & Development
## 278 38 No Travel_Rarely 322 Sales
## 279 26 No Travel_Frequently 1479 Research & Development
## 280 50 No Travel_Rarely 797 Research & Development
## 281 53 No Travel_Rarely 1070 Research & Development
## 282 42 No Travel_Rarely 635 Sales
## 283 29 No Travel_Frequently 442 Sales
## 284 55 No Travel_Rarely 147 Research & Development
## 285 26 No Travel_Frequently 496 Research & Development
## 286 37 No Travel_Rarely 1372 Research & Development
## 287 44 Yes Travel_Frequently 920 Research & Development
## 288 38 No Travel_Rarely 688 Research & Development
## 289 26 Yes Travel_Rarely 1449 Research & Development
## 290 28 No Travel_Rarely 1117 Research & Development
## 291 49 No Travel_Frequently 636 Research & Development
## 292 36 No Travel_Rarely 506 Research & Development
## 293 31 No Travel_Frequently 444 Sales
## 294 26 Yes Travel_Rarely 950 Sales
## 295 37 No Travel_Frequently 889 Research & Development
## 296 42 No Travel_Frequently 555 Sales
## 297 18 Yes Travel_Rarely 230 Research & Development
## 298 35 No Travel_Rarely 1232 Sales
## 299 36 No Travel_Frequently 566 Research & Development
## 300 51 No Travel_Rarely 1302 Research & Development
## 301 41 No Travel_Rarely 334 Sales
## 302 18 No Travel_Rarely 812 Sales
## 303 28 No Travel_Rarely 1476 Research & Development
## 304 31 No Travel_Rarely 218 Sales
## 305 39 No Travel_Rarely 1132 Research & Development
## 306 36 No Non-Travel 1105 Research & Development
## 307 32 No Travel_Rarely 906 Sales
## 308 38 No Travel_Rarely 849 Research & Development
## 309 58 No Non-Travel 390 Research & Development
## 310 31 No Travel_Rarely 691 Research & Development
## 311 31 No Travel_Rarely 106 Human Resources
## 312 45 No Travel_Frequently 1249 Research & Development
## 313 31 No Travel_Rarely 192 Research & Development
## 314 33 No Travel_Frequently 553 Research & Development
## 315 39 No Travel_Rarely 117 Research & Development
## 316 43 No Travel_Frequently 185 Research & Development
## 317 49 No Travel_Rarely 1091 Research & Development
## 318 52 Yes Travel_Rarely 723 Research & Development
## 319 27 No Travel_Rarely 1220 Research & Development
## 320 32 No Travel_Rarely 588 Sales
## 321 27 No Travel_Rarely 1377 Sales
## 322 31 No Travel_Rarely 691 Sales
## 323 32 No Travel_Rarely 1018 Research & Development
## 324 28 Yes Travel_Rarely 1157 Research & Development
## 325 30 No Travel_Rarely 1275 Research & Development
## 326 31 No Travel_Frequently 798 Research & Development
## 327 39 No Travel_Frequently 672 Research & Development
## 328 39 Yes Travel_Rarely 1162 Sales
## 329 33 No Travel_Frequently 508 Sales
## 330 47 No Travel_Rarely 1482 Research & Development
## 331 43 No Travel_Frequently 559 Research & Development
## 332 27 No Non-Travel 210 Sales
## 333 54 No Travel_Frequently 928 Research & Development
## 334 43 No Travel_Rarely 1001 Research & Development
## 335 45 No Travel_Rarely 549 Research & Development
## 336 40 No Travel_Rarely 1124 Sales
## 337 29 Yes Travel_Rarely 318 Research & Development
## 338 29 No Travel_Rarely 738 Research & Development
## 339 30 No Travel_Rarely 570 Sales
## 340 27 No Travel_Rarely 1130 Sales
## 341 37 No Travel_Rarely 1192 Research & Development
## 342 38 No Travel_Rarely 343 Research & Development
## 343 31 No Travel_Rarely 1232 Research & Development
## 344 29 No Travel_Rarely 144 Sales
## 345 35 No Travel_Rarely 1296 Research & Development
## 346 23 No Travel_Rarely 1309 Research & Development
## 347 41 No Travel_Rarely 483 Research & Development
## 348 47 No Travel_Frequently 1309 Sales
## 349 42 No Travel_Rarely 810 Research & Development
## 350 29 No Non-Travel 746 Sales
## 351 42 No Travel_Rarely 544 Human Resources
## 352 32 No Travel_Rarely 1062 Research & Development
## 353 48 No Travel_Rarely 530 Sales
## 354 37 No Travel_Rarely 1319 Research & Development
## 355 30 No Non-Travel 641 Sales
## 356 26 No Travel_Rarely 933 Sales
## 357 42 No Travel_Rarely 1332 Research & Development
## 358 21 Yes Travel_Frequently 756 Sales
## 359 36 No Non-Travel 845 Sales
## 360 36 No Travel_Frequently 541 Sales
## 361 57 No Travel_Rarely 593 Research & Development
## 362 40 No Travel_Rarely 1171 Research & Development
## 363 21 No Non-Travel 895 Sales
## 364 33 Yes Travel_Rarely 350 Sales
## 365 37 No Travel_Rarely 921 Research & Development
## 366 46 No Non-Travel 1144 Research & Development
## 367 41 Yes Travel_Frequently 143 Sales
## 368 50 No Travel_Rarely 1046 Research & Development
## 369 40 Yes Travel_Rarely 575 Sales
## 370 31 No Travel_Rarely 408 Research & Development
## 371 21 Yes Travel_Rarely 156 Sales
## 372 29 No Travel_Rarely 1283 Research & Development
## 373 35 No Travel_Rarely 755 Research & Development
## 374 27 No Travel_Rarely 1469 Research & Development
## 375 28 No Travel_Rarely 304 Sales
## 376 49 No Travel_Rarely 1261 Research & Development
## 377 51 No Travel_Rarely 1178 Sales
## 378 36 No Travel_Rarely 329 Research & Development
## 379 34 Yes Non-Travel 1362 Sales
## 380 55 No Travel_Rarely 1311 Research & Development
## 381 24 No Travel_Rarely 1371 Sales
## 382 30 No Travel_Rarely 202 Sales
## 383 26 Yes Travel_Frequently 575 Research & Development
## 384 22 No Travel_Rarely 253 Research & Development
## 385 36 No Travel_Rarely 164 Sales
## 386 30 Yes Travel_Frequently 464 Research & Development
## 387 37 No Travel_Rarely 1107 Research & Development
## 388 40 No Travel_Rarely 759 Sales
## 389 42 No Travel_Rarely 201 Research & Development
## 390 37 No Travel_Rarely 1305 Research & Development
## 391 43 No Travel_Rarely 982 Research & Development
## 392 40 No Travel_Rarely 555 Research & Development
## 393 54 No Travel_Rarely 821 Research & Development
## 394 34 No Non-Travel 1381 Sales
## 395 31 No Travel_Rarely 480 Research & Development
## 396 43 No Travel_Frequently 313 Research & Development
## 397 43 No Travel_Rarely 1473 Research & Development
## 398 25 No Travel_Rarely 891 Sales
## 399 37 No Non-Travel 1063 Research & Development
## 400 31 No Travel_Rarely 329 Research & Development
## 401 39 No Travel_Frequently 1218 Research & Development
## 402 56 No Travel_Frequently 906 Sales
## 403 30 No Travel_Rarely 1082 Sales
## 404 41 No Travel_Rarely 645 Sales
## 405 28 No Travel_Rarely 1300 Research & Development
## 406 25 Yes Travel_Rarely 688 Research & Development
## 407 52 No Travel_Rarely 319 Research & Development
## 408 45 No Travel_Rarely 192 Research & Development
## 409 52 No Travel_Rarely 1490 Research & Development
## 410 42 No Travel_Frequently 532 Research & Development
## 411 30 No Travel_Rarely 317 Research & Development
## 412 60 No Travel_Rarely 422 Research & Development
## 413 46 No Travel_Rarely 1485 Research & Development
## 414 42 No Travel_Frequently 1368 Research & Development
## 415 24 Yes Travel_Rarely 1448 Sales
## 416 34 Yes Travel_Frequently 296 Sales
## 417 38 No Travel_Frequently 1490 Research & Development
## 418 40 No Travel_Rarely 1398 Sales
## 419 26 No Travel_Rarely 1349 Research & Development
## 420 30 No Non-Travel 1400 Research & Development
## 421 29 No Travel_Rarely 986 Research & Development
## 422 29 Yes Travel_Rarely 408 Research & Development
## 423 19 Yes Travel_Rarely 489 Human Resources
## 424 30 No Non-Travel 1398 Sales
## 425 57 No Travel_Rarely 210 Sales
## 426 50 No Travel_Rarely 1099 Research & Development
## 427 30 No Non-Travel 1116 Research & Development
## 428 60 No Travel_Frequently 1499 Sales
## 429 47 No Travel_Rarely 983 Research & Development
## 430 46 No Travel_Rarely 1009 Research & Development
## 431 35 No Travel_Rarely 144 Research & Development
## 432 54 No Travel_Rarely 548 Research & Development
## 433 34 No Travel_Rarely 1303 Research & Development
## 434 46 No Travel_Rarely 1125 Sales
## 435 31 No Travel_Rarely 1274 Research & Development
## 436 33 Yes Travel_Rarely 1277 Research & Development
## 437 33 Yes Travel_Rarely 587 Research & Development
## 438 30 No Travel_Rarely 413 Sales
## 439 35 No Travel_Rarely 1276 Research & Development
## 440 31 Yes Travel_Frequently 534 Research & Development
## 441 34 Yes Travel_Frequently 988 Human Resources
## 442 42 No Travel_Frequently 1474 Research & Development
## 443 36 No Non-Travel 635 Sales
## 444 22 Yes Travel_Frequently 1368 Research & Development
## 445 48 No Travel_Rarely 163 Sales
## 446 55 No Travel_Rarely 1117 Sales
## 447 41 No Non-Travel 267 Sales
## 448 35 No Travel_Rarely 619 Sales
## 449 40 No Travel_Rarely 302 Research & Development
## 450 39 No Travel_Frequently 443 Research & Development
## 451 31 No Travel_Rarely 828 Sales
## 452 42 No Travel_Rarely 319 Research & Development
## 453 45 No Travel_Rarely 561 Sales
## 454 26 Yes Travel_Frequently 426 Human Resources
## 455 29 No Travel_Rarely 232 Research & Development
## 456 33 No Travel_Rarely 922 Research & Development
## 457 31 No Travel_Rarely 688 Sales
## 458 18 Yes Travel_Frequently 1306 Sales
## 459 40 No Non-Travel 1094 Sales
## 460 41 No Non-Travel 509 Research & Development
## 461 26 No Travel_Rarely 775 Sales
## 462 35 No Travel_Rarely 195 Sales
## 463 34 No Travel_Rarely 258 Sales
## 464 26 Yes Travel_Rarely 471 Research & Development
## 465 37 No Travel_Rarely 799 Research & Development
## 466 46 No Travel_Frequently 1034 Research & Development
## 467 41 No Travel_Rarely 1276 Sales
## 468 37 No Non-Travel 142 Sales
## 469 52 No Travel_Rarely 956 Research & Development
## 470 32 Yes Non-Travel 1474 Sales
## 471 24 No Travel_Frequently 535 Sales
## 472 38 No Travel_Rarely 1495 Research & Development
## 473 37 No Travel_Rarely 446 Research & Development
## 474 49 No Travel_Rarely 1245 Research & Development
## 475 24 No Travel_Rarely 691 Research & Development
## 476 26 No Travel_Rarely 703 Sales
## 477 24 No Travel_Rarely 823 Research & Development
## 478 50 No Travel_Frequently 1246 Human Resources
## 479 25 No Travel_Rarely 622 Sales
## 480 24 Yes Travel_Frequently 1287 Research & Development
## 481 30 Yes Travel_Frequently 448 Sales
## 482 34 No Travel_Rarely 254 Research & Development
## 483 31 Yes Travel_Rarely 1365 Sales
## 484 35 No Travel_Rarely 538 Research & Development
## 485 31 No Travel_Rarely 525 Sales
## 486 27 No Travel_Rarely 798 Research & Development
## 487 37 No Travel_Rarely 558 Sales
## 488 20 No Travel_Rarely 959 Research & Development
## 489 42 No Travel_Rarely 622 Research & Development
## 490 43 No Travel_Rarely 782 Research & Development
## 491 38 No Travel_Rarely 362 Research & Development
## 492 43 No Travel_Frequently 1001 Research & Development
## 493 48 No Travel_Rarely 1236 Research & Development
## 494 44 No Travel_Rarely 1112 Human Resources
## 495 34 No Travel_Rarely 204 Sales
## 496 27 Yes Travel_Rarely 1420 Sales
## 497 21 No Travel_Rarely 1343 Sales
## 498 44 No Travel_Rarely 1315 Research & Development
## 499 22 No Travel_Rarely 604 Research & Development
## 500 33 No Travel_Rarely 1216 Sales
## 501 32 No Travel_Rarely 646 Research & Development
## 502 30 No Travel_Frequently 160 Research & Development
## 503 53 No Travel_Rarely 238 Sales
## 504 34 No Travel_Rarely 1397 Research & Development
## 505 45 Yes Travel_Frequently 306 Sales
## 506 26 No Travel_Rarely 991 Research & Development
## 507 37 No Travel_Rarely 482 Research & Development
## 508 29 No Travel_Rarely 1176 Sales
## 509 35 No Travel_Rarely 1017 Research & Development
## 510 33 No Travel_Frequently 1296 Research & Development
## 511 54 No Travel_Rarely 397 Human Resources
## 512 36 No Travel_Rarely 913 Research & Development
## 513 27 No Travel_Rarely 1115 Research & Development
## 514 20 Yes Travel_Rarely 1362 Research & Development
## 515 33 Yes Travel_Frequently 1076 Research & Development
## 516 35 No Non-Travel 727 Research & Development
## 517 23 No Travel_Rarely 885 Research & Development
## 518 25 No Travel_Rarely 810 Sales
## 519 38 No Travel_Rarely 243 Sales
## 520 29 No Travel_Frequently 806 Research & Development
## 521 48 No Travel_Rarely 817 Sales
## 522 27 No Travel_Frequently 1410 Sales
## 523 37 No Travel_Rarely 1225 Research & Development
## 524 50 No Travel_Rarely 1207 Research & Development
## 525 34 No Travel_Rarely 1442 Research & Development
## 526 24 Yes Travel_Rarely 693 Sales
## 527 39 No Travel_Rarely 408 Research & Development
## 528 32 No Travel_Rarely 929 Sales
## 529 50 Yes Travel_Frequently 562 Sales
## 530 38 No Travel_Rarely 827 Research & Development
## 531 27 No Travel_Rarely 608 Research & Development
## 532 32 No Travel_Rarely 1018 Research & Development
## 533 47 No Travel_Rarely 703 Sales
## 534 40 No Travel_Frequently 580 Sales
## 535 53 No Travel_Rarely 970 Research & Development
## 536 41 No Travel_Rarely 427 Human Resources
## 537 60 No Travel_Rarely 1179 Sales
## 538 27 No Travel_Frequently 294 Research & Development
## 539 41 No Travel_Rarely 314 Human Resources
## 540 50 No Travel_Rarely 316 Sales
## 541 28 Yes Travel_Rarely 654 Research & Development
## 542 36 No Non-Travel 427 Research & Development
## 543 38 No Travel_Rarely 168 Research & Development
## 544 44 No Non-Travel 381 Research & Development
## 545 47 No Travel_Frequently 217 Sales
## 546 30 No Travel_Rarely 501 Sales
## 547 29 No Travel_Rarely 1396 Sales
## 548 42 Yes Travel_Frequently 933 Research & Development
## 549 43 No Travel_Frequently 775 Sales
## 550 34 No Travel_Rarely 970 Research & Development
## 551 23 No Travel_Rarely 650 Research & Development
## 552 39 No Travel_Rarely 141 Human Resources
## 553 56 No Travel_Rarely 832 Research & Development
## 554 40 No Travel_Rarely 804 Research & Development
## 555 27 No Travel_Rarely 975 Research & Development
## 556 29 No Travel_Rarely 1090 Sales
## 557 53 No Travel_Rarely 346 Research & Development
## 558 35 No Non-Travel 1225 Research & Development
## 559 32 No Travel_Frequently 430 Research & Development
## 560 38 No Travel_Rarely 268 Research & Development
## 561 34 No Travel_Rarely 167 Research & Development
## 562 52 No Travel_Rarely 621 Sales
## 563 33 Yes Travel_Rarely 527 Research & Development
## 564 25 No Travel_Rarely 883 Sales
## 565 45 No Travel_Rarely 954 Sales
## 566 23 No Travel_Rarely 310 Research & Development
## 567 47 Yes Travel_Frequently 719 Sales
## 568 34 No Travel_Rarely 304 Sales
## 569 55 Yes Travel_Rarely 725 Research & Development
## 570 36 No Non-Travel 1434 Sales
## 571 52 No Non-Travel 715 Research & Development
## 572 26 No Travel_Frequently 575 Research & Development
## 573 29 No Travel_Rarely 657 Research & Development
## 574 26 Yes Travel_Rarely 1146 Sales
## 575 34 No Travel_Rarely 182 Research & Development
## 576 54 No Travel_Rarely 376 Research & Development
## 577 27 No Travel_Frequently 829 Sales
## 578 37 No Travel_Rarely 571 Research & Development
## 579 38 No Travel_Frequently 240 Research & Development
## 580 34 No Travel_Rarely 121 Research & Development
## 581 35 No Travel_Rarely 384 Sales
## 582 30 No Travel_Rarely 921 Research & Development
## 583 40 No Travel_Frequently 791 Research & Development
## 584 34 No Travel_Rarely 1111 Sales
## 585 42 No Travel_Frequently 570 Research & Development
## 586 23 Yes Travel_Rarely 1243 Research & Development
## 587 24 No Non-Travel 1092 Research & Development
## 588 52 No Travel_Rarely 1325 Research & Development
## 589 50 No Travel_Rarely 691 Research & Development
## 590 29 Yes Travel_Rarely 805 Research & Development
## 591 33 No Travel_Rarely 213 Research & Development
## 592 33 Yes Travel_Rarely 118 Sales
## 593 47 No Travel_Rarely 202 Research & Development
## 594 36 No Travel_Rarely 676 Research & Development
## 595 29 No Travel_Rarely 1252 Research & Development
## 596 58 Yes Travel_Rarely 286 Research & Development
## 597 35 No Travel_Rarely 1258 Research & Development
## 598 42 No Travel_Rarely 932 Research & Development
## 599 28 Yes Travel_Rarely 890 Research & Development
## 600 36 No Travel_Rarely 1041 Human Resources
## 601 32 No Travel_Rarely 859 Research & Development
## 602 40 No Travel_Frequently 720 Research & Development
## 603 30 No Travel_Rarely 946 Research & Development
## 604 45 No Travel_Rarely 252 Research & Development
## 605 42 No Travel_Rarely 933 Research & Development
## 606 38 No Travel_Frequently 471 Research & Development
## 607 34 No Travel_Frequently 702 Research & Development
## 608 49 Yes Travel_Rarely 1184 Sales
## 609 55 Yes Travel_Rarely 436 Sales
## 610 43 No Travel_Rarely 589 Research & Development
## 611 27 No Travel_Rarely 269 Research & Development
## 612 35 No Travel_Rarely 950 Research & Development
## 613 28 No Travel_Rarely 760 Sales
## 614 34 No Travel_Rarely 829 Human Resources
## 615 26 Yes Travel_Frequently 887 Research & Development
## 616 27 No Non-Travel 443 Research & Development
## 617 51 No Travel_Rarely 1318 Sales
## 618 44 No Travel_Rarely 625 Research & Development
## 619 25 No Travel_Rarely 180 Research & Development
## 620 33 No Travel_Rarely 586 Sales
## 621 35 No Travel_Rarely 1343 Research & Development
## 622 36 No Travel_Rarely 928 Sales
## 623 32 No Travel_Rarely 117 Sales
## 624 30 No Travel_Frequently 1012 Research & Development
## 625 53 No Travel_Rarely 661 Sales
## 626 45 No Travel_Rarely 930 Sales
## 627 32 No Travel_Rarely 638 Research & Development
## 628 52 No Travel_Frequently 890 Research & Development
## 629 37 No Travel_Rarely 342 Sales
## 630 28 No Travel_Rarely 1169 Human Resources
## 631 22 No Travel_Rarely 1230 Research & Development
## 632 44 No Travel_Rarely 986 Research & Development
## 633 42 No Travel_Frequently 1271 Research & Development
## 634 36 No Travel_Rarely 1278 Human Resources
## 635 25 No Travel_Rarely 141 Sales
## 636 35 No Travel_Rarely 607 Research & Development
## 637 35 Yes Travel_Frequently 130 Research & Development
## 638 32 No Non-Travel 300 Research & Development
## 639 25 No Travel_Rarely 583 Sales
## 640 49 No Travel_Rarely 1418 Research & Development
## 641 24 No Non-Travel 1269 Research & Development
## 642 32 No Travel_Frequently 379 Sales
## 643 38 No Travel_Rarely 395 Sales
## 644 42 No Travel_Rarely 1265 Research & Development
## 645 31 No Travel_Rarely 1222 Research & Development
## 646 29 Yes Travel_Rarely 341 Sales
## 647 53 No Travel_Rarely 868 Sales
## 648 35 No Travel_Rarely 672 Research & Development
## 649 37 No Travel_Frequently 1231 Sales
## 650 53 No Travel_Rarely 102 Research & Development
## 651 43 No Travel_Frequently 422 Research & Development
## 652 47 No Travel_Rarely 249 Sales
## 653 37 No Non-Travel 1252 Sales
## 654 50 No Non-Travel 881 Research & Development
## 655 39 No Travel_Rarely 1383 Human Resources
## 656 33 No Travel_Rarely 1075 Human Resources
## 657 32 Yes Travel_Rarely 374 Research & Development
## 658 29 No Travel_Rarely 1086 Research & Development
## 659 44 No Travel_Rarely 661 Research & Development
## 660 28 No Travel_Rarely 821 Sales
## 661 58 Yes Travel_Frequently 781 Research & Development
## 662 43 No Travel_Rarely 177 Research & Development
## 663 20 Yes Travel_Rarely 500 Sales
## 664 21 Yes Travel_Rarely 1427 Research & Development
## 665 36 No Travel_Rarely 1425 Research & Development
## 666 47 No Travel_Rarely 1454 Sales
## 667 22 Yes Travel_Rarely 617 Research & Development
## 668 41 Yes Travel_Rarely 1085 Research & Development
## 669 28 No Travel_Rarely 995 Research & Development
## 670 39 Yes Travel_Rarely 1122 Research & Development
## 671 27 No Travel_Rarely 618 Research & Development
## 672 34 No Travel_Rarely 546 Research & Development
## 673 42 No Travel_Rarely 462 Sales
## 674 33 No Travel_Rarely 1198 Research & Development
## 675 58 No Travel_Rarely 1272 Research & Development
## 676 31 No Travel_Rarely 154 Sales
## 677 35 No Travel_Rarely 1137 Research & Development
## 678 49 No Travel_Rarely 527 Research & Development
## 679 48 No Travel_Rarely 1469 Research & Development
## 680 31 No Non-Travel 1188 Sales
## 681 36 No Travel_Rarely 188 Research & Development
## 682 38 No Travel_Rarely 1333 Research & Development
## 683 32 No Non-Travel 1184 Research & Development
## 684 25 Yes Travel_Rarely 867 Sales
## 685 40 No Travel_Rarely 658 Sales
## 686 26 No Travel_Frequently 1283 Sales
## 687 41 No Travel_Rarely 263 Research & Development
## 688 36 No Travel_Rarely 938 Research & Development
## 689 19 Yes Travel_Rarely 419 Sales
## 690 20 Yes Travel_Rarely 129 Research & Development
## 691 31 No Travel_Rarely 616 Research & Development
## 692 40 No Travel_Frequently 1469 Research & Development
## 693 32 No Travel_Rarely 498 Research & Development
## 694 36 Yes Travel_Rarely 530 Sales
## 695 33 No Travel_Rarely 1069 Research & Development
## 696 37 Yes Travel_Rarely 625 Sales
## 697 45 No Non-Travel 805 Research & Development
## 698 29 No Travel_Frequently 1404 Sales
## 699 35 No Travel_Rarely 1219 Sales
## 700 52 No Travel_Rarely 1053 Research & Development
## 701 58 Yes Travel_Rarely 289 Research & Development
## 702 53 No Travel_Rarely 1376 Sales
## 703 30 No Travel_Rarely 231 Sales
## 704 38 No Non-Travel 152 Sales
## 705 35 No Travel_Rarely 882 Sales
## 706 39 No Travel_Rarely 903 Sales
## 707 40 Yes Non-Travel 1479 Sales
## 708 47 No Travel_Frequently 1379 Research & Development
## 709 36 No Non-Travel 1229 Sales
## 710 31 Yes Non-Travel 335 Research & Development
## 711 33 No Non-Travel 722 Sales
## 712 29 Yes Travel_Rarely 906 Research & Development
## 713 33 No Travel_Rarely 461 Research & Development
## 714 45 No Travel_Rarely 974 Research & Development
## 715 50 No Travel_Rarely 1126 Research & Development
## 716 33 No Travel_Frequently 827 Research & Development
## 717 41 No Travel_Frequently 840 Research & Development
## 718 27 No Travel_Rarely 1134 Research & Development
## 719 45 No Non-Travel 248 Research & Development
## 720 47 No Travel_Rarely 955 Sales
## 721 30 Yes Travel_Rarely 138 Research & Development
## 722 50 No Travel_Rarely 939 Research & Development
## 723 38 No Travel_Frequently 1391 Research & Development
## 724 46 No Travel_Rarely 566 Research & Development
## 725 24 No Travel_Rarely 1206 Research & Development
## 726 35 Yes Travel_Rarely 622 Research & Development
## 727 31 No Travel_Frequently 853 Research & Development
## 728 18 No Non-Travel 287 Research & Development
## 729 54 No Travel_Rarely 1441 Research & Development
## 730 35 No Travel_Rarely 583 Research & Development
## 731 30 No Travel_Rarely 153 Research & Development
## 732 20 Yes Travel_Rarely 1097 Research & Development
## 733 30 Yes Travel_Frequently 109 Research & Development
## 734 26 No Travel_Rarely 1066 Research & Development
## 735 22 No Travel_Rarely 217 Research & Development
## 736 48 No Travel_Rarely 277 Research & Development
## 737 48 No Travel_Rarely 1355 Research & Development
## 738 41 No Travel_Rarely 549 Research & Development
## 739 39 No Travel_Rarely 466 Research & Development
## 740 27 No Travel_Rarely 1055 Research & Development
## 741 35 No Travel_Rarely 802 Research & Development
## 742 42 No Travel_Rarely 265 Sales
## 743 50 No Travel_Rarely 804 Research & Development
## 744 59 No Travel_Rarely 715 Research & Development
## 745 37 Yes Travel_Rarely 1141 Research & Development
## 746 55 No Travel_Frequently 135 Research & Development
## 747 41 No Non-Travel 247 Research & Development
## 748 38 No Travel_Rarely 1035 Sales
## 749 26 Yes Non-Travel 265 Sales
## 750 52 Yes Travel_Rarely 266 Sales
## 751 44 No Travel_Rarely 1448 Sales
## 752 50 No Non-Travel 145 Sales
## 753 36 Yes Travel_Rarely 885 Research & Development
## 754 39 No Travel_Frequently 945 Research & Development
## 755 33 No Non-Travel 1038 Sales
## 756 45 No Travel_Rarely 1234 Sales
## 757 32 No Non-Travel 1109 Research & Development
## 758 34 No Travel_Rarely 216 Sales
## 759 59 No Travel_Rarely 1089 Sales
## 760 45 No Travel_Rarely 788 Human Resources
## 761 53 No Travel_Frequently 124 Sales
## 762 36 Yes Travel_Rarely 660 Research & Development
## 763 26 Yes Travel_Frequently 342 Research & Development
## 764 34 No Travel_Rarely 1333 Sales
## 765 28 No Travel_Rarely 1144 Sales
## 766 38 No Travel_Frequently 1186 Research & Development
## 767 50 No Travel_Rarely 1464 Research & Development
## 768 37 No Travel_Rarely 124 Research & Development
## 769 40 No Travel_Rarely 300 Sales
## 770 26 No Travel_Frequently 921 Research & Development
## 771 46 No Travel_Rarely 430 Research & Development
## 772 54 No Travel_Rarely 1082 Sales
## 773 56 No Travel_Frequently 1240 Research & Development
## 774 36 No Travel_Rarely 796 Research & Development
## 775 55 No Non-Travel 444 Research & Development
## 776 43 No Travel_Rarely 415 Sales
## 777 20 Yes Travel_Frequently 769 Sales
## 778 21 Yes Travel_Rarely 1334 Research & Development
## 779 46 No Travel_Rarely 1003 Research & Development
## 780 51 Yes Travel_Rarely 1323 Research & Development
## 781 28 Yes Non-Travel 1366 Research & Development
## 782 26 No Travel_Rarely 192 Research & Development
## 783 30 No Travel_Rarely 1176 Research & Development
## 784 41 No Travel_Rarely 509 Research & Development
## 785 38 No Travel_Rarely 330 Research & Development
## 786 40 No Travel_Rarely 1492 Research & Development
## 787 27 No Non-Travel 1277 Research & Development
## 788 55 No Travel_Frequently 1091 Research & Development
## 789 28 No Travel_Rarely 857 Research & Development
## 790 44 Yes Travel_Rarely 1376 Human Resources
## 791 33 No Travel_Rarely 654 Research & Development
## 792 35 Yes Travel_Rarely 1204 Sales
## 793 33 Yes Travel_Frequently 827 Research & Development
## 794 28 No Travel_Rarely 895 Research & Development
## 795 34 No Travel_Frequently 618 Research & Development
## 796 37 No Travel_Rarely 309 Sales
## 797 25 Yes Travel_Rarely 1219 Research & Development
## 798 26 Yes Travel_Rarely 1330 Research & Development
## 799 33 Yes Travel_Rarely 1017 Research & Development
## 800 42 No Travel_Rarely 469 Research & Development
## 801 28 Yes Travel_Frequently 1009 Research & Development
## 802 50 Yes Travel_Frequently 959 Sales
## 803 33 No Travel_Frequently 970 Sales
## 804 34 No Non-Travel 697 Research & Development
## 805 48 No Non-Travel 1262 Research & Development
## 806 45 No Non-Travel 1050 Sales
## 807 52 No Travel_Rarely 994 Research & Development
## 808 38 No Travel_Rarely 770 Sales
## 809 29 No Travel_Rarely 1107 Research & Development
## 810 28 No Travel_Rarely 950 Research & Development
## 811 46 No Travel_Rarely 406 Sales
## 812 38 No Travel_Rarely 130 Sales
## 813 43 No Travel_Frequently 1082 Research & Development
## 814 39 Yes Travel_Frequently 203 Research & Development
## 815 40 No Travel_Rarely 1308 Research & Development
## 816 21 No Travel_Rarely 984 Research & Development
## 817 39 No Non-Travel 439 Research & Development
## 818 36 No Non-Travel 217 Research & Development
## 819 31 No Travel_Frequently 793 Sales
## 820 28 No Travel_Rarely 1451 Research & Development
## 821 35 No Travel_Frequently 1182 Sales
## 822 49 No Travel_Rarely 174 Sales
## 823 34 No Travel_Frequently 1003 Research & Development
## 824 29 No Travel_Frequently 490 Research & Development
## 825 42 No Travel_Rarely 188 Research & Development
## 826 29 No Travel_Rarely 718 Research & Development
## 827 38 No Travel_Rarely 433 Human Resources
## 828 28 No Travel_Frequently 773 Research & Development
## 829 18 Yes Non-Travel 247 Research & Development
## 830 33 Yes Travel_Rarely 603 Sales
## 831 41 No Travel_Rarely 167 Research & Development
## 832 31 Yes Travel_Frequently 874 Research & Development
## 833 37 No Travel_Rarely 367 Research & Development
## 834 27 No Travel_Rarely 199 Research & Development
## 835 34 No Travel_Rarely 1400 Sales
## 836 35 No Travel_Rarely 528 Human Resources
## 837 29 Yes Travel_Rarely 408 Sales
## 838 40 No Travel_Frequently 593 Research & Development
## 839 42 Yes Travel_Frequently 481 Sales
## 840 42 No Travel_Rarely 647 Sales
## 841 35 No Travel_Rarely 982 Research & Development
## 842 24 No Travel_Rarely 477 Research & Development
## 843 28 Yes Travel_Rarely 1485 Research & Development
## 844 26 No Travel_Rarely 1384 Research & Development
## 845 30 No Travel_Rarely 852 Sales
## 846 40 No Travel_Frequently 902 Research & Development
## 847 35 No Travel_Rarely 819 Research & Development
## 848 34 No Travel_Frequently 669 Research & Development
## 849 35 No Travel_Frequently 636 Research & Development
## 850 43 Yes Travel_Rarely 1372 Sales
## 851 32 No Non-Travel 862 Sales
## 852 56 No Travel_Rarely 718 Research & Development
## 853 29 No Travel_Rarely 1401 Research & Development
## 854 19 No Travel_Rarely 645 Research & Development
## 855 45 No Travel_Rarely 1457 Research & Development
## 856 37 No Travel_Rarely 977 Research & Development
## 857 20 No Travel_Rarely 805 Research & Development
## 858 44 Yes Travel_Rarely 1097 Research & Development
## 859 53 No Travel_Rarely 1223 Research & Development
## 860 29 No Travel_Rarely 942 Research & Development
## 861 22 Yes Travel_Frequently 1256 Research & Development
## 862 46 No Travel_Rarely 1402 Sales
## 863 44 No Non-Travel 111 Research & Development
## 864 33 No Travel_Rarely 147 Human Resources
## 865 41 Yes Non-Travel 906 Research & Development
## 866 30 No Travel_Rarely 1329 Sales
## 867 40 No Travel_Frequently 1184 Sales
## 868 50 No Travel_Frequently 1421 Research & Development
## 869 28 No Travel_Rarely 1179 Research & Development
## 870 46 No Travel_Rarely 1450 Research & Development
## 871 35 No Travel_Rarely 1361 Sales
## 872 24 Yes Travel_Rarely 984 Research & Development
## 873 33 No Travel_Frequently 1146 Sales
## 874 36 No Travel_Rarely 917 Research & Development
## 875 30 No Travel_Rarely 853 Research & Development
## 876 44 No Travel_Rarely 200 Research & Development
## 877 20 No Travel_Rarely 654 Sales
## 878 46 No Travel_Rarely 150 Research & Development
## 879 42 No Non-Travel 179 Human Resources
## 880 60 No Travel_Rarely 696 Sales
## 881 32 No Travel_Frequently 116 Research & Development
## 882 32 No Travel_Frequently 1316 Research & Development
## 883 36 No Travel_Rarely 363 Research & Development
## 884 33 No Travel_Rarely 117 Research & Development
## 885 40 No Travel_Rarely 107 Sales
## 886 25 No Travel_Rarely 1356 Sales
## 887 30 No Travel_Rarely 1465 Research & Development
## 888 42 No Travel_Frequently 458 Research & Development
## 889 35 No Non-Travel 1212 Sales
## 890 27 No Travel_Rarely 1103 Research & Development
## 891 54 No Travel_Frequently 966 Research & Development
## 892 44 No Travel_Rarely 1117 Research & Development
## 893 19 Yes Non-Travel 504 Research & Development
## 894 29 No Travel_Rarely 1010 Research & Development
## 895 54 No Travel_Rarely 685 Research & Development
## 896 31 No Travel_Rarely 1332 Research & Development
## 897 31 No Travel_Rarely 1062 Research & Development
## 898 59 No Travel_Rarely 326 Sales
## 899 43 No Travel_Rarely 920 Research & Development
## 900 49 No Travel_Rarely 1098 Research & Development
## 901 36 No Travel_Frequently 469 Research & Development
## 902 48 No Travel_Rarely 969 Research & Development
## 903 27 No Travel_Rarely 1167 Research & Development
## 904 29 No Travel_Rarely 1329 Research & Development
## 905 48 No Travel_Rarely 715 Research & Development
## 906 29 No Travel_Rarely 694 Research & Development
## 907 34 No Travel_Rarely 1320 Research & Development
## 908 44 No Travel_Rarely 1099 Sales
## 909 33 No Travel_Rarely 536 Sales
## 910 19 No Travel_Rarely 265 Research & Development
## 911 23 No Travel_Rarely 373 Research & Development
## 912 25 Yes Travel_Frequently 599 Sales
## 913 26 No Travel_Rarely 583 Research & Development
## 914 45 Yes Travel_Rarely 1449 Sales
## 915 55 No Non-Travel 177 Research & Development
## 916 21 Yes Travel_Frequently 251 Research & Development
## 917 46 No Travel_Rarely 168 Sales
## 918 34 No Travel_Rarely 131 Sales
## 919 51 No Travel_Frequently 237 Sales
## 920 59 No Travel_Rarely 1429 Research & Development
## 921 34 No Travel_Frequently 135 Research & Development
## 922 28 No Travel_Frequently 791 Research & Development
## 923 44 No Travel_Rarely 1199 Research & Development
## 924 34 No Travel_Frequently 648 Human Resources
## 925 35 No Travel_Rarely 735 Research & Development
## 926 42 No Travel_Rarely 603 Research & Development
## 927 43 No Travel_Rarely 531 Sales
## 928 36 No Travel_Rarely 429 Research & Development
## 929 44 Yes Travel_Rarely 621 Research & Development
## 930 28 No Travel_Frequently 193 Research & Development
## 931 51 No Travel_Frequently 968 Research & Development
## 932 30 No Non-Travel 879 Research & Development
## 933 29 Yes Travel_Rarely 806 Research & Development
## 934 28 No Travel_Rarely 640 Research & Development
## 935 25 No Travel_Rarely 266 Research & Development
## 936 32 No Travel_Rarely 604 Sales
## 937 45 No Travel_Frequently 364 Research & Development
## 938 39 No Travel_Rarely 412 Research & Development
## 939 58 No Travel_Rarely 848 Research & Development
## 940 32 Yes Travel_Rarely 1089 Research & Development
## 941 39 Yes Travel_Rarely 360 Research & Development
## 942 30 No Travel_Rarely 1138 Research & Development
## 943 36 No Travel_Rarely 325 Research & Development
## 944 46 No Travel_Rarely 991 Human Resources
## 945 28 No Non-Travel 1476 Research & Development
## 946 50 No Travel_Rarely 1322 Research & Development
## 947 40 Yes Travel_Rarely 299 Sales
## 948 52 Yes Travel_Rarely 1030 Sales
## 949 30 No Travel_Rarely 634 Research & Development
## 950 39 No Travel_Rarely 524 Research & Development
## 951 31 No Non-Travel 587 Sales
## 952 41 No Non-Travel 256 Sales
## 953 31 Yes Travel_Frequently 1060 Sales
## 954 44 Yes Travel_Rarely 935 Research & Development
## 955 42 No Non-Travel 495 Research & Development
## 956 55 No Travel_Rarely 282 Research & Development
## 957 56 No Travel_Rarely 206 Human Resources
## 958 40 No Non-Travel 458 Research & Development
## 959 34 No Travel_Rarely 943 Research & Development
## 960 40 No Travel_Rarely 523 Research & Development
## 961 41 No Travel_Frequently 1018 Sales
## 962 35 No Travel_Frequently 482 Research & Development
## 963 51 No Travel_Rarely 770 Human Resources
## 964 38 No Travel_Rarely 1009 Sales
## 965 34 No Travel_Rarely 507 Sales
## 966 25 No Travel_Rarely 882 Research & Development
## 967 58 Yes Travel_Rarely 601 Research & Development
## 968 40 No Travel_Rarely 329 Research & Development
## 969 36 No Travel_Frequently 607 Sales
## 970 48 No Travel_Rarely 855 Research & Development
## 971 27 No Travel_Rarely 1291 Sales
## 972 51 No Travel_Rarely 1405 Research & Development
## 973 18 No Non-Travel 1124 Research & Development
## 974 35 No Travel_Rarely 817 Research & Development
## 975 27 No Travel_Frequently 793 Sales
## 976 55 Yes Travel_Rarely 267 Sales
## 977 56 No Travel_Rarely 1369 Research & Development
## 978 34 No Non-Travel 999 Research & Development
## 979 40 No Travel_Rarely 1202 Research & Development
## 980 34 No Travel_Rarely 285 Research & Development
## 981 31 Yes Travel_Frequently 703 Sales
## 982 35 Yes Travel_Frequently 662 Sales
## 983 38 No Travel_Frequently 693 Research & Development
## 984 34 No Travel_Rarely 404 Research & Development
## 985 28 No Travel_Rarely 736 Sales
## 986 31 Yes Travel_Rarely 330 Research & Development
## 987 39 No Travel_Rarely 1498 Sales
## 988 51 No Travel_Frequently 541 Sales
## 989 41 No Travel_Frequently 1200 Research & Development
## 990 37 No Travel_Rarely 1439 Research & Development
## 991 33 No Travel_Frequently 1111 Sales
## 992 32 No Travel_Rarely 499 Sales
## 993 39 No Non-Travel 1485 Research & Development
## 994 25 No Travel_Rarely 1372 Sales
## 995 52 No Travel_Frequently 322 Research & Development
## 996 43 No Travel_Rarely 930 Research & Development
## 997 27 No Travel_Rarely 205 Sales
## 998 27 Yes Travel_Rarely 135 Research & Development
## 999 26 No Travel_Rarely 683 Research & Development
## 1000 42 No Travel_Rarely 1147 Human Resources
## 1001 52 No Travel_Rarely 258 Research & Development
## 1002 37 No Travel_Rarely 1462 Research & Development
## 1003 35 No Travel_Frequently 200 Research & Development
## 1004 25 No Travel_Rarely 949 Research & Development
## 1005 26 No Travel_Rarely 652 Research & Development
## 1006 29 No Travel_Rarely 332 Human Resources
## 1007 49 Yes Travel_Frequently 1475 Research & Development
## 1008 29 Yes Travel_Frequently 337 Research & Development
## 1009 54 No Travel_Rarely 971 Research & Development
## 1010 58 No Travel_Rarely 1055 Research & Development
## 1011 55 No Travel_Rarely 1136 Research & Development
## 1012 36 No Travel_Rarely 1174 Sales
## 1013 31 Yes Travel_Frequently 667 Sales
## 1014 30 No Travel_Rarely 855 Sales
## 1015 31 No Travel_Rarely 182 Research & Development
## 1016 34 No Travel_Frequently 560 Research & Development
## 1017 31 Yes Travel_Rarely 202 Research & Development
## 1018 27 No Travel_Rarely 1377 Research & Development
## 1019 36 No Travel_Rarely 172 Research & Development
## 1020 36 No Travel_Rarely 329 Sales
## 1021 47 No Travel_Rarely 465 Research & Development
## 1022 25 Yes Travel_Rarely 383 Sales
## 1023 37 No Non-Travel 1413 Research & Development
## 1024 56 No Travel_Rarely 1255 Research & Development
## 1025 47 No Travel_Rarely 359 Research & Development
## 1026 24 No Travel_Rarely 1476 Sales
## 1027 32 No Travel_Rarely 601 Sales
## 1028 34 No Travel_Rarely 401 Research & Development
## 1029 41 No Travel_Rarely 1283 Research & Development
## 1030 40 No Non-Travel 663 Research & Development
## 1031 31 No Travel_Rarely 326 Sales
## 1032 46 Yes Travel_Rarely 377 Sales
## 1033 39 Yes Non-Travel 592 Research & Development
## 1034 31 Yes Travel_Frequently 1445 Research & Development
## 1035 45 No Travel_Rarely 1038 Research & Development
## 1036 31 No Travel_Rarely 1398 Human Resources
## 1037 31 Yes Travel_Frequently 523 Research & Development
## 1038 45 No Travel_Rarely 1448 Research & Development
## 1039 48 No Travel_Rarely 1221 Sales
## 1040 34 Yes Travel_Rarely 1107 Human Resources
## 1041 40 No Non-Travel 218 Research & Development
## 1042 28 No Travel_Rarely 866 Sales
## 1043 44 No Non-Travel 981 Research & Development
## 1044 53 No Travel_Rarely 447 Research & Development
## 1045 49 No Travel_Rarely 1495 Research & Development
## 1046 40 No Travel_Rarely 896 Research & Development
## 1047 44 No Travel_Rarely 1467 Research & Development
## 1048 33 No Travel_Frequently 430 Sales
## 1049 34 No Travel_Rarely 1326 Sales
## 1050 30 No Travel_Rarely 1358 Sales
## 1051 42 No Travel_Frequently 748 Research & Development
## 1052 44 No Travel_Frequently 383 Sales
## 1053 30 No Non-Travel 990 Research & Development
## 1054 57 No Travel_Rarely 405 Research & Development
## 1055 49 No Travel_Rarely 1490 Research & Development
## 1056 34 No Travel_Frequently 829 Research & Development
## 1057 28 Yes Travel_Frequently 1496 Sales
## 1058 29 Yes Travel_Frequently 115 Sales
## 1059 34 Yes Travel_Rarely 790 Sales
## 1060 35 No Travel_Rarely 660 Sales
## 1061 24 Yes Travel_Frequently 381 Research & Development
## 1062 24 No Non-Travel 830 Sales
## 1063 44 No Travel_Frequently 1193 Research & Development
## 1064 29 No Travel_Rarely 1246 Sales
## 1065 30 No Travel_Rarely 330 Human Resources
## 1066 55 No Travel_Rarely 1229 Research & Development
## 1067 33 No Travel_Rarely 1099 Research & Development
## 1068 47 No Travel_Rarely 571 Sales
## 1069 28 Yes Travel_Frequently 289 Research & Development
## 1070 28 No Travel_Rarely 1423 Research & Development
## 1071 28 No Travel_Frequently 467 Sales
## 1072 49 No Travel_Rarely 271 Research & Development
## 1073 29 No Travel_Frequently 410 Research & Development
## 1074 28 No Travel_Rarely 1083 Research & Development
## 1075 33 No Travel_Rarely 516 Research & Development
## 1076 32 No Travel_Rarely 495 Research & Development
## 1077 54 No Travel_Frequently 1050 Research & Development
## 1078 29 Yes Travel_Rarely 224 Research & Development
## 1079 44 No Travel_Rarely 136 Research & Development
## 1080 39 No Travel_Rarely 1089 Research & Development
## 1081 46 No Travel_Rarely 228 Sales
## 1082 35 No Travel_Rarely 1029 Research & Development
## 1083 23 No Travel_Rarely 507 Research & Development
## 1084 40 Yes Travel_Rarely 676 Research & Development
## 1085 34 No Travel_Rarely 971 Sales
## 1086 31 Yes Travel_Frequently 561 Research & Development
## 1087 50 No Travel_Frequently 333 Research & Development
## 1088 34 No Travel_Rarely 1440 Sales
## 1089 42 No Travel_Rarely 1210 Research & Development
## 1090 37 No Travel_Rarely 674 Research & Development
## 1091 29 No Travel_Rarely 441 Research & Development
## 1092 33 No Travel_Rarely 575 Research & Development
## 1093 45 No Travel_Rarely 950 Research & Development
## 1094 42 No Travel_Frequently 288 Research & Development
## 1095 40 No Travel_Rarely 1342 Sales
## 1096 33 No Travel_Rarely 589 Research & Development
## 1097 40 No Travel_Rarely 898 Human Resources
## 1098 24 No Travel_Rarely 350 Research & Development
## 1099 40 No Non-Travel 1142 Research & Development
## 1100 45 No Travel_Rarely 538 Research & Development
## 1101 35 No Travel_Rarely 1402 Sales
## 1102 32 No Travel_Rarely 824 Research & Development
## 1103 36 No Travel_Rarely 1157 Sales
## 1104 48 No Travel_Rarely 492 Sales
## 1105 29 No Travel_Rarely 598 Research & Development
## 1106 33 No Travel_Rarely 1242 Sales
## 1107 30 Yes Travel_Rarely 740 Sales
## 1108 38 No Travel_Frequently 888 Human Resources
## 1109 35 No Travel_Rarely 992 Research & Development
## 1110 30 No Travel_Rarely 1288 Sales
## 1111 35 Yes Travel_Rarely 104 Research & Development
## 1112 53 Yes Travel_Rarely 607 Research & Development
## 1113 38 Yes Travel_Rarely 903 Research & Development
## 1114 32 No Non-Travel 1200 Research & Development
## 1115 48 No Travel_Rarely 1108 Research & Development
## 1116 34 No Travel_Rarely 479 Research & Development
## 1117 55 No Travel_Rarely 685 Sales
## 1118 34 No Travel_Rarely 1351 Research & Development
## 1119 26 No Travel_Rarely 474 Research & Development
## 1120 38 No Travel_Rarely 1245 Sales
## 1121 38 No Travel_Rarely 437 Sales
## 1122 36 No Travel_Rarely 884 Sales
## 1123 29 No Travel_Rarely 1370 Research & Development
## 1124 35 No Travel_Rarely 670 Research & Development
## 1125 39 No Travel_Rarely 1462 Sales
## 1126 29 No Travel_Frequently 995 Research & Development
## 1127 50 No Travel_Rarely 264 Sales
## 1128 23 No Travel_Rarely 977 Research & Development
## 1129 36 No Travel_Frequently 1302 Research & Development
## 1130 42 No Travel_Rarely 1059 Research & Development
## 1131 35 No Travel_Rarely 750 Research & Development
## 1132 34 No Travel_Frequently 653 Research & Development
## 1133 40 No Travel_Rarely 118 Sales
## 1134 43 No Travel_Rarely 990 Research & Development
## 1135 35 No Travel_Rarely 1349 Research & Development
## 1136 46 No Travel_Rarely 563 Sales
## 1137 28 Yes Travel_Rarely 329 Research & Development
## 1138 22 No Non-Travel 457 Research & Development
## 1139 50 No Travel_Frequently 1234 Research & Development
## 1140 32 No Travel_Rarely 634 Research & Development
## 1141 44 No Travel_Rarely 1313 Research & Development
## 1142 30 No Travel_Rarely 241 Research & Development
## 1143 45 No Travel_Rarely 1015 Research & Development
## 1144 45 No Non-Travel 336 Sales
## 1145 31 No Travel_Frequently 715 Sales
## 1146 36 No Travel_Rarely 559 Research & Development
## 1147 34 No Travel_Frequently 426 Research & Development
## 1148 49 No Travel_Rarely 722 Research & Development
## 1149 39 No Travel_Rarely 1387 Research & Development
## 1150 27 No Travel_Rarely 1302 Research & Development
## 1151 35 No Travel_Rarely 819 Research & Development
## 1152 28 No Travel_Rarely 580 Research & Development
## 1153 21 No Travel_Rarely 546 Research & Development
## 1154 18 Yes Travel_Frequently 544 Sales
## 1155 47 No Travel_Rarely 1176 Human Resources
## 1156 39 No Travel_Rarely 170 Research & Development
## 1157 40 No Travel_Rarely 884 Research & Development
## 1158 35 No Non-Travel 208 Research & Development
## 1159 37 No Travel_Rarely 671 Research & Development
## 1160 39 No Travel_Frequently 711 Research & Development
## 1161 45 No Travel_Rarely 1329 Research & Development
## 1162 38 No Travel_Rarely 397 Research & Development
## 1163 35 Yes Travel_Rarely 737 Sales
## 1164 37 No Travel_Rarely 1470 Research & Development
## 1165 40 No Travel_Rarely 448 Research & Development
## 1166 44 No Travel_Frequently 602 Human Resources
## 1167 48 No Travel_Frequently 365 Research & Development
## 1168 35 Yes Travel_Rarely 763 Sales
## 1169 24 No Travel_Frequently 567 Research & Development
## 1170 27 No Travel_Rarely 486 Research & Development
## 1171 27 No Travel_Frequently 591 Research & Development
## 1172 40 Yes Travel_Rarely 1329 Research & Development
## 1173 29 No Travel_Rarely 469 Sales
## 1174 36 No Travel_Rarely 711 Research & Development
## 1175 25 No Travel_Frequently 772 Research & Development
## 1176 39 No Travel_Rarely 492 Research & Development
## 1177 49 No Travel_Rarely 301 Research & Development
## 1178 50 No Travel_Rarely 813 Research & Development
## 1179 20 No Travel_Rarely 1141 Sales
## 1180 34 No Travel_Rarely 1130 Research & Development
## 1181 36 No Travel_Rarely 311 Research & Development
## 1182 49 No Travel_Rarely 465 Research & Development
## 1183 36 No Non-Travel 894 Research & Development
## 1184 36 No Travel_Rarely 1040 Research & Development
## 1185 54 No Travel_Rarely 584 Research & Development
## 1186 43 No Travel_Rarely 1291 Research & Development
## 1187 35 Yes Travel_Frequently 880 Sales
## 1188 38 No Travel_Frequently 1189 Research & Development
## 1189 29 No Travel_Rarely 991 Sales
## 1190 33 No Travel_Rarely 392 Sales
## 1191 32 No Travel_Rarely 977 Research & Development
## 1192 31 No Travel_Rarely 1112 Sales
## 1193 49 No Travel_Rarely 464 Research & Development
## 1194 38 No Travel_Frequently 148 Research & Development
## 1195 47 No Travel_Rarely 1225 Sales
## 1196 49 No Travel_Rarely 809 Research & Development
## 1197 41 No Travel_Rarely 1206 Sales
## 1198 20 No Travel_Rarely 727 Sales
## 1199 33 No Non-Travel 530 Sales
## 1200 36 No Travel_Rarely 1351 Research & Development
## 1201 44 No Travel_Rarely 528 Human Resources
## 1202 23 Yes Travel_Rarely 1320 Research & Development
## 1203 38 No Travel_Rarely 1495 Research & Development
## 1204 53 No Travel_Rarely 1395 Research & Development
## 1205 48 Yes Travel_Frequently 708 Sales
## 1206 32 Yes Travel_Rarely 1259 Research & Development
## 1207 26 No Non-Travel 786 Research & Development
## 1208 55 No Travel_Rarely 1441 Research & Development
## 1209 34 No Travel_Rarely 1157 Research & Development
## 1210 60 No Travel_Rarely 370 Research & Development
## 1211 33 No Travel_Rarely 267 Research & Development
## 1212 37 No Travel_Frequently 1278 Sales
## 1213 34 No Travel_Rarely 678 Research & Development
## 1214 23 Yes Travel_Rarely 427 Sales
## 1215 44 No Travel_Rarely 921 Research & Development
## 1216 35 No Travel_Frequently 146 Research & Development
## 1217 43 No Travel_Rarely 1179 Sales
## 1218 24 No Travel_Rarely 581 Research & Development
## 1219 41 No Travel_Rarely 918 Sales
## 1220 29 No Travel_Rarely 1082 Research & Development
## 1221 36 No Travel_Rarely 530 Sales
## 1222 45 No Non-Travel 1238 Research & Development
## 1223 24 Yes Travel_Rarely 240 Human Resources
## 1224 47 Yes Travel_Frequently 1093 Sales
## 1225 26 No Travel_Rarely 390 Research & Development
## 1226 45 No Travel_Rarely 1005 Research & Development
## 1227 32 No Travel_Frequently 585 Research & Development
## 1228 31 No Travel_Rarely 741 Research & Development
## 1229 41 No Non-Travel 552 Human Resources
## 1230 40 No Travel_Rarely 369 Research & Development
## 1231 24 No Travel_Rarely 506 Research & Development
## 1232 46 No Travel_Rarely 717 Research & Development
## 1233 35 No Travel_Rarely 1370 Research & Development
## 1234 30 No Travel_Rarely 793 Research & Development
## 1235 47 No Non-Travel 543 Sales
## 1236 46 No Travel_Rarely 1277 Sales
## 1237 36 Yes Travel_Rarely 1456 Sales
## 1238 32 Yes Travel_Rarely 964 Sales
## 1239 23 No Travel_Rarely 160 Research & Development
## 1240 31 No Travel_Frequently 163 Research & Development
## 1241 39 No Non-Travel 792 Research & Development
## 1242 32 No Travel_Rarely 371 Sales
## 1243 40 No Travel_Rarely 611 Sales
## 1244 45 No Travel_Rarely 176 Human Resources
## 1245 30 No Travel_Frequently 1312 Research & Development
## 1246 24 No Travel_Frequently 897 Human Resources
## 1247 30 Yes Travel_Frequently 600 Human Resources
## 1248 31 No Travel_Rarely 1003 Sales
## 1249 27 No Travel_Rarely 1054 Research & Development
## 1250 29 Yes Travel_Rarely 428 Sales
## 1251 29 No Travel_Frequently 461 Research & Development
## 1252 30 No Travel_Rarely 979 Sales
## 1253 34 No Travel_Rarely 181 Research & Development
## 1254 33 No Non-Travel 1283 Sales
## 1255 49 No Travel_Rarely 1313 Sales
## 1256 33 Yes Travel_Rarely 211 Sales
## 1257 38 No Travel_Frequently 594 Research & Development
## 1258 31 Yes Travel_Rarely 1079 Sales
## 1259 29 No Travel_Rarely 590 Research & Development
## 1260 30 No Travel_Rarely 305 Research & Development
## 1261 32 No Non-Travel 953 Research & Development
## 1262 38 No Travel_Rarely 833 Research & Development
## 1263 43 Yes Travel_Frequently 807 Research & Development
## 1264 42 No Travel_Rarely 855 Research & Development
## 1265 55 No Travel_Rarely 478 Research & Development
## 1266 33 No Non-Travel 775 Research & Development
## 1267 41 No Travel_Rarely 548 Research & Development
## 1268 34 No Non-Travel 1375 Sales
## 1269 53 No Non-Travel 661 Research & Development
## 1270 43 No Travel_Rarely 244 Human Resources
## 1271 34 No Travel_Rarely 511 Sales
## 1272 21 Yes Travel_Rarely 337 Sales
## 1273 38 No Travel_Rarely 1153 Research & Development
## 1274 22 Yes Travel_Rarely 1294 Research & Development
## 1275 31 No Travel_Rarely 196 Sales
## 1276 51 No Travel_Rarely 942 Research & Development
## 1277 37 No Travel_Rarely 589 Sales
## 1278 46 No Travel_Rarely 734 Research & Development
## 1279 36 No Travel_Rarely 1383 Research & Development
## 1280 44 Yes Travel_Frequently 429 Research & Development
## 1281 37 No Travel_Rarely 1239 Human Resources
## 1282 35 Yes Travel_Rarely 303 Sales
## 1283 33 No Travel_Rarely 867 Research & Development
## 1284 28 No Travel_Rarely 1181 Research & Development
## 1285 39 No Travel_Rarely 1253 Research & Development
## 1286 46 No Non-Travel 849 Sales
## 1287 40 No Travel_Rarely 616 Research & Development
## 1288 42 No Travel_Rarely 1128 Research & Development
## 1289 35 No Non-Travel 1180 Research & Development
## 1290 38 No Non-Travel 1336 Human Resources
## 1291 34 Yes Travel_Frequently 234 Research & Development
## 1292 37 Yes Travel_Rarely 370 Research & Development
## 1293 39 No Travel_Frequently 766 Sales
## 1294 43 No Non-Travel 343 Research & Development
## 1295 41 No Travel_Rarely 447 Research & Development
## 1296 41 No Travel_Rarely 796 Sales
## 1297 30 No Travel_Rarely 1092 Research & Development
## 1298 26 Yes Travel_Rarely 920 Human Resources
## 1299 46 Yes Travel_Rarely 261 Research & Development
## 1300 40 No Travel_Rarely 1194 Research & Development
## 1301 34 No Travel_Rarely 810 Sales
## 1302 58 No Non-Travel 350 Sales
## 1303 35 No Travel_Rarely 185 Research & Development
## 1304 47 No Travel_Rarely 1001 Research & Development
## 1305 40 No Travel_Rarely 750 Research & Development
## 1306 54 No Travel_Rarely 431 Research & Development
## 1307 31 No Travel_Frequently 1125 Sales
## 1308 28 No Travel_Rarely 1217 Research & Development
## 1309 38 No Travel_Rarely 723 Sales
## 1310 26 No Travel_Rarely 572 Sales
## 1311 58 No Travel_Frequently 1216 Research & Development
## 1312 18 No Non-Travel 1431 Research & Development
## 1313 31 Yes Travel_Rarely 359 Human Resources
## 1314 29 Yes Travel_Rarely 350 Human Resources
## 1315 45 No Non-Travel 589 Sales
## 1316 36 No Travel_Rarely 430 Research & Development
## 1317 43 No Travel_Frequently 1422 Sales
## 1318 27 No Travel_Frequently 1297 Research & Development
## 1319 29 No Travel_Frequently 574 Research & Development
## 1320 32 No Travel_Frequently 1318 Sales
## 1321 42 No Non-Travel 355 Research & Development
## 1322 47 No Travel_Rarely 207 Research & Development
## 1323 46 No Travel_Rarely 706 Research & Development
## 1324 28 No Non-Travel 280 Human Resources
## 1325 29 No Travel_Rarely 726 Research & Development
## 1326 42 No Travel_Rarely 1142 Research & Development
## 1327 32 Yes Travel_Rarely 414 Sales
## 1328 46 No Travel_Rarely 1319 Sales
## 1329 27 No Travel_Rarely 728 Sales
## 1330 29 No Travel_Rarely 352 Human Resources
## 1331 43 No Travel_Rarely 823 Research & Development
## 1332 48 No Travel_Rarely 1224 Research & Development
## 1333 29 Yes Travel_Frequently 459 Research & Development
## 1334 46 Yes Travel_Rarely 1254 Sales
## 1335 27 No Travel_Frequently 1131 Research & Development
## 1336 39 No Travel_Rarely 835 Research & Development
## 1337 55 No Travel_Rarely 836 Research & Development
## 1338 28 No Travel_Rarely 1172 Sales
## 1339 30 Yes Travel_Rarely 945 Sales
## 1340 22 Yes Travel_Rarely 391 Research & Development
## 1341 36 No Travel_Rarely 1266 Sales
## 1342 31 No Travel_Rarely 311 Research & Development
## 1343 34 No Travel_Rarely 1480 Sales
## 1344 29 No Travel_Rarely 592 Research & Development
## 1345 37 No Travel_Rarely 783 Research & Development
## 1346 35 No Travel_Rarely 219 Research & Development
## 1347 45 No Travel_Rarely 556 Research & Development
## 1348 36 No Travel_Frequently 1213 Human Resources
## 1349 40 No Travel_Rarely 1137 Research & Development
## 1350 26 No Travel_Rarely 482 Research & Development
## 1351 27 No Travel_Rarely 511 Sales
## 1352 48 No Travel_Frequently 117 Research & Development
## 1353 44 No Travel_Rarely 170 Research & Development
## 1354 34 Yes Non-Travel 967 Research & Development
## 1355 56 Yes Travel_Rarely 1162 Research & Development
## 1356 36 No Travel_Rarely 335 Sales
## 1357 41 No Travel_Rarely 337 Sales
## 1358 42 No Travel_Rarely 1396 Research & Development
## 1359 31 No Travel_Rarely 1079 Sales
## 1360 34 No Travel_Rarely 735 Sales
## 1361 31 No Travel_Rarely 471 Research & Development
## 1362 26 No Travel_Frequently 1096 Research & Development
## 1363 45 No Travel_Frequently 1297 Research & Development
## 1364 33 No Travel_Rarely 217 Sales
## 1365 28 No Travel_Frequently 783 Sales
## 1366 29 Yes Travel_Frequently 746 Sales
## 1367 39 No Non-Travel 1251 Sales
## 1368 27 No Travel_Rarely 1354 Research & Development
## 1369 34 No Travel_Frequently 735 Research & Development
## 1370 28 Yes Travel_Rarely 1475 Sales
## 1371 47 No Non-Travel 1169 Research & Development
## 1372 56 No Travel_Rarely 1443 Sales
## 1373 39 No Travel_Rarely 867 Research & Development
## 1374 38 No Travel_Frequently 1394 Research & Development
## 1375 58 No Travel_Rarely 605 Sales
## 1376 32 Yes Travel_Frequently 238 Research & Development
## 1377 38 No Travel_Rarely 1206 Research & Development
## 1378 49 No Travel_Frequently 1064 Research & Development
## 1379 42 No Travel_Rarely 419 Sales
## 1380 27 Yes Travel_Frequently 1337 Human Resources
## 1381 35 No Travel_Rarely 682 Sales
## 1382 28 No Non-Travel 1103 Research & Development
## 1383 31 No Non-Travel 976 Research & Development
## 1384 36 No Non-Travel 1351 Research & Development
## 1385 34 No Travel_Rarely 937 Sales
## 1386 34 No Travel_Rarely 1239 Sales
## 1387 26 No Travel_Rarely 157 Research & Development
## 1388 29 No Travel_Rarely 136 Research & Development
## 1389 32 No Non-Travel 1146 Research & Development
## 1390 31 No Travel_Frequently 1125 Research & Development
## 1391 28 Yes Travel_Rarely 1404 Research & Development
## 1392 38 No Travel_Rarely 1404 Sales
## 1393 35 No Travel_Rarely 1224 Sales
## 1394 27 No Travel_Rarely 954 Sales
## 1395 32 No Travel_Rarely 1373 Research & Development
## 1396 31 Yes Travel_Frequently 754 Sales
## 1397 53 Yes Travel_Rarely 1168 Sales
## 1398 54 No Travel_Rarely 155 Research & Development
## 1399 33 No Travel_Frequently 1303 Research & Development
## 1400 43 No Travel_Rarely 574 Research & Development
## 1401 38 No Travel_Frequently 1444 Human Resources
## 1402 55 No Travel_Rarely 189 Human Resources
## 1403 31 No Travel_Rarely 1276 Research & Development
## 1404 39 No Travel_Rarely 119 Sales
## 1405 42 No Non-Travel 335 Research & Development
## 1406 31 No Non-Travel 697 Research & Development
## 1407 54 No Travel_Rarely 157 Research & Development
## 1408 24 No Travel_Rarely 771 Research & Development
## 1409 23 No Travel_Rarely 571 Research & Development
## 1410 40 No Travel_Frequently 692 Research & Development
## 1411 40 No Travel_Rarely 444 Sales
## 1412 25 No Travel_Rarely 309 Human Resources
## 1413 30 No Travel_Rarely 911 Research & Development
## 1414 25 No Travel_Rarely 977 Research & Development
## 1415 47 No Travel_Rarely 1180 Research & Development
## 1416 33 No Non-Travel 1313 Research & Development
## 1417 38 No Travel_Rarely 1321 Sales
## 1418 31 No Travel_Rarely 1154 Sales
## 1419 38 No Travel_Frequently 508 Research & Development
## 1420 42 No Travel_Rarely 557 Research & Development
## 1421 41 No Travel_Rarely 642 Research & Development
## 1422 47 No Non-Travel 1162 Research & Development
## 1423 35 No Travel_Rarely 1490 Research & Development
## 1424 22 No Travel_Rarely 581 Research & Development
## 1425 35 No Travel_Rarely 1395 Research & Development
## 1426 33 No Travel_Rarely 501 Research & Development
## 1427 32 No Travel_Rarely 267 Research & Development
## 1428 40 No Travel_Rarely 543 Research & Development
## 1429 32 No Travel_Rarely 234 Sales
## 1430 39 No Travel_Rarely 116 Research & Development
## 1431 38 No Travel_Rarely 201 Research & Development
## 1432 32 No Travel_Rarely 801 Sales
## 1433 37 No Travel_Rarely 161 Research & Development
## 1434 25 No Travel_Rarely 1382 Sales
## 1435 52 No Non-Travel 585 Sales
## 1436 44 No Travel_Rarely 1037 Research & Development
## 1437 21 No Travel_Rarely 501 Sales
## 1438 39 No Non-Travel 105 Research & Development
## 1439 23 Yes Travel_Frequently 638 Sales
## 1440 36 No Travel_Rarely 557 Sales
## 1441 36 No Travel_Frequently 688 Research & Development
## 1442 56 No Non-Travel 667 Research & Development
## 1443 29 Yes Travel_Rarely 1092 Research & Development
## 1444 42 No Travel_Rarely 300 Research & Development
## 1445 56 Yes Travel_Rarely 310 Research & Development
## 1446 41 No Travel_Rarely 582 Research & Development
## 1447 34 No Travel_Rarely 704 Sales
## 1448 36 No Non-Travel 301 Sales
## 1449 41 No Travel_Rarely 930 Sales
## 1450 32 No Travel_Rarely 529 Research & Development
## 1451 35 No Travel_Rarely 1146 Human Resources
## 1452 38 No Travel_Rarely 345 Sales
## 1453 50 Yes Travel_Frequently 878 Sales
## 1454 36 No Travel_Rarely 1120 Sales
## 1455 45 No Travel_Rarely 374 Sales
## 1456 40 No Travel_Rarely 1322 Research & Development
## 1457 35 No Travel_Frequently 1199 Research & Development
## 1458 40 No Travel_Rarely 1194 Research & Development
## 1459 35 No Travel_Rarely 287 Research & Development
## 1460 29 No Travel_Rarely 1378 Research & Development
## 1461 29 No Travel_Rarely 468 Research & Development
## 1462 50 Yes Travel_Rarely 410 Sales
## 1463 39 No Travel_Rarely 722 Sales
## 1464 31 No Non-Travel 325 Research & Development
## 1465 26 No Travel_Rarely 1167 Sales
## 1466 36 No Travel_Frequently 884 Research & Development
## 1467 39 No Travel_Rarely 613 Research & Development
## 1468 27 No Travel_Rarely 155 Research & Development
## 1469 49 No Travel_Frequently 1023 Sales
## 1470 34 No Travel_Rarely 628 Research & Development
## DistanceFromHome Education EducationField EmployeeCount EmployeeNumber
## 1 1 2 Life Sciences 1 1
## 2 8 1 Life Sciences 1 2
## 3 2 2 Other 1 4
## 4 3 4 Life Sciences 1 5
## 5 2 1 Medical 1 7
## 6 2 2 Life Sciences 1 8
## 7 3 3 Medical 1 10
## 8 24 1 Life Sciences 1 11
## 9 23 3 Life Sciences 1 12
## 10 27 3 Medical 1 13
## 11 16 3 Medical 1 14
## 12 15 2 Life Sciences 1 15
## 13 26 1 Life Sciences 1 16
## 14 19 2 Medical 1 18
## 15 24 3 Life Sciences 1 19
## 16 21 4 Life Sciences 1 20
## 17 5 2 Life Sciences 1 21
## 18 16 2 Medical 1 22
## 19 2 4 Life Sciences 1 23
## 20 2 3 Life Sciences 1 24
## 21 11 2 Other 1 26
## 22 9 4 Life Sciences 1 27
## 23 7 4 Life Sciences 1 28
## 24 15 2 Life Sciences 1 30
## 25 6 1 Medical 1 31
## 26 5 3 Other 1 32
## 27 16 1 Life Sciences 1 33
## 28 8 4 Marketing 1 35
## 29 7 4 Medical 1 36
## 30 2 4 Marketing 1 38
## 31 2 3 Medical 1 39
## 32 10 4 Other 1 40
## 33 9 2 Medical 1 41
## 34 5 3 Technical Degree 1 42
## 35 1 3 Medical 1 45
## 36 2 2 Medical 1 46
## 37 3 2 Marketing 1 47
## 38 2 3 Marketing 1 49
## 39 5 4 Life Sciences 1 51
## 40 1 3 Life Sciences 1 52
## 41 4 2 Other 1 53
## 42 2 4 Life Sciences 1 54
## 43 25 3 Life Sciences 1 55
## 44 8 3 Life Sciences 1 56
## 45 1 2 Medical 1 57
## 46 12 3 Technical Degree 1 58
## 47 23 4 Marketing 1 60
## 48 19 2 Life Sciences 1 61
## 49 5 4 Marketing 1 62
## 50 8 1 Life Sciences 1 63
## 51 1 2 Life Sciences 1 64
## 52 5 4 Technical Degree 1 65
## 53 1 5 Marketing 1 68
## 54 11 2 Medical 1 70
## 55 23 3 Marketing 1 72
## 56 1 2 Life Sciences 1 73
## 57 18 5 Life Sciences 1 74
## 58 23 4 Medical 1 75
## 59 7 4 Life Sciences 1 76
## 60 1 4 Life Sciences 1 77
## 61 1 3 Medical 1 78
## 62 29 5 Life Sciences 1 79
## 63 7 2 Medical 1 80
## 64 25 3 Life Sciences 1 81
## 65 8 3 Technical Degree 1 83
## 66 8 3 Medical 1 84
## 67 11 3 Life Sciences 1 85
## 68 7 3 Life Sciences 1 86
## 69 1 3 Medical 1 88
## 70 9 3 Medical 1 90
## 71 1 1 Life Sciences 1 91
## 72 2 3 Life Sciences 1 94
## 73 1 4 Medical 1 95
## 74 1 3 Life Sciences 1 96
## 75 6 3 Life Sciences 1 97
## 76 8 4 Life Sciences 1 98
## 77 1 4 Marketing 1 100
## 78 6 4 Other 1 101
## 79 7 4 Medical 1 102
## 80 5 2 Medical 1 103
## 81 1 1 Life Sciences 1 104
## 82 1 3 Medical 1 105
## 83 1 2 Life Sciences 1 106
## 84 6 3 Medical 1 107
## 85 1 2 Medical 1 110
## 86 7 3 Life Sciences 1 112
## 87 2 1 Technical Degree 1 113
## 88 9 4 Life Sciences 1 116
## 89 2 3 Life Sciences 1 117
## 90 9 2 Medical 1 118
## 91 1 4 Life Sciences 1 119
## 92 21 4 Marketing 1 120
## 93 4 2 Medical 1 121
## 94 1 3 Medical 1 124
## 95 6 4 Medical 1 125
## 96 2 4 Technical Degree 1 126
## 97 3 2 Other 1 128
## 98 4 3 Medical 1 129
## 99 10 4 Medical 1 131
## 100 23 3 Medical 1 132
## 101 6 4 Human Resources 1 133
## 102 1 1 Life Sciences 1 134
## 103 6 3 Life Sciences 1 137
## 104 6 4 Other 1 138
## 105 2 2 Life Sciences 1 139
## 106 2 4 Human Resources 1 140
## 107 1 3 Life Sciences 1 141
## 108 5 3 Marketing 1 142
## 109 7 1 Medical 1 143
## 110 15 3 Medical 1 144
## 111 1 4 Medical 1 145
## 112 7 3 Life Sciences 1 147
## 113 26 3 Human Resources 1 148
## 114 18 1 Life Sciences 1 150
## 115 6 4 Life Sciences 1 151
## 116 3 3 Life Sciences 1 152
## 117 5 3 Medical 1 153
## 118 11 2 Technical Degree 1 154
## 119 3 2 Life Sciences 1 155
## 120 26 2 Life Sciences 1 158
## 121 23 3 Life Sciences 1 159
## 122 22 2 Marketing 1 160
## 123 14 4 Life Sciences 1 161
## 124 6 3 Life Sciences 1 162
## 125 6 4 Life Sciences 1 163
## 126 6 3 Other 1 164
## 127 23 4 Medical 1 165
## 128 22 1 Marketing 1 167
## 129 2 1 Technical Degree 1 169
## 130 20 4 Medical 1 170
## 131 28 3 Medical 1 171
## 132 12 3 Marketing 1 174
## 133 20 3 Life Sciences 1 175
## 134 9 1 Life Sciences 1 176
## 135 25 1 Life Sciences 1 177
## 136 6 2 Medical 1 178
## 137 8 4 Life Sciences 1 179
## 138 4 4 Life Sciences 1 182
## 139 28 3 Life Sciences 1 183
## 140 9 3 Human Resources 1 184
## 141 9 3 Medical 1 190
## 142 29 3 Medical 1 192
## 143 3 5 Technical Degree 1 193
## 144 18 3 Life Sciences 1 194
## 145 9 2 Medical 1 195
## 146 5 3 Technical Degree 1 197
## 147 2 1 Medical 1 198
## 148 10 3 Life Sciences 1 199
## 149 9 4 Life Sciences 1 200
## 150 3 1 Medical 1 201
## 151 26 3 Medical 1 202
## 152 1 5 Marketing 1 204
## 153 6 2 Marketing 1 205
## 154 9 3 Life Sciences 1 206
## 155 8 3 Marketing 1 207
## 156 1 1 Technical Degree 1 208
## 157 7 4 Medical 1 211
## 158 9 3 Medical 1 214
## 159 4 4 Marketing 1 215
## 160 2 4 Marketing 1 216
## 161 19 1 Medical 1 217
## 162 9 3 Medical 1 218
## 163 21 3 Medical 1 221
## 164 24 2 Life Sciences 1 223
## 165 3 3 Medical 1 224
## 166 11 3 Life Sciences 1 226
## 167 14 3 Life Sciences 1 227
## 168 5 3 Life Sciences 1 228
## 169 1 4 Life Sciences 1 230
## 170 6 5 Life Sciences 1 231
## 171 17 3 Technical Degree 1 233
## 172 1 1 Technical Degree 1 235
## 173 3 2 Medical 1 238
## 174 9 3 Medical 1 239
## 175 4 2 Life Sciences 1 240
## 176 8 3 Life Sciences 1 241
## 177 2 3 Life Sciences 1 242
## 178 2 3 Life Sciences 1 243
## 179 1 2 Marketing 1 244
## 180 9 2 Life Sciences 1 245
## 181 12 1 Medical 1 246
## 182 27 2 Medical 1 247
## 183 20 2 Marketing 1 248
## 184 1 3 Medical 1 249
## 185 13 2 Medical 1 250
## 186 14 3 Medical 1 252
## 187 4 1 Medical 1 253
## 188 14 4 Medical 1 254
## 189 2 1 Life Sciences 1 256
## 190 3 3 Medical 1 258
## 191 1 4 Life Sciences 1 259
## 192 9 3 Medical 1 260
## 193 23 2 Life Sciences 1 261
## 194 7 3 Medical 1 262
## 195 2 2 Medical 1 264
## 196 21 3 Life Sciences 1 267
## 197 2 3 Medical 1 269
## 198 21 2 Medical 1 270
## 199 2 4 Life Sciences 1 271
## 200 29 3 Technical Degree 1 273
## 201 1 1 Technical Degree 1 274
## 202 18 4 Life Sciences 1 275
## 203 10 4 Medical 1 277
## 204 19 2 Medical 1 281
## 205 29 1 Medical 1 282
## 206 27 3 Marketing 1 283
## 207 5 3 Life Sciences 1 284
## 208 18 1 Medical 1 286
## 209 9 5 Life Sciences 1 287
## 210 1 4 Medical 1 288
## 211 4 4 Medical 1 291
## 212 1 1 Life Sciences 1 292
## 213 20 3 Life Sciences 1 293
## 214 8 4 Life Sciences 1 296
## 215 3 3 Technical Degree 1 297
## 216 6 3 Life Sciences 1 298
## 217 26 4 Marketing 1 299
## 218 1 3 Technical Degree 1 300
## 219 6 3 Medical 1 302
## 220 3 3 Marketing 1 303
## 221 5 2 Life Sciences 1 304
## 222 4 4 Medical 1 305
## 223 11 3 Other 1 306
## 224 3 3 Life Sciences 1 307
## 225 1 4 Medical 1 308
## 226 3 3 Life Sciences 1 309
## 227 4 4 Marketing 1 311
## 228 1 1 Medical 1 312
## 229 1 3 Marketing 1 314
## 230 18 1 Medical 1 315
## 231 2 3 Life Sciences 1 316
## 232 4 2 Technical Degree 1 319
## 233 6 2 Medical 1 321
## 234 1 4 Medical 1 323
## 235 14 3 Medical 1 325
## 236 16 3 Marketing 1 327
## 237 2 2 Life Sciences 1 328
## 238 2 4 Life Sciences 1 329
## 239 4 2 Life Sciences 1 330
## 240 1 3 Life Sciences 1 331
## 241 1 4 Medical 1 332
## 242 26 4 Marketing 1 333
## 243 19 2 Life Sciences 1 334
## 244 24 2 Technical Degree 1 335
## 245 1 3 Other 1 336
## 246 3 4 Medical 1 337
## 247 5 4 Life Sciences 1 338
## 248 2 4 Life Sciences 1 339
## 249 1 2 Medical 1 340
## 250 7 4 Life Sciences 1 341
## 251 10 3 Medical 1 342
## 252 2 4 Technical Degree 1 343
## 253 15 3 Life Sciences 1 346
## 254 17 2 Life Sciences 1 347
## 255 20 2 Marketing 1 349
## 256 1 3 Life Sciences 1 350
## 257 2 3 Medical 1 351
## 258 2 2 Medical 1 352
## 259 1 3 Life Sciences 1 353
## 260 29 2 Medical 1 355
## 261 7 3 Life Sciences 1 359
## 262 2 2 Life Sciences 1 361
## 263 2 1 Technical Degree 1 362
## 264 2 3 Technical Degree 1 363
## 265 2 4 Life Sciences 1 364
## 266 2 3 Medical 1 366
## 267 23 3 Medical 1 367
## 268 5 2 Life Sciences 1 369
## 269 20 2 Medical 1 372
## 270 6 3 Life Sciences 1 373
## 271 1 3 Medical 1 374
## 272 29 4 Life Sciences 1 376
## 273 9 3 Medical 1 377
## 274 6 4 Medical 1 378
## 275 3 2 Medical 1 379
## 276 1 4 Medical 1 380
## 277 22 3 Life Sciences 1 381
## 278 7 2 Medical 1 382
## 279 1 3 Life Sciences 1 384
## 280 4 1 Life Sciences 1 385
## 281 3 4 Medical 1 386
## 282 1 1 Life Sciences 1 387
## 283 2 2 Life Sciences 1 388
## 284 20 2 Technical Degree 1 389
## 285 11 2 Medical 1 390
## 286 1 3 Life Sciences 1 391
## 287 24 3 Life Sciences 1 392
## 288 23 4 Life Sciences 1 393
## 289 16 4 Medical 1 394
## 290 8 2 Life Sciences 1 395
## 291 10 4 Life Sciences 1 396
## 292 3 3 Technical Degree 1 397
## 293 5 3 Marketing 1 399
## 294 4 4 Marketing 1 401
## 295 9 3 Medical 1 403
## 296 26 3 Marketing 1 404
## 297 3 3 Life Sciences 1 405
## 298 16 3 Marketing 1 406
## 299 18 4 Life Sciences 1 407
## 300 2 3 Medical 1 408
## 301 2 4 Life Sciences 1 410
## 302 10 3 Medical 1 411
## 303 16 2 Medical 1 412
## 304 7 3 Technical Degree 1 416
## 305 1 3 Medical 1 417
## 306 24 4 Life Sciences 1 419
## 307 7 3 Life Sciences 1 420
## 308 25 2 Life Sciences 1 421
## 309 1 4 Life Sciences 1 422
## 310 5 4 Technical Degree 1 423
## 311 2 3 Human Resources 1 424
## 312 7 3 Life Sciences 1 425
## 313 2 4 Life Sciences 1 426
## 314 5 4 Life Sciences 1 428
## 315 10 1 Medical 1 429
## 316 10 4 Life Sciences 1 430
## 317 1 2 Technical Degree 1 431
## 318 8 4 Medical 1 433
## 319 5 3 Life Sciences 1 434
## 320 8 2 Technical Degree 1 436
## 321 2 3 Life Sciences 1 437
## 322 7 3 Marketing 1 438
## 323 2 4 Medical 1 439
## 324 2 4 Medical 1 440
## 325 28 2 Medical 1 441
## 326 7 2 Life Sciences 1 442
## 327 7 2 Medical 1 444
## 328 3 2 Medical 1 445
## 329 10 3 Marketing 1 446
## 330 5 5 Life Sciences 1 447
## 331 10 4 Life Sciences 1 448
## 332 1 1 Marketing 1 449
## 333 20 4 Life Sciences 1 450
## 334 7 3 Life Sciences 1 451
## 335 8 4 Other 1 452
## 336 1 2 Medical 1 453
## 337 8 4 Other 1 454
## 338 9 5 Other 1 455
## 339 5 3 Marketing 1 456
## 340 8 4 Marketing 1 458
## 341 5 2 Medical 1 460
## 342 15 2 Life Sciences 1 461
## 343 7 4 Medical 1 462
## 344 10 1 Marketing 1 463
## 345 5 4 Technical Degree 1 464
## 346 26 1 Life Sciences 1 465
## 347 6 3 Medical 1 466
## 348 4 1 Medical 1 467
## 349 23 5 Life Sciences 1 468
## 350 2 3 Life Sciences 1 469
## 351 2 1 Technical Degree 1 470
## 352 2 3 Medical 1 471
## 353 29 1 Medical 1 473
## 354 6 3 Medical 1 474
## 355 25 2 Technical Degree 1 475
## 356 1 3 Life Sciences 1 476
## 357 2 4 Other 1 477
## 358 1 1 Technical Degree 1 478
## 359 1 5 Medical 1 479
## 360 3 4 Medical 1 481
## 361 1 4 Medical 1 482
## 362 10 4 Life Sciences 1 483
## 363 9 2 Medical 1 484
## 364 5 3 Marketing 1 485
## 365 10 3 Medical 1 486
## 366 7 4 Medical 1 487
## 367 4 3 Marketing 1 488
## 368 10 3 Technical Degree 1 491
## 369 22 2 Marketing 1 492
## 370 9 4 Life Sciences 1 493
## 371 12 3 Life Sciences 1 494
## 372 23 3 Life Sciences 1 495
## 373 9 4 Life Sciences 1 496
## 374 1 2 Medical 1 497
## 375 9 4 Life Sciences 1 498
## 376 7 3 Other 1 499
## 377 14 2 Life Sciences 1 500
## 378 2 3 Life Sciences 1 501
## 379 19 3 Marketing 1 502
## 380 2 3 Life Sciences 1 505
## 381 10 4 Marketing 1 507
## 382 2 1 Technical Degree 1 508
## 383 3 1 Technical Degree 1 510
## 384 11 3 Medical 1 511
## 385 2 2 Medical 1 513
## 386 4 3 Technical Degree 1 514
## 387 14 3 Life Sciences 1 515
## 388 2 2 Marketing 1 516
## 389 1 4 Life Sciences 1 517
## 390 10 4 Life Sciences 1 518
## 391 12 3 Life Sciences 1 520
## 392 2 3 Medical 1 521
## 393 5 2 Medical 1 522
## 394 4 4 Marketing 1 523
## 395 7 2 Medical 1 524
## 396 21 3 Medical 1 525
## 397 8 4 Other 1 526
## 398 4 2 Life Sciences 1 527
## 399 25 5 Medical 1 529
## 400 1 2 Life Sciences 1 530
## 401 1 1 Life Sciences 1 531
## 402 6 3 Life Sciences 1 532
## 403 12 3 Technical Degree 1 533
## 404 1 3 Marketing 1 534
## 405 17 2 Medical 1 536
## 406 3 3 Medical 1 538
## 407 3 3 Medical 1 543
## 408 10 2 Life Sciences 1 544
## 409 4 2 Life Sciences 1 546
## 410 29 2 Life Sciences 1 547
## 411 2 3 Life Sciences 1 548
## 412 7 3 Life Sciences 1 549
## 413 18 3 Medical 1 550
## 414 28 4 Technical Degree 1 551
## 415 1 1 Technical Degree 1 554
## 416 6 2 Marketing 1 555
## 417 2 2 Life Sciences 1 556
## 418 2 4 Life Sciences 1 558
## 419 23 3 Life Sciences 1 560
## 420 3 3 Life Sciences 1 562
## 421 3 4 Medical 1 564
## 422 25 5 Technical Degree 1 565
## 423 2 2 Technical Degree 1 566
## 424 22 4 Other 1 567
## 425 29 3 Marketing 1 568
## 426 29 4 Life Sciences 1 569
## 427 2 3 Medical 1 571
## 428 28 3 Marketing 1 573
## 429 2 2 Medical 1 574
## 430 2 3 Life Sciences 1 575
## 431 22 3 Life Sciences 1 577
## 432 8 4 Life Sciences 1 578
## 433 2 4 Life Sciences 1 579
## 434 10 3 Marketing 1 580
## 435 9 1 Life Sciences 1 581
## 436 15 1 Medical 1 582
## 437 10 1 Medical 1 584
## 438 7 1 Marketing 1 585
## 439 16 3 Life Sciences 1 586
## 440 20 3 Life Sciences 1 587
## 441 23 3 Human Resources 1 590
## 442 5 2 Other 1 591
## 443 10 4 Medical 1 592
## 444 4 1 Technical Degree 1 593
## 445 2 5 Marketing 1 595
## 446 18 5 Life Sciences 1 597
## 447 10 2 Life Sciences 1 599
## 448 1 3 Marketing 1 600
## 449 6 3 Life Sciences 1 601
## 450 8 1 Life Sciences 1 602
## 451 2 1 Life Sciences 1 604
## 452 24 3 Medical 1 605
## 453 2 3 Other 1 606
## 454 17 4 Life Sciences 1 608
## 455 19 3 Technical Degree 1 611
## 456 1 5 Medical 1 612
## 457 7 3 Life Sciences 1 613
## 458 5 3 Marketing 1 614
## 459 28 3 Other 1 615
## 460 2 4 Other 1 616
## 461 29 2 Medical 1 618
## 462 1 3 Medical 1 620
## 463 21 4 Life Sciences 1 621
## 464 24 3 Technical Degree 1 622
## 465 1 3 Technical Degree 1 623
## 466 18 1 Medical 1 624
## 467 2 5 Life Sciences 1 625
## 468 9 4 Medical 1 626
## 469 6 2 Technical Degree 1 630
## 470 11 4 Other 1 631
## 471 24 3 Medical 1 632
## 472 10 3 Medical 1 634
## 473 1 4 Life Sciences 1 635
## 474 18 4 Life Sciences 1 638
## 475 23 3 Medical 1 639
## 476 28 2 Marketing 1 641
## 477 17 2 Other 1 643
## 478 3 3 Medical 1 644
## 479 13 1 Medical 1 645
## 480 7 3 Life Sciences 1 647
## 481 12 4 Life Sciences 1 648
## 482 1 2 Life Sciences 1 649
## 483 13 4 Medical 1 650
## 484 25 2 Other 1 652
## 485 6 4 Medical 1 653
## 486 6 4 Medical 1 655
## 487 2 3 Marketing 1 656
## 488 1 3 Life Sciences 1 657
## 489 2 4 Life Sciences 1 659
## 490 6 4 Other 1 661
## 491 1 1 Life Sciences 1 662
## 492 9 5 Medical 1 663
## 493 1 4 Life Sciences 1 664
## 494 1 4 Life Sciences 1 665
## 495 14 3 Technical Degree 1 666
## 496 2 1 Marketing 1 667
## 497 22 1 Technical Degree 1 669
## 498 3 4 Other 1 671
## 499 6 1 Medical 1 675
## 500 8 4 Marketing 1 677
## 501 9 4 Life Sciences 1 679
## 502 3 3 Medical 1 680
## 503 1 1 Medical 1 682
## 504 1 5 Life Sciences 1 683
## 505 26 4 Life Sciences 1 684
## 506 6 3 Life Sciences 1 686
## 507 3 3 Other 1 689
## 508 3 2 Medical 1 690
## 509 6 4 Life Sciences 1 691
## 510 6 3 Life Sciences 1 692
## 511 19 4 Medical 1 698
## 512 9 2 Medical 1 699
## 513 3 4 Medical 1 700
## 514 10 1 Medical 1 701
## 515 3 3 Life Sciences 1 702
## 516 3 3 Life Sciences 1 704
## 517 4 3 Medical 1 705
## 518 8 3 Life Sciences 1 707
## 519 7 4 Marketing 1 709
## 520 1 4 Life Sciences 1 710
## 521 2 1 Marketing 1 712
## 522 3 1 Medical 1 714
## 523 10 2 Life Sciences 1 715
## 524 28 1 Medical 1 716
## 525 9 3 Medical 1 717
## 526 3 2 Life Sciences 1 720
## 527 2 4 Technical Degree 1 721
## 528 10 3 Marketing 1 722
## 529 8 2 Technical Degree 1 723
## 530 1 4 Life Sciences 1 724
## 531 1 2 Life Sciences 1 725
## 532 3 2 Life Sciences 1 727
## 533 14 4 Marketing 1 728
## 534 5 4 Life Sciences 1 729
## 535 7 3 Life Sciences 1 730
## 536 10 4 Human Resources 1 731
## 537 16 4 Marketing 1 732
## 538 10 2 Life Sciences 1 733
## 539 1 3 Human Resources 1 734
## 540 8 4 Marketing 1 738
## 541 1 2 Life Sciences 1 741
## 542 8 3 Life Sciences 1 742
## 543 1 3 Life Sciences 1 743
## 544 24 3 Medical 1 744
## 545 3 3 Medical 1 746
## 546 27 5 Marketing 1 747
## 547 10 3 Life Sciences 1 749
## 548 19 3 Medical 1 752
## 549 15 3 Life Sciences 1 754
## 550 8 2 Medical 1 757
## 551 9 1 Medical 1 758
## 552 3 3 Human Resources 1 760
## 553 9 3 Medical 1 762
## 554 2 1 Medical 1 763
## 555 7 3 Medical 1 764
## 556 10 3 Marketing 1 766
## 557 6 3 Life Sciences 1 769
## 558 2 4 Life Sciences 1 771
## 559 24 4 Life Sciences 1 772
## 560 2 5 Medical 1 773
## 561 8 5 Life Sciences 1 775
## 562 3 4 Marketing 1 776
## 563 1 4 Other 1 780
## 564 26 1 Medical 1 781
## 565 2 2 Technical Degree 1 783
## 566 10 1 Medical 1 784
## 567 27 2 Life Sciences 1 785
## 568 2 3 Other 1 786
## 569 2 3 Medical 1 787
## 570 8 4 Life Sciences 1 789
## 571 19 4 Medical 1 791
## 572 1 2 Life Sciences 1 792
## 573 27 3 Medical 1 793
## 574 8 3 Technical Degree 1 796
## 575 1 4 Life Sciences 1 797
## 576 19 4 Medical 1 799
## 577 8 1 Marketing 1 800
## 578 10 1 Life Sciences 1 802
## 579 2 4 Life Sciences 1 803
## 580 2 4 Medical 1 804
## 581 8 4 Life Sciences 1 805
## 582 1 3 Life Sciences 1 806
## 583 2 2 Medical 1 807
## 584 8 2 Life Sciences 1 808
## 585 8 3 Life Sciences 1 809
## 586 6 3 Life Sciences 1 811
## 587 9 3 Life Sciences 1 812
## 588 11 4 Life Sciences 1 813
## 589 2 3 Medical 1 815
## 590 1 2 Life Sciences 1 816
## 591 7 3 Medical 1 817
## 592 16 3 Marketing 1 819
## 593 2 2 Other 1 820
## 594 1 3 Other 1 823
## 595 23 2 Life Sciences 1 824
## 596 2 4 Life Sciences 1 825
## 597 1 4 Life Sciences 1 826
## 598 1 2 Life Sciences 1 827
## 599 2 4 Medical 1 828
## 600 13 3 Human Resources 1 829
## 601 4 3 Life Sciences 1 830
## 602 16 4 Medical 1 832
## 603 2 3 Medical 1 833
## 604 2 3 Life Sciences 1 834
## 605 29 3 Life Sciences 1 836
## 606 12 3 Life Sciences 1 837
## 607 16 4 Life Sciences 1 838
## 608 11 3 Marketing 1 840
## 609 2 1 Medical 1 842
## 610 14 2 Life Sciences 1 843
## 611 5 1 Technical Degree 1 844
## 612 7 3 Other 1 845
## 613 2 4 Marketing 1 846
## 614 3 2 Human Resources 1 847
## 615 5 2 Medical 1 848
## 616 3 3 Medical 1 850
## 617 26 4 Marketing 1 851
## 618 4 3 Medical 1 852
## 619 2 1 Medical 1 854
## 620 1 3 Medical 1 855
## 621 27 1 Medical 1 856
## 622 1 2 Life Sciences 1 857
## 623 13 4 Life Sciences 1 859
## 624 5 4 Life Sciences 1 861
## 625 7 2 Marketing 1 862
## 626 9 3 Marketing 1 864
## 627 8 2 Medical 1 865
## 628 25 4 Medical 1 867
## 629 16 4 Marketing 1 868
## 630 8 2 Medical 1 869
## 631 1 2 Life Sciences 1 872
## 632 8 4 Life Sciences 1 874
## 633 2 1 Medical 1 875
## 634 8 3 Life Sciences 1 878
## 635 3 1 Other 1 879
## 636 9 3 Life Sciences 1 880
## 637 25 4 Life Sciences 1 881
## 638 1 3 Life Sciences 1 882
## 639 4 1 Marketing 1 885
## 640 1 3 Technical Degree 1 887
## 641 4 1 Life Sciences 1 888
## 642 5 2 Life Sciences 1 889
## 643 9 3 Marketing 1 893
## 644 3 3 Life Sciences 1 894
## 645 11 4 Life Sciences 1 895
## 646 1 3 Medical 1 896
## 647 8 3 Marketing 1 897
## 648 25 3 Technical Degree 1 899
## 649 21 2 Medical 1 900
## 650 23 4 Life Sciences 1 901
## 651 1 3 Life Sciences 1 902
## 652 2 2 Marketing 1 903
## 653 19 2 Medical 1 904
## 654 2 4 Life Sciences 1 905
## 655 2 3 Life Sciences 1 909
## 656 3 2 Human Resources 1 910
## 657 25 4 Life Sciences 1 911
## 658 7 1 Medical 1 912
## 659 9 2 Life Sciences 1 913
## 660 5 4 Medical 1 916
## 661 2 1 Life Sciences 1 918
## 662 8 3 Life Sciences 1 920
## 663 2 3 Medical 1 922
## 664 18 1 Other 1 923
## 665 14 1 Life Sciences 1 924
## 666 2 4 Life Sciences 1 925
## 667 3 1 Life Sciences 1 926
## 668 2 4 Life Sciences 1 927
## 669 9 3 Medical 1 930
## 670 6 3 Medical 1 932
## 671 4 3 Life Sciences 1 933
## 672 10 3 Life Sciences 1 934
## 673 14 2 Medical 1 936
## 674 1 4 Other 1 939
## 675 5 3 Technical Degree 1 940
## 676 7 4 Life Sciences 1 941
## 677 21 1 Life Sciences 1 942
## 678 8 2 Other 1 944
## 679 20 4 Medical 1 945
## 680 20 2 Marketing 1 947
## 681 7 4 Other 1 949
## 682 1 3 Technical Degree 1 950
## 683 1 3 Life Sciences 1 951
## 684 19 2 Marketing 1 952
## 685 10 4 Marketing 1 954
## 686 1 3 Medical 1 956
## 687 6 3 Medical 1 957
## 688 2 4 Medical 1 958
## 689 21 3 Other 1 959
## 690 4 3 Technical Degree 1 960
## 691 12 3 Medical 1 961
## 692 9 4 Medical 1 964
## 693 3 4 Medical 1 966
## 694 3 1 Life Sciences 1 967
## 695 1 3 Life Sciences 1 969
## 696 1 4 Life Sciences 1 970
## 697 4 2 Life Sciences 1 972
## 698 20 3 Technical Degree 1 974
## 699 18 3 Medical 1 975
## 700 1 2 Life Sciences 1 976
## 701 2 3 Technical Degree 1 977
## 702 2 2 Medical 1 981
## 703 8 2 Other 1 982
## 704 10 3 Technical Degree 1 983
## 705 3 4 Life Sciences 1 984
## 706 2 5 Life Sciences 1 985
## 707 24 3 Life Sciences 1 986
## 708 16 4 Medical 1 987
## 709 8 4 Technical Degree 1 990
## 710 9 2 Medical 1 991
## 711 17 3 Life Sciences 1 992
## 712 10 3 Life Sciences 1 994
## 713 13 1 Life Sciences 1 995
## 714 1 4 Medical 1 996
## 715 1 2 Medical 1 997
## 716 1 4 Other 1 998
## 717 9 3 Medical 1 999
## 718 16 4 Technical Degree 1 1001
## 719 23 2 Life Sciences 1 1002
## 720 4 2 Life Sciences 1 1003
## 721 22 3 Life Sciences 1 1004
## 722 24 3 Life Sciences 1 1005
## 723 10 1 Medical 1 1006
## 724 7 2 Medical 1 1007
## 725 17 1 Medical 1 1009
## 726 14 4 Other 1 1010
## 727 1 1 Life Sciences 1 1011
## 728 5 2 Life Sciences 1 1012
## 729 17 3 Technical Degree 1 1013
## 730 25 4 Medical 1 1014
## 731 8 2 Life Sciences 1 1015
## 732 11 3 Medical 1 1016
## 733 5 3 Medical 1 1017
## 734 2 2 Medical 1 1018
## 735 8 1 Life Sciences 1 1019
## 736 6 3 Life Sciences 1 1022
## 737 4 4 Life Sciences 1 1024
## 738 7 2 Medical 1 1025
## 739 1 1 Life Sciences 1 1026
## 740 2 4 Life Sciences 1 1027
## 741 10 3 Other 1 1028
## 742 5 2 Marketing 1 1029
## 743 9 3 Life Sciences 1 1030
## 744 2 3 Life Sciences 1 1032
## 745 11 2 Medical 1 1033
## 746 18 4 Medical 1 1034
## 747 7 1 Life Sciences 1 1035
## 748 3 4 Life Sciences 1 1036
## 749 29 2 Medical 1 1037
## 750 2 1 Marketing 1 1038
## 751 28 3 Medical 1 1039
## 752 1 3 Life Sciences 1 1040
## 753 16 4 Life Sciences 1 1042
## 754 22 3 Medical 1 1043
## 755 8 1 Life Sciences 1 1044
## 756 11 2 Life Sciences 1 1045
## 757 29 4 Medical 1 1046
## 758 1 4 Marketing 1 1047
## 759 1 2 Technical Degree 1 1048
## 760 24 4 Medical 1 1049
## 761 2 3 Marketing 1 1050
## 762 15 3 Other 1 1052
## 763 2 3 Life Sciences 1 1053
## 764 10 4 Life Sciences 1 1055
## 765 10 1 Medical 1 1056
## 766 3 4 Other 1 1060
## 767 2 4 Medical 1 1061
## 768 3 3 Other 1 1062
## 769 26 3 Marketing 1 1066
## 770 1 1 Medical 1 1068
## 771 1 4 Medical 1 1069
## 772 2 4 Life Sciences 1 1070
## 773 9 3 Medical 1 1071
## 774 12 5 Medical 1 1073
## 775 2 1 Medical 1 1074
## 776 25 3 Medical 1 1076
## 777 9 3 Marketing 1 1077
## 778 10 3 Life Sciences 1 1079
## 779 8 4 Life Sciences 1 1080
## 780 4 4 Life Sciences 1 1081
## 781 24 2 Technical Degree 1 1082
## 782 1 2 Medical 1 1083
## 783 20 3 Other 1 1084
## 784 7 2 Technical Degree 1 1085
## 785 17 1 Life Sciences 1 1088
## 786 20 4 Technical Degree 1 1092
## 787 8 5 Life Sciences 1 1094
## 788 2 1 Life Sciences 1 1096
## 789 10 3 Other 1 1097
## 790 1 2 Medical 1 1098
## 791 5 3 Life Sciences 1 1099
## 792 4 3 Technical Degree 1 1100
## 793 29 4 Medical 1 1101
## 794 15 2 Life Sciences 1 1102
## 795 3 1 Life Sciences 1 1103
## 796 10 4 Life Sciences 1 1105
## 797 4 1 Technical Degree 1 1106
## 798 21 3 Medical 1 1107
## 799 25 3 Medical 1 1108
## 800 2 2 Medical 1 1109
## 801 1 3 Medical 1 1111
## 802 1 4 Other 1 1113
## 803 7 3 Life Sciences 1 1114
## 804 3 4 Life Sciences 1 1115
## 805 1 4 Medical 1 1116
## 806 9 4 Life Sciences 1 1117
## 807 7 4 Life Sciences 1 1118
## 808 10 4 Marketing 1 1119
## 809 28 4 Life Sciences 1 1120
## 810 3 3 Medical 1 1121
## 811 3 1 Marketing 1 1124
## 812 2 2 Marketing 1 1125
## 813 27 3 Life Sciences 1 1126
## 814 2 3 Life Sciences 1 1127
## 815 14 3 Medical 1 1128
## 816 1 1 Technical Degree 1 1131
## 817 9 3 Life Sciences 1 1132
## 818 18 4 Life Sciences 1 1133
## 819 20 3 Life Sciences 1 1135
## 820 2 1 Life Sciences 1 1136
## 821 11 2 Marketing 1 1137
## 822 8 4 Technical Degree 1 1138
## 823 2 2 Life Sciences 1 1140
## 824 10 3 Life Sciences 1 1143
## 825 29 3 Medical 1 1148
## 826 8 1 Medical 1 1150
## 827 1 3 Human Resources 1 1152
## 828 6 3 Life Sciences 1 1154
## 829 8 1 Medical 1 1156
## 830 9 4 Marketing 1 1157
## 831 12 4 Life Sciences 1 1158
## 832 15 3 Medical 1 1160
## 833 25 2 Medical 1 1161
## 834 6 3 Life Sciences 1 1162
## 835 9 1 Life Sciences 1 1163
## 836 8 4 Technical Degree 1 1164
## 837 23 1 Life Sciences 1 1165
## 838 9 4 Medical 1 1166
## 839 12 3 Life Sciences 1 1167
## 840 4 4 Marketing 1 1171
## 841 1 4 Medical 1 1172
## 842 24 3 Medical 1 1173
## 843 12 1 Life Sciences 1 1175
## 844 3 4 Medical 1 1177
## 845 10 3 Marketing 1 1179
## 846 26 2 Medical 1 1180
## 847 2 3 Life Sciences 1 1182
## 848 1 3 Medical 1 1184
## 849 4 4 Other 1 1185
## 850 9 3 Marketing 1 1188
## 851 2 1 Life Sciences 1 1190
## 852 4 4 Technical Degree 1 1191
## 853 6 1 Medical 1 1192
## 854 9 2 Life Sciences 1 1193
## 855 7 3 Medical 1 1195
## 856 1 3 Life Sciences 1 1196
## 857 3 3 Life Sciences 1 1198
## 858 10 4 Life Sciences 1 1200
## 859 7 2 Medical 1 1201
## 860 15 1 Life Sciences 1 1202
## 861 3 4 Life Sciences 1 1203
## 862 2 3 Marketing 1 1204
## 863 17 3 Life Sciences 1 1206
## 864 2 3 Human Resources 1 1207
## 865 5 2 Life Sciences 1 1210
## 866 29 4 Life Sciences 1 1211
## 867 2 4 Medical 1 1212
## 868 2 3 Medical 1 1215
## 869 19 4 Medical 1 1216
## 870 15 2 Life Sciences 1 1217
## 871 17 4 Life Sciences 1 1218
## 872 17 2 Life Sciences 1 1219
## 873 25 3 Medical 1 1220
## 874 6 4 Life Sciences 1 1221
## 875 7 4 Life Sciences 1 1224
## 876 29 4 Other 1 1225
## 877 21 3 Marketing 1 1226
## 878 2 4 Technical Degree 1 1228
## 879 2 5 Medical 1 1231
## 880 7 4 Marketing 1 1233
## 881 13 3 Other 1 1234
## 882 2 2 Life Sciences 1 1235
## 883 1 3 Technical Degree 1 1237
## 884 9 3 Medical 1 1238
## 885 10 3 Technical Degree 1 1239
## 886 10 4 Life Sciences 1 1240
## 887 1 3 Medical 1 1241
## 888 26 5 Medical 1 1242
## 889 8 2 Marketing 1 1243
## 890 14 3 Life Sciences 1 1244
## 891 1 4 Life Sciences 1 1245
## 892 2 1 Life Sciences 1 1246
## 893 10 3 Medical 1 1248
## 894 1 3 Life Sciences 1 1249
## 895 3 3 Life Sciences 1 1250
## 896 11 2 Medical 1 1251
## 897 24 3 Medical 1 1252
## 898 3 3 Life Sciences 1 1254
## 899 3 3 Life Sciences 1 1255
## 900 4 2 Medical 1 1256
## 901 3 3 Technical Degree 1 1257
## 902 2 2 Technical Degree 1 1258
## 903 4 2 Life Sciences 1 1259
## 904 7 3 Life Sciences 1 1260
## 905 1 3 Life Sciences 1 1263
## 906 1 3 Life Sciences 1 1264
## 907 20 3 Technical Degree 1 1265
## 908 5 3 Marketing 1 1267
## 909 10 5 Marketing 1 1268
## 910 25 3 Life Sciences 1 1269
## 911 1 2 Life Sciences 1 1270
## 912 24 1 Life Sciences 1 1273
## 913 4 2 Life Sciences 1 1275
## 914 2 3 Marketing 1 1277
## 915 8 1 Medical 1 1278
## 916 10 2 Life Sciences 1 1279
## 917 4 2 Marketing 1 1280
## 918 2 3 Marketing 1 1281
## 919 9 3 Life Sciences 1 1282
## 920 18 4 Medical 1 1283
## 921 19 3 Medical 1 1285
## 922 1 4 Medical 1 1286
## 923 4 2 Life Sciences 1 1288
## 924 11 3 Life Sciences 1 1289
## 925 6 1 Life Sciences 1 1291
## 926 7 4 Medical 1 1292
## 927 4 4 Marketing 1 1293
## 928 2 4 Life Sciences 1 1294
## 929 15 3 Medical 1 1295
## 930 2 3 Life Sciences 1 1296
## 931 6 2 Medical 1 1297
## 932 9 2 Medical 1 1298
## 933 7 3 Technical Degree 1 1299
## 934 1 3 Technical Degree 1 1301
## 935 1 3 Medical 1 1303
## 936 8 3 Medical 1 1304
## 937 25 3 Medical 1 1306
## 938 13 4 Medical 1 1307
## 939 23 4 Life Sciences 1 1308
## 940 7 2 Life Sciences 1 1309
## 941 23 3 Medical 1 1310
## 942 6 3 Technical Degree 1 1311
## 943 10 4 Technical Degree 1 1312
## 944 1 2 Life Sciences 1 1314
## 945 1 3 Life Sciences 1 1315
## 946 28 3 Life Sciences 1 1317
## 947 25 4 Marketing 1 1318
## 948 5 3 Life Sciences 1 1319
## 949 17 4 Medical 1 1321
## 950 18 2 Life Sciences 1 1322
## 951 2 4 Life Sciences 1 1324
## 952 10 2 Medical 1 1329
## 953 1 3 Life Sciences 1 1331
## 954 3 3 Life Sciences 1 1333
## 955 2 1 Life Sciences 1 1334
## 956 2 2 Medical 1 1336
## 957 8 4 Life Sciences 1 1338
## 958 16 2 Life Sciences 1 1340
## 959 9 3 Life Sciences 1 1344
## 960 2 3 Life Sciences 1 1346
## 961 1 3 Marketing 1 1349
## 962 4 4 Life Sciences 1 1350
## 963 5 3 Life Sciences 1 1352
## 964 2 2 Life Sciences 1 1355
## 965 15 2 Medical 1 1356
## 966 19 1 Medical 1 1358
## 967 7 4 Medical 1 1360
## 968 1 4 Life Sciences 1 1361
## 969 7 3 Marketing 1 1362
## 970 4 3 Life Sciences 1 1363
## 971 11 3 Medical 1 1364
## 972 11 2 Technical Degree 1 1367
## 973 1 3 Life Sciences 1 1368
## 974 1 3 Medical 1 1369
## 975 2 1 Life Sciences 1 1371
## 976 13 4 Marketing 1 1372
## 977 23 3 Life Sciences 1 1373
## 978 26 1 Technical Degree 1 1374
## 979 2 1 Medical 1 1375
## 980 29 3 Medical 1 1377
## 981 2 3 Life Sciences 1 1379
## 982 18 4 Marketing 1 1380
## 983 7 3 Life Sciences 1 1382
## 984 2 4 Technical Degree 1 1383
## 985 26 3 Life Sciences 1 1387
## 986 22 4 Medical 1 1389
## 987 21 4 Life Sciences 1 1390
## 988 2 3 Marketing 1 1391
## 989 22 3 Life Sciences 1 1392
## 990 4 1 Life Sciences 1 1394
## 991 5 1 Life Sciences 1 1395
## 992 2 1 Marketing 1 1396
## 993 25 2 Life Sciences 1 1397
## 994 18 1 Life Sciences 1 1399
## 995 28 2 Medical 1 1401
## 996 6 3 Medical 1 1402
## 997 10 3 Marketing 1 1403
## 998 17 4 Life Sciences 1 1405
## 999 2 1 Medical 1 1407
## 1000 10 3 Human Resources 1 1408
## 1001 8 4 Other 1 1409
## 1002 11 3 Medical 1 1411
## 1003 18 2 Life Sciences 1 1412
## 1004 1 3 Technical Degree 1 1415
## 1005 7 3 Other 1 1417
## 1006 17 3 Other 1 1419
## 1007 28 2 Life Sciences 1 1420
## 1008 14 1 Other 1 1421
## 1009 1 3 Medical 1 1422
## 1010 1 3 Medical 1 1423
## 1011 1 4 Medical 1 1424
## 1012 3 4 Marketing 1 1425
## 1013 1 4 Life Sciences 1 1427
## 1014 7 4 Marketing 1 1428
## 1015 8 5 Life Sciences 1 1430
## 1016 1 4 Other 1 1431
## 1017 8 3 Life Sciences 1 1433
## 1018 11 1 Life Sciences 1 1434
## 1019 4 4 Life Sciences 1 1435
## 1020 16 4 Marketing 1 1436
## 1021 1 3 Technical Degree 1 1438
## 1022 9 2 Life Sciences 1 1439
## 1023 5 2 Technical Degree 1 1440
## 1024 1 2 Life Sciences 1 1441
## 1025 2 4 Medical 1 1443
## 1026 4 1 Medical 1 1445
## 1027 7 5 Marketing 1 1446
## 1028 1 3 Life Sciences 1 1447
## 1029 5 5 Medical 1 1448
## 1030 9 4 Other 1 1449
## 1031 8 2 Life Sciences 1 1453
## 1032 9 3 Marketing 1 1457
## 1033 2 3 Life Sciences 1 1458
## 1034 1 5 Life Sciences 1 1459
## 1035 20 3 Medical 1 1460
## 1036 8 2 Medical 1 1461
## 1037 2 3 Life Sciences 1 1464
## 1038 29 3 Technical Degree 1 1465
## 1039 7 3 Marketing 1 1466
## 1040 9 4 Technical Degree 1 1467
## 1041 8 1 Medical 1 1468
## 1042 5 3 Medical 1 1469
## 1043 5 3 Life Sciences 1 1471
## 1044 2 3 Medical 1 1472
## 1045 5 4 Technical Degree 1 1473
## 1046 2 3 Medical 1 1474
## 1047 20 3 Life Sciences 1 1475
## 1048 7 3 Medical 1 1477
## 1049 3 3 Other 1 1478
## 1050 16 1 Life Sciences 1 1479
## 1051 9 2 Medical 1 1480
## 1052 1 5 Marketing 1 1481
## 1053 7 3 Technical Degree 1 1482
## 1054 1 2 Life Sciences 1 1483
## 1055 7 4 Life Sciences 1 1484
## 1056 15 3 Medical 1 1485
## 1057 1 3 Technical Degree 1 1486
## 1058 13 3 Technical Degree 1 1487
## 1059 24 4 Medical 1 1489
## 1060 7 1 Life Sciences 1 1492
## 1061 9 3 Medical 1 1494
## 1062 13 2 Life Sciences 1 1495
## 1063 2 1 Medical 1 1496
## 1064 19 3 Life Sciences 1 1497
## 1065 1 3 Life Sciences 1 1499
## 1066 4 4 Life Sciences 1 1501
## 1067 4 4 Medical 1 1502
## 1068 14 3 Medical 1 1503
## 1069 2 2 Medical 1 1504
## 1070 1 3 Life Sciences 1 1506
## 1071 7 3 Life Sciences 1 1507
## 1072 3 2 Medical 1 1509
## 1073 2 1 Life Sciences 1 1513
## 1074 29 1 Life Sciences 1 1514
## 1075 8 5 Life Sciences 1 1515
## 1076 10 3 Medical 1 1516
## 1077 11 4 Medical 1 1520
## 1078 1 4 Technical Degree 1 1522
## 1079 28 3 Life Sciences 1 1523
## 1080 6 3 Life Sciences 1 1525
## 1081 3 3 Life Sciences 1 1527
## 1082 16 3 Life Sciences 1 1529
## 1083 20 1 Life Sciences 1 1533
## 1084 9 4 Life Sciences 1 1534
## 1085 1 3 Technical Degree 1 1535
## 1086 3 3 Life Sciences 1 1537
## 1087 22 5 Medical 1 1539
## 1088 7 2 Technical Degree 1 1541
## 1089 2 3 Medical 1 1542
## 1090 13 3 Medical 1 1543
## 1091 8 1 Other 1 1544
## 1092 25 3 Life Sciences 1 1545
## 1093 28 3 Technical Degree 1 1546
## 1094 2 3 Life Sciences 1 1547
## 1095 9 2 Medical 1 1548
## 1096 28 4 Life Sciences 1 1549
## 1097 6 2 Medical 1 1550
## 1098 21 2 Technical Degree 1 1551
## 1099 8 2 Life Sciences 1 1552
## 1100 1 4 Technical Degree 1 1553
## 1101 28 4 Life Sciences 1 1554
## 1102 5 2 Life Sciences 1 1555
## 1103 2 4 Life Sciences 1 1556
## 1104 16 4 Life Sciences 1 1557
## 1105 9 3 Life Sciences 1 1558
## 1106 8 4 Life Sciences 1 1560
## 1107 1 3 Life Sciences 1 1562
## 1108 10 4 Human Resources 1 1563
## 1109 1 3 Medical 1 1564
## 1110 29 4 Technical Degree 1 1568
## 1111 2 3 Life Sciences 1 1569
## 1112 2 5 Technical Degree 1 1572
## 1113 2 3 Medical 1 1573
## 1114 1 4 Technical Degree 1 1574
## 1115 15 4 Other 1 1576
## 1116 7 4 Medical 1 1577
## 1117 26 5 Marketing 1 1578
## 1118 1 4 Life Sciences 1 1580
## 1119 3 3 Life Sciences 1 1581
## 1120 14 3 Life Sciences 1 1582
## 1121 16 3 Life Sciences 1 1583
## 1122 1 4 Life Sciences 1 1585
## 1123 3 1 Medical 1 1586
## 1124 10 4 Medical 1 1587
## 1125 6 3 Medical 1 1588
## 1126 2 1 Life Sciences 1 1590
## 1127 9 3 Marketing 1 1591
## 1128 10 3 Technical Degree 1 1592
## 1129 6 4 Life Sciences 1 1594
## 1130 9 2 Other 1 1595
## 1131 28 3 Life Sciences 1 1596
## 1132 10 4 Technical Degree 1 1597
## 1133 14 2 Life Sciences 1 1598
## 1134 27 3 Technical Degree 1 1599
## 1135 7 2 Life Sciences 1 1601
## 1136 1 4 Life Sciences 1 1602
## 1137 24 3 Medical 1 1604
## 1138 26 2 Other 1 1605
## 1139 20 5 Medical 1 1606
## 1140 5 4 Other 1 1607
## 1141 7 3 Medical 1 1608
## 1142 7 3 Medical 1 1609
## 1143 5 5 Medical 1 1611
## 1144 26 3 Marketing 1 1612
## 1145 2 4 Other 1 1613
## 1146 12 4 Life Sciences 1 1614
## 1147 10 4 Life Sciences 1 1615
## 1148 25 4 Life Sciences 1 1617
## 1149 10 5 Medical 1 1618
## 1150 19 3 Other 1 1619
## 1151 18 5 Life Sciences 1 1621
## 1152 27 3 Medical 1 1622
## 1153 5 1 Medical 1 1623
## 1154 3 2 Medical 1 1624
## 1155 26 4 Life Sciences 1 1625
## 1156 3 2 Medical 1 1627
## 1157 15 3 Life Sciences 1 1628
## 1158 8 4 Life Sciences 1 1630
## 1159 19 3 Life Sciences 1 1631
## 1160 4 3 Medical 1 1633
## 1161 2 2 Other 1 1635
## 1162 2 2 Medical 1 1638
## 1163 10 3 Medical 1 1639
## 1164 10 3 Medical 1 1640
## 1165 16 3 Life Sciences 1 1641
## 1166 1 5 Human Resources 1 1642
## 1167 4 5 Medical 1 1644
## 1168 15 2 Medical 1 1645
## 1169 2 1 Technical Degree 1 1646
## 1170 8 3 Medical 1 1647
## 1171 2 3 Medical 1 1648
## 1172 7 3 Life Sciences 1 1649
## 1173 10 3 Medical 1 1650
## 1174 5 4 Life Sciences 1 1651
## 1175 2 1 Life Sciences 1 1653
## 1176 12 3 Medical 1 1654
## 1177 22 4 Other 1 1655
## 1178 17 5 Life Sciences 1 1656
## 1179 2 3 Medical 1 1657
## 1180 3 3 Life Sciences 1 1658
## 1181 7 3 Life Sciences 1 1659
## 1182 6 1 Life Sciences 1 1661
## 1183 1 4 Medical 1 1662
## 1184 3 2 Life Sciences 1 1664
## 1185 22 5 Medical 1 1665
## 1186 15 2 Life Sciences 1 1666
## 1187 12 4 Other 1 1667
## 1188 1 3 Life Sciences 1 1668
## 1189 5 3 Medical 1 1669
## 1190 2 4 Medical 1 1670
## 1191 2 3 Medical 1 1671
## 1192 5 4 Life Sciences 1 1673
## 1193 16 3 Medical 1 1674
## 1194 2 3 Medical 1 1675
## 1195 2 4 Life Sciences 1 1676
## 1196 1 3 Life Sciences 1 1677
## 1197 23 2 Life Sciences 1 1678
## 1198 9 1 Life Sciences 1 1680
## 1199 16 3 Life Sciences 1 1681
## 1200 26 4 Life Sciences 1 1682
## 1201 1 3 Life Sciences 1 1683
## 1202 8 1 Medical 1 1684
## 1203 4 2 Medical 1 1687
## 1204 24 4 Medical 1 1689
## 1205 7 2 Medical 1 1691
## 1206 2 4 Life Sciences 1 1692
## 1207 7 3 Medical 1 1693
## 1208 22 3 Technical Degree 1 1694
## 1209 5 2 Medical 1 1696
## 1210 1 4 Medical 1 1697
## 1211 21 3 Medical 1 1698
## 1212 1 4 Medical 1 1700
## 1213 19 3 Life Sciences 1 1701
## 1214 7 3 Life Sciences 1 1702
## 1215 2 3 Life Sciences 1 1703
## 1216 2 4 Medical 1 1704
## 1217 2 3 Medical 1 1706
## 1218 9 3 Medical 1 1707
## 1219 6 3 Marketing 1 1708
## 1220 9 4 Medical 1 1709
## 1221 2 4 Life Sciences 1 1710
## 1222 1 1 Life Sciences 1 1712
## 1223 22 1 Human Resources 1 1714
## 1224 9 3 Life Sciences 1 1716
## 1225 17 4 Medical 1 1718
## 1226 28 2 Technical Degree 1 1719
## 1227 10 3 Life Sciences 1 1720
## 1228 2 4 Life Sciences 1 1721
## 1229 4 3 Human Resources 1 1722
## 1230 8 2 Life Sciences 1 1724
## 1231 29 1 Medical 1 1725
## 1232 13 4 Life Sciences 1 1727
## 1233 27 4 Life Sciences 1 1728
## 1234 16 1 Life Sciences 1 1729
## 1235 2 4 Marketing 1 1731
## 1236 2 3 Life Sciences 1 1732
## 1237 13 5 Marketing 1 1733
## 1238 1 2 Life Sciences 1 1734
## 1239 4 1 Medical 1 1735
## 1240 24 1 Technical Degree 1 1736
## 1241 1 3 Life Sciences 1 1737
## 1242 19 3 Life Sciences 1 1739
## 1243 7 4 Medical 1 1740
## 1244 4 3 Life Sciences 1 1744
## 1245 2 4 Technical Degree 1 1745
## 1246 10 3 Medical 1 1746
## 1247 8 3 Human Resources 1 1747
## 1248 5 3 Technical Degree 1 1749
## 1249 8 3 Medical 1 1751
## 1250 9 3 Marketing 1 1752
## 1251 1 3 Life Sciences 1 1753
## 1252 15 2 Marketing 1 1754
## 1253 2 4 Medical 1 1755
## 1254 2 3 Marketing 1 1756
## 1255 11 4 Marketing 1 1757
## 1256 16 3 Life Sciences 1 1758
## 1257 2 2 Medical 1 1760
## 1258 16 4 Marketing 1 1761
## 1259 4 3 Technical Degree 1 1762
## 1260 16 3 Life Sciences 1 1763
## 1261 5 4 Technical Degree 1 1764
## 1262 18 3 Medical 1 1766
## 1263 17 3 Technical Degree 1 1767
## 1264 12 3 Medical 1 1768
## 1265 2 3 Medical 1 1770
## 1266 4 3 Technical Degree 1 1771
## 1267 9 4 Life Sciences 1 1772
## 1268 10 3 Life Sciences 1 1774
## 1269 1 4 Medical 1 1775
## 1270 2 3 Life Sciences 1 1778
## 1271 3 2 Life Sciences 1 1779
## 1272 7 1 Marketing 1 1780
## 1273 6 2 Other 1 1782
## 1274 8 1 Medical 1 1783
## 1275 29 4 Marketing 1 1784
## 1276 3 3 Technical Degree 1 1786
## 1277 9 2 Marketing 1 1787
## 1278 2 4 Medical 1 1789
## 1279 10 3 Life Sciences 1 1790
## 1280 1 2 Medical 1 1792
## 1281 8 2 Other 1 1794
## 1282 27 3 Life Sciences 1 1797
## 1283 8 4 Life Sciences 1 1798
## 1284 1 3 Life Sciences 1 1799
## 1285 10 1 Medical 1 1800
## 1286 26 2 Life Sciences 1 1801
## 1287 2 2 Life Sciences 1 1802
## 1288 13 3 Medical 1 1803
## 1289 2 2 Medical 1 1804
## 1290 2 3 Human Resources 1 1805
## 1291 9 4 Life Sciences 1 1807
## 1292 10 4 Medical 1 1809
## 1293 20 3 Life Sciences 1 1812
## 1294 9 3 Life Sciences 1 1813
## 1295 5 3 Life Sciences 1 1814
## 1296 4 1 Marketing 1 1815
## 1297 10 3 Medical 1 1816
## 1298 20 2 Medical 1 1818
## 1299 21 2 Medical 1 1821
## 1300 1 3 Life Sciences 1 1822
## 1301 8 2 Technical Degree 1 1823
## 1302 2 3 Medical 1 1824
## 1303 23 4 Medical 1 1826
## 1304 4 3 Life Sciences 1 1827
## 1305 12 3 Life Sciences 1 1829
## 1306 7 4 Medical 1 1830
## 1307 7 4 Marketing 1 1833
## 1308 1 3 Medical 1 1834
## 1309 2 4 Marketing 1 1835
## 1310 10 3 Medical 1 1836
## 1311 15 4 Life Sciences 1 1837
## 1312 14 3 Medical 1 1839
## 1313 18 5 Human Resources 1 1842
## 1314 13 3 Human Resources 1 1844
## 1315 2 4 Life Sciences 1 1845
## 1316 2 4 Other 1 1847
## 1317 2 4 Life Sciences 1 1849
## 1318 5 2 Life Sciences 1 1850
## 1319 20 1 Medical 1 1852
## 1320 10 4 Marketing 1 1853
## 1321 10 4 Technical Degree 1 1854
## 1322 9 4 Life Sciences 1 1856
## 1323 2 2 Life Sciences 1 1857
## 1324 1 2 Life Sciences 1 1858
## 1325 29 1 Life Sciences 1 1859
## 1326 8 3 Life Sciences 1 1860
## 1327 2 4 Marketing 1 1862
## 1328 3 3 Technical Degree 1 1863
## 1329 23 1 Medical 1 1864
## 1330 6 1 Medical 1 1865
## 1331 6 3 Medical 1 1866
## 1332 10 3 Life Sciences 1 1867
## 1333 24 2 Life Sciences 1 1868
## 1334 10 3 Life Sciences 1 1869
## 1335 15 3 Life Sciences 1 1870
## 1336 19 4 Other 1 1871
## 1337 2 4 Technical Degree 1 1873
## 1338 3 3 Medical 1 1875
## 1339 9 3 Medical 1 1876
## 1340 7 1 Life Sciences 1 1878
## 1341 10 4 Technical Degree 1 1880
## 1342 20 3 Life Sciences 1 1881
## 1343 4 3 Life Sciences 1 1882
## 1344 7 3 Life Sciences 1 1883
## 1345 7 4 Medical 1 1885
## 1346 16 2 Other 1 1886
## 1347 25 2 Life Sciences 1 1888
## 1348 2 1 Human Resources 1 1890
## 1349 1 4 Life Sciences 1 1892
## 1350 1 2 Life Sciences 1 1893
## 1351 2 2 Medical 1 1898
## 1352 22 3 Medical 1 1900
## 1353 1 4 Life Sciences 1 1903
## 1354 16 4 Technical Degree 1 1905
## 1355 24 2 Life Sciences 1 1907
## 1356 17 2 Marketing 1 1908
## 1357 8 3 Marketing 1 1909
## 1358 6 3 Medical 1 1911
## 1359 10 2 Medical 1 1912
## 1360 3 1 Medical 1 1915
## 1361 4 3 Medical 1 1916
## 1362 6 3 Other 1 1918
## 1363 1 4 Medical 1 1922
## 1364 10 4 Marketing 1 1924
## 1365 1 2 Life Sciences 1 1927
## 1366 24 3 Technical Degree 1 1928
## 1367 21 4 Life Sciences 1 1929
## 1368 2 4 Technical Degree 1 1931
## 1369 22 4 Other 1 1932
## 1370 13 2 Marketing 1 1933
## 1371 14 4 Technical Degree 1 1934
## 1372 11 5 Marketing 1 1935
## 1373 9 2 Medical 1 1936
## 1374 8 3 Medical 1 1937
## 1375 21 3 Life Sciences 1 1938
## 1376 5 2 Life Sciences 1 1939
## 1377 9 2 Life Sciences 1 1940
## 1378 2 1 Life Sciences 1 1941
## 1379 12 4 Marketing 1 1943
## 1380 22 3 Human Resources 1 1944
## 1381 18 4 Medical 1 1945
## 1382 16 3 Medical 1 1947
## 1383 3 2 Medical 1 1948
## 1384 9 4 Life Sciences 1 1949
## 1385 1 3 Marketing 1 1950
## 1386 13 4 Medical 1 1951
## 1387 1 3 Medical 1 1952
## 1388 1 3 Life Sciences 1 1954
## 1389 15 4 Medical 1 1955
## 1390 1 3 Life Sciences 1 1956
## 1391 17 3 Technical Degree 1 1960
## 1392 1 3 Life Sciences 1 1961
## 1393 7 4 Life Sciences 1 1962
## 1394 9 3 Marketing 1 1965
## 1395 5 4 Life Sciences 1 1966
## 1396 26 4 Marketing 1 1967
## 1397 24 4 Life Sciences 1 1968
## 1398 9 2 Life Sciences 1 1969
## 1399 7 2 Life Sciences 1 1970
## 1400 11 3 Life Sciences 1 1971
## 1401 1 4 Other 1 1972
## 1402 26 4 Human Resources 1 1973
## 1403 2 1 Medical 1 1974
## 1404 15 4 Marketing 1 1975
## 1405 23 2 Life Sciences 1 1976
## 1406 10 3 Medical 1 1979
## 1407 10 3 Medical 1 1980
## 1408 1 2 Life Sciences 1 1981
## 1409 12 2 Other 1 1982
## 1410 11 3 Technical Degree 1 1985
## 1411 2 2 Marketing 1 1986
## 1412 2 3 Human Resources 1 1987
## 1413 1 2 Medical 1 1989
## 1414 2 1 Other 1 1992
## 1415 25 3 Medical 1 1993
## 1416 1 2 Medical 1 1994
## 1417 1 4 Life Sciences 1 1995
## 1418 2 2 Life Sciences 1 1996
## 1419 6 4 Life Sciences 1 1997
## 1420 18 4 Life Sciences 1 1998
## 1421 1 3 Life Sciences 1 1999
## 1422 1 1 Medical 1 2000
## 1423 11 4 Medical 1 2003
## 1424 1 2 Life Sciences 1 2007
## 1425 9 4 Medical 1 2008
## 1426 15 2 Medical 1 2009
## 1427 29 4 Life Sciences 1 2010
## 1428 1 4 Life Sciences 1 2012
## 1429 1 4 Medical 1 2013
## 1430 24 1 Life Sciences 1 2014
## 1431 10 3 Medical 1 2015
## 1432 1 4 Marketing 1 2016
## 1433 10 3 Life Sciences 1 2017
## 1434 8 2 Other 1 2018
## 1435 29 4 Life Sciences 1 2019
## 1436 1 3 Medical 1 2020
## 1437 5 1 Medical 1 2021
## 1438 9 3 Life Sciences 1 2022
## 1439 9 3 Marketing 1 2023
## 1440 3 3 Medical 1 2024
## 1441 4 2 Life Sciences 1 2025
## 1442 1 4 Life Sciences 1 2026
## 1443 1 4 Medical 1 2027
## 1444 2 3 Life Sciences 1 2031
## 1445 7 2 Technical Degree 1 2032
## 1446 28 4 Life Sciences 1 2034
## 1447 28 3 Marketing 1 2035
## 1448 15 4 Marketing 1 2036
## 1449 3 3 Life Sciences 1 2037
## 1450 2 3 Technical Degree 1 2038
## 1451 26 4 Life Sciences 1 2040
## 1452 10 2 Life Sciences 1 2041
## 1453 1 4 Life Sciences 1 2044
## 1454 11 4 Marketing 1 2045
## 1455 20 3 Life Sciences 1 2046
## 1456 2 4 Life Sciences 1 2048
## 1457 18 4 Life Sciences 1 2049
## 1458 2 4 Medical 1 2051
## 1459 1 4 Life Sciences 1 2052
## 1460 13 2 Other 1 2053
## 1461 28 4 Medical 1 2054
## 1462 28 3 Marketing 1 2055
## 1463 24 1 Marketing 1 2056
## 1464 5 3 Medical 1 2057
## 1465 5 3 Other 1 2060
## 1466 23 2 Medical 1 2061
## 1467 6 1 Medical 1 2062
## 1468 4 3 Life Sciences 1 2064
## 1469 2 3 Medical 1 2065
## 1470 8 3 Medical 1 2068
## EnvironmentSatisfaction Gender HourlyRate JobInvolvement JobLevel
## 1 2 Female 94 3 2
## 2 3 Male 61 2 2
## 3 4 Male 92 2 1
## 4 4 Female 56 3 1
## 5 1 Male 40 3 1
## 6 4 Male 79 3 1
## 7 3 Female 81 4 1
## 8 4 Male 67 3 1
## 9 4 Male 44 2 3
## 10 3 Male 94 3 2
## 11 1 Male 84 4 1
## 12 4 Female 49 2 2
## 13 1 Male 31 3 1
## 14 2 Male 93 3 1
## 15 3 Male 50 2 1
## 16 2 Female 51 4 3
## 17 1 Male 80 4 1
## 18 4 Male 96 4 1
## 19 1 Female 78 2 4
## 20 4 Male 45 3 1
## 21 1 Female 96 4 2
## 22 3 Male 82 2 1
## 23 1 Female 53 3 3
## 24 3 Male 96 3 1
## 25 2 Male 83 3 1
## 26 3 Female 58 3 5
## 27 2 Female 72 1 1
## 28 3 Male 48 3 2
## 29 1 Female 42 2 3
## 30 2 Female 83 3 5
## 31 3 Male 78 3 1
## 32 4 Male 41 3 2
## 33 4 Male 83 2 1
## 34 4 Male 56 3 2
## 35 2 Male 61 3 1
## 36 4 Female 72 4 1
## 37 1 Male 86 2 1
## 38 4 Female 97 3 1
## 39 2 Female 82 2 1
## 40 3 Female 42 4 2
## 41 3 Male 75 3 1
## 42 4 Female 33 3 1
## 43 1 Male 48 1 1
## 44 4 Male 37 3 3
## 45 3 Female 58 3 2
## 46 2 Female 49 3 5
## 47 2 Male 72 3 2
## 48 2 Male 73 3 1
## 49 1 Male 98 3 2
## 50 4 Male 36 4 1
## 51 1 Male 98 2 3
## 52 3 Male 50 3 1
## 53 2 Female 75 3 2
## 54 3 Male 79 2 3
## 55 3 Female 47 2 2
## 56 1 Female 98 3 3
## 57 2 Male 71 3 3
## 58 3 Female 30 3 1
## 59 4 Male 48 3 2
## 60 1 Male 51 2 2
## 61 1 Male 33 3 2
## 62 4 Female 50 3 2
## 63 2 Female 43 2 5
## 64 1 Female 99 3 3
## 65 3 Female 59 3 3
## 66 4 Female 33 3 4
## 67 2 Male 95 2 2
## 68 2 Male 59 3 3
## 69 2 Male 79 3 1
## 70 4 Male 79 2 1
## 71 1 Female 57 2 2
## 72 3 Male 76 3 1
## 73 3 Male 87 3 1
## 74 2 Male 66 3 2
## 75 2 Female 55 4 1
## 76 3 Female 61 3 2
## 77 3 Male 32 2 2
## 78 4 Male 52 3 3
## 79 1 Male 30 3 3
## 80 2 Male 80 3 2
## 81 4 Male 55 2 2
## 82 2 Male 30 2 1
## 83 1 Male 70 3 3
## 84 2 Female 79 1 2
## 85 1 Male 94 3 2
## 86 4 Male 49 1 3
## 87 3 Male 62 3 1
## 88 4 Male 96 3 1
## 89 3 Male 99 2 2
## 90 3 Male 64 2 3
## 91 3 Male 78 2 4
## 92 3 Male 71 3 2
## 93 3 Female 63 2 2
## 94 3 Male 40 2 3
## 95 2 Male 87 3 2
## 96 1 Female 60 3 3
## 97 1 Female 33 3 2
## 98 2 Male 43 3 2
## 99 4 Male 37 3 4
## 100 2 Male 67 3 2
## 101 3 Male 63 3 1
## 102 4 Male 71 3 1
## 103 4 Female 66 2 1
## 104 1 Female 41 3 2
## 105 3 Male 100 2 2
## 106 3 Female 32 2 5
## 107 1 Female 73 3 5
## 108 3 Male 46 2 2
## 109 4 Male 64 2 1
## 110 2 Female 59 3 1
## 111 1 Female 30 2 3
## 112 1 Male 66 1 2
## 113 4 Female 30 4 4
## 114 2 Male 52 3 1
## 115 3 Female 45 2 2
## 116 3 Male 87 3 3
## 117 3 Female 45 2 3
## 118 2 Female 92 3 3
## 119 1 Female 39 3 1
## 120 3 Male 92 3 4
## 121 1 Male 96 1 1
## 122 3 Male 95 3 2
## 123 2 Female 72 3 1
## 124 1 Male 51 3 5
## 125 2 Male 76 1 2
## 126 3 Female 46 2 1
## 127 4 Female 94 3 3
## 128 4 Male 50 3 1
## 129 3 Male 100 3 1
## 130 3 Female 96 3 2
## 131 2 Female 72 4 1
## 132 3 Female 77 3 3
## 133 2 Female 71 1 2
## 134 3 Male 96 3 3
## 135 3 Female 61 3 1
## 136 2 Male 84 3 2
## 137 1 Male 53 1 3
## 138 4 Female 47 2 2
## 139 1 Male 41 2 2
## 140 3 Male 48 3 2
## 141 1 Female 41 3 1
## 142 3 Male 83 3 1
## 143 4 Female 32 3 2
## 144 1 Female 75 3 1
## 145 4 Male 35 1 2
## 146 4 Female 84 3 1
## 147 2 Male 35 2 1
## 148 4 Male 91 2 4
## 149 3 Male 94 3 1
## 150 2 Female 79 3 1
## 151 2 Female 54 3 2
## 152 3 Male 94 3 3
## 153 2 Male 34 3 2
## 154 2 Male 60 3 2
## 155 2 Female 43 3 3
## 156 4 Male 41 3 2
## 157 2 Male 34 2 2
## 158 2 Female 75 2 1
## 159 3 Male 67 2 3
## 160 3 Female 75 3 1
## 161 3 Male 80 3 1
## 162 4 Male 57 3 1
## 163 3 Male 42 3 1
## 164 3 Male 83 4 3
## 165 3 Male 79 2 1
## 166 3 Female 53 3 5
## 167 1 Male 56 3 1
## 168 2 Female 41 3 3
## 169 1 Female 59 2 2
## 170 3 Male 43 3 1
## 171 3 Male 51 3 1
## 172 3 Female 100 1 1
## 173 4 Male 30 3 1
## 174 3 Male 66 3 2
## 175 3 Female 30 3 2
## 176 3 Female 67 3 1
## 177 3 Male 90 3 1
## 178 2 Male 47 2 1
## 179 2 Female 92 3 3
## 180 3 Female 75 3 1
## 181 3 Female 95 3 1
## 182 4 Female 95 3 1
## 183 2 Female 70 3 1
## 184 3 Male 86 2 1
## 185 4 Female 57 4 2
## 186 4 Female 72 3 1
## 187 4 Female 46 3 5
## 188 3 Male 61 4 5
## 189 4 Male 45 2 2
## 190 4 Female 98 3 4
## 191 3 Male 65 2 5
## 192 4 Female 99 3 1
## 193 2 Male 50 2 2
## 194 4 Male 37 4 1
## 195 1 Male 65 2 4
## 196 2 Male 65 4 1
## 197 2 Female 37 3 2
## 198 3 Female 36 3 2
## 199 4 Male 88 3 2
## 200 4 Male 54 3 2
## 201 3 Male 60 2 2
## 202 4 Male 92 3 2
## 203 4 Male 43 3 1
## 204 3 Male 99 3 2
## 205 2 Male 70 3 2
## 206 2 Female 35 3 3
## 207 4 Male 60 4 1
## 208 2 Female 73 3 1
## 209 4 Male 63 2 2
## 210 4 Male 97 3 3
## 211 4 Male 32 1 3
## 212 3 Male 88 2 3
## 213 4 Female 90 3 2
## 214 2 Male 81 2 3
## 215 4 Female 88 3 1
## 216 4 Female 75 3 3
## 217 3 Female 52 2 2
## 218 3 Male 85 3 1
## 219 4 Female 57 2 3
## 220 4 Female 52 3 2
## 221 4 Male 62 3 2
## 222 3 Female 47 2 1
## 223 2 Male 47 3 3
## 224 1 Male 76 3 3
## 225 3 Male 90 1 2
## 226 3 Male 70 2 1
## 227 1 Male 41 3 1
## 228 2 Female 42 3 3
## 229 3 Female 92 3 3
## 230 3 Male 86 2 1
## 231 3 Female 89 2 1
## 232 3 Male 58 3 5
## 233 2 Male 52 3 1
## 234 4 Female 68 3 5
## 235 3 Male 58 3 1
## 236 4 Female 80 3 4
## 237 1 Female 39 3 1
## 238 1 Male 79 2 5
## 239 3 Female 56 3 1
## 240 4 Male 62 2 1
## 241 3 Female 96 3 1
## 242 3 Male 100 3 2
## 243 3 Male 36 3 2
## 244 1 Male 62 3 2
## 245 3 Male 70 4 5
## 246 2 Male 73 3 3
## 247 3 Female 63 2 1
## 248 4 Male 84 2 2
## 249 3 Female 83 2 1
## 250 1 Male 77 4 2
## 251 1 Male 61 3 3
## 252 3 Female 64 3 3
## 253 3 Male 60 3 1
## 254 4 Female 82 4 2
## 255 4 Male 45 3 2
## 256 1 Female 62 3 2
## 257 4 Female 56 2 1
## 258 1 Male 49 3 5
## 259 3 Male 96 3 1
## 260 3 Male 71 2 1
## 261 2 Male 100 4 1
## 262 4 Male 39 2 2
## 263 4 Male 84 2 2
## 264 3 Female 75 1 4
## 265 1 Male 79 3 1
## 266 1 Male 78 2 2
## 267 2 Male 64 2 2
## 268 2 Male 85 4 2
## 269 3 Male 79 3 4
## 270 4 Male 47 3 1
## 271 4 Male 81 3 5
## 272 1 Male 88 3 3
## 273 4 Male 94 3 1
## 274 3 Male 98 3 2
## 275 4 Male 100 2 1
## 276 1 Female 80 3 3
## 277 2 Female 71 4 3
## 278 1 Female 44 4 2
## 279 3 Female 84 3 2
## 280 1 Male 96 3 5
## 281 3 Male 45 3 4
## 282 2 Male 99 3 2
## 283 2 Male 44 3 2
## 284 2 Male 37 3 2
## 285 1 Male 60 3 2
## 286 4 Female 42 3 1
## 287 4 Male 43 3 1
## 288 4 Male 82 3 2
## 289 1 Male 45 3 1
## 290 4 Female 66 3 1
## 291 3 Female 35 3 5
## 292 3 Male 30 3 2
## 293 4 Female 84 3 1
## 294 4 Male 48 2 2
## 295 2 Male 53 3 1
## 296 3 Female 77 3 4
## 297 3 Male 54 3 1
## 298 3 Male 96 3 3
## 299 3 Male 81 4 1
## 300 4 Male 84 1 2
## 301 4 Male 88 3 4
## 302 4 Female 69 2 1
## 303 2 Male 68 4 2
## 304 2 Male 100 4 2
## 305 3 Male 48 4 3
## 306 2 Female 47 3 2
## 307 4 Male 91 2 2
## 308 1 Female 81 2 3
## 309 4 Male 32 1 2
## 310 3 Male 86 3 1
## 311 1 Male 62 2 2
## 312 1 Male 97 3 3
## 313 3 Male 32 3 1
## 314 4 Female 74 3 3
## 315 3 Male 99 3 4
## 316 3 Female 33 3 1
## 317 3 Female 90 2 4
## 318 3 Male 85 2 2
## 319 3 Female 85 3 1
## 320 3 Female 65 2 2
## 321 4 Male 74 3 2
## 322 4 Male 73 3 2
## 323 1 Female 74 4 2
## 324 1 Male 84 1 1
## 325 4 Female 64 3 2
## 326 3 Female 48 2 3
## 327 3 Male 54 2 5
## 328 4 Female 41 3 2
## 329 2 Male 46 2 2
## 330 4 Male 42 3 5
## 331 3 Female 82 2 2
## 332 3 Male 73 3 2
## 333 4 Female 31 3 2
## 334 3 Female 43 3 3
## 335 4 Male 75 3 2
## 336 2 Male 57 1 2
## 337 2 Male 77 1 1
## 338 2 Male 30 2 1
## 339 4 Female 30 2 2
## 340 2 Female 56 3 2
## 341 4 Male 61 3 2
## 342 3 Male 92 2 3
## 343 3 Female 39 3 3
## 344 4 Female 39 2 2
## 345 3 Male 62 3 3
## 346 3 Male 83 3 1
## 347 4 Male 95 2 2
## 348 2 Male 99 3 2
## 349 1 Female 44 3 4
## 350 4 Male 61 3 2
## 351 3 Male 52 3 1
## 352 3 Female 75 3 1
## 353 1 Female 91 3 3
## 354 3 Male 51 4 2
## 355 4 Female 85 3 2
## 356 3 Male 57 3 2
## 357 1 Male 98 2 2
## 358 1 Female 99 2 1
## 359 4 Female 45 3 2
## 360 1 Male 48 2 3
## 361 4 Male 88 3 2
## 362 4 Female 46 4 1
## 363 1 Male 39 3 1
## 364 4 Female 34 3 1
## 365 3 Female 98 3 1
## 366 3 Female 30 3 2
## 367 1 Male 56 3 2
## 368 4 Male 100 2 3
## 369 3 Male 68 2 2
## 370 3 Male 42 2 1
## 371 3 Female 90 4 1
## 372 4 Male 54 3 1
## 373 3 Male 97 2 2
## 374 4 Male 82 3 1
## 375 2 Male 92 3 2
## 376 2 Male 31 2 3
## 377 3 Female 87 3 2
## 378 4 Female 96 3 1
## 379 1 Male 67 4 2
## 380 3 Female 97 3 4
## 381 4 Female 77 3 2
## 382 3 Male 72 3 1
## 383 3 Male 73 3 1
## 384 1 Female 43 3 1
## 385 2 Male 61 2 3
## 386 3 Male 40 3 1
## 387 4 Female 95 3 1
## 388 4 Female 46 3 2
## 389 2 Female 95 3 1
## 390 3 Male 49 3 2
## 391 1 Male 59 2 4
## 392 2 Female 78 2 2
## 393 1 Male 86 3 5
## 394 3 Female 72 3 2
## 395 2 Female 31 3 2
## 396 4 Male 61 3 1
## 397 3 Female 74 3 2
## 398 2 Female 99 2 2
## 399 2 Female 72 3 2
## 400 4 Male 98 2 1
## 401 2 Male 52 3 5
## 402 3 Female 86 4 4
## 403 2 Female 83 3 2
## 404 2 Male 49 4 3
## 405 3 Male 79 3 2
## 406 1 Male 91 3 1
## 407 4 Male 39 2 3
## 408 1 Male 69 3 1
## 409 4 Female 30 3 4
## 410 1 Female 92 3 2
## 411 3 Female 43 1 2
## 412 1 Female 41 3 5
## 413 3 Female 87 3 2
## 414 4 Female 88 2 2
## 415 1 Female 62 3 1
## 416 4 Female 33 1 1
## 417 4 Male 42 3 1
## 418 3 Female 79 3 5
## 419 1 Female 90 3 1
## 420 3 Male 53 3 1
## 421 2 Male 93 2 3
## 422 3 Female 71 2 1
## 423 1 Male 52 2 1
## 424 3 Female 69 3 3
## 425 1 Male 56 2 4
## 426 2 Male 88 2 4
## 427 3 Female 49 3 1
## 428 3 Female 80 2 3
## 429 1 Female 65 3 2
## 430 1 Male 51 3 4
## 431 4 Male 46 1 1
## 432 3 Female 42 3 2
## 433 4 Male 62 2 1
## 434 3 Female 94 2 3
## 435 3 Male 33 3 3
## 436 2 Male 56 3 3
## 437 1 Male 38 1 1
## 438 4 Male 57 3 1
## 439 4 Male 72 3 3
## 440 1 Male 66 3 3
## 441 2 Female 43 3 3
## 442 2 Male 97 3 1
## 443 2 Male 32 3 3
## 444 3 Male 99 2 1
## 445 2 Female 37 3 2
## 446 1 Female 83 3 4
## 447 4 Male 56 3 2
## 448 2 Male 85 3 2
## 449 2 Female 75 3 4
## 450 3 Female 48 3 1
## 451 2 Male 77 3 2
## 452 4 Male 56 3 3
## 453 4 Male 61 3 2
## 454 2 Female 58 3 1
## 455 4 Male 34 3 2
## 456 1 Female 95 4 4
## 457 3 Male 44 2 3
## 458 2 Male 69 3 1
## 459 3 Male 58 1 3
## 460 1 Female 62 2 2
## 461 1 Male 45 3 2
## 462 1 Female 80 3 2
## 463 4 Male 74 4 2
## 464 3 Male 66 1 1
## 465 2 Female 59 3 3
## 466 1 Female 86 3 3
## 467 2 Female 91 3 4
## 468 1 Male 69 3 3
## 469 4 Male 78 3 2
## 470 4 Male 60 4 2
## 471 4 Male 38 3 1
## 472 3 Female 76 3 2
## 473 2 Female 65 3 2
## 474 4 Male 58 2 5
## 475 2 Male 89 4 1
## 476 1 Male 66 3 2
## 477 4 Male 94 2 1
## 478 1 Male 99 3 5
## 479 2 Male 40 3 1
## 480 1 Female 55 3 1
## 481 2 Male 74 2 1
## 482 2 Male 83 2 1
## 483 2 Male 46 3 2
## 484 1 Male 54 2 2
## 485 1 Male 66 4 2
## 486 1 Female 66 2 1
## 487 4 Male 75 3 2
## 488 4 Female 83 2 1
## 489 3 Female 81 3 2
## 490 2 Male 50 2 4
## 491 3 Female 43 3 1
## 492 4 Male 72 3 2
## 493 4 Female 40 2 4
## 494 1 Female 50 2 2
## 495 3 Female 31 3 1
## 496 3 Male 85 3 1
## 497 3 Male 49 3 1
## 498 4 Male 35 3 5
## 499 1 Male 69 3 1
## 500 3 Male 39 3 2
## 501 1 Female 92 3 2
## 502 3 Female 71 3 1
## 503 4 Female 34 3 2
## 504 2 Male 42 3 1
## 505 1 Female 100 3 2
## 506 3 Female 71 3 1
## 507 3 Male 36 3 3
## 508 2 Female 62 3 2
## 509 2 Male 82 1 2
## 510 3 Male 30 3 2
## 511 3 Male 88 3 3
## 512 2 Male 48 2 2
## 513 1 Male 54 2 1
## 514 4 Male 32 3 1
## 515 1 Male 70 3 1
## 516 3 Male 41 2 1
## 517 1 Male 58 4 1
## 518 4 Male 57 4 2
## 519 4 Female 46 2 2
## 520 2 Male 76 1 1
## 521 2 Male 56 4 2
## 522 4 Female 71 4 2
## 523 4 Male 80 4 1
## 524 4 Male 74 4 1
## 525 4 Female 46 2 3
## 526 1 Female 65 3 2
## 527 4 Female 80 2 2
## 528 4 Male 55 3 2
## 529 2 Male 50 3 2
## 530 2 Female 33 4 2
## 531 3 Female 68 3 3
## 532 3 Female 39 3 3
## 533 4 Male 42 3 2
## 534 4 Male 48 2 3
## 535 3 Male 59 4 4
## 536 2 Male 73 2 5
## 537 1 Male 84 3 2
## 538 4 Male 32 3 3
## 539 4 Male 59 2 5
## 540 4 Male 54 3 1
## 541 1 Female 67 1 1
## 542 1 Female 63 4 3
## 543 3 Female 81 3 3
## 544 1 Male 49 1 1
## 545 4 Female 49 3 4
## 546 3 Male 99 3 2
## 547 3 Male 99 3 1
## 548 3 Male 57 4 1
## 549 4 Male 47 2 2
## 550 2 Female 96 3 2
## 551 2 Male 37 3 1
## 552 3 Female 44 4 2
## 553 3 Male 81 3 4
## 554 4 Female 86 2 1
## 555 4 Female 55 2 2
## 556 4 Male 83 3 1
## 557 4 Male 86 3 2
## 558 4 Female 61 3 2
## 559 1 Male 80 3 2
## 560 4 Male 92 3 1
## 561 2 Female 32 3 2
## 562 3 Male 31 2 4
## 563 4 Male 63 3 1
## 564 3 Female 32 3 2
## 565 2 Male 46 1 2
## 566 1 Male 79 4 1
## 567 2 Female 77 4 2
## 568 4 Male 60 3 2
## 569 4 Male 78 3 5
## 570 1 Male 76 2 3
## 571 4 Male 41 3 1
## 572 1 Female 71 1 1
## 573 2 Female 66 3 2
## 574 4 Male 38 2 2
## 575 2 Female 72 4 1
## 576 4 Female 95 3 2
## 577 3 Male 84 3 2
## 578 4 Female 82 3 1
## 579 1 Female 75 4 2
## 580 3 Female 86 2 1
## 581 1 Female 72 3 1
## 582 4 Male 38 1 1
## 583 3 Female 38 4 2
## 584 3 Female 93 3 2
## 585 2 Male 66 3 5
## 586 3 Male 63 4 1
## 587 3 Male 60 2 1
## 588 4 Female 82 3 2
## 589 3 Male 64 3 4
## 590 2 Female 36 2 1
## 591 3 Male 49 3 3
## 592 1 Female 69 3 2
## 593 3 Female 33 3 4
## 594 3 Female 35 3 2
## 595 3 Male 81 4 1
## 596 4 Male 31 3 5
## 597 4 Female 40 4 1
## 598 4 Female 43 2 2
## 599 3 Male 46 3 1
## 600 3 Male 36 3 1
## 601 3 Female 98 2 2
## 602 1 Male 51 2 2
## 603 3 Female 52 2 2
## 604 2 Female 95 2 1
## 605 2 Male 98 3 2
## 606 1 Male 45 2 2
## 607 3 Female 100 2 1
## 608 3 Female 43 3 3
## 609 3 Male 37 3 2
## 610 2 Male 94 3 4
## 611 3 Male 42 2 3
## 612 3 Male 59 3 3
## 613 2 Female 81 3 2
## 614 3 Male 88 3 1
## 615 3 Female 88 2 1
## 616 4 Male 50 3 1
## 617 1 Female 66 3 4
## 618 4 Male 50 3 2
## 619 1 Male 65 4 1
## 620 1 Male 48 4 2
## 621 3 Female 53 2 1
## 622 2 Male 56 3 2
## 623 2 Male 73 3 2
## 624 2 Male 75 2 1
## 625 1 Female 78 2 3
## 626 4 Male 74 3 3
## 627 3 Female 91 4 2
## 628 3 Female 81 2 4
## 629 4 Male 66 2 2
## 630 2 Male 63 2 1
## 631 4 Male 33 2 2
## 632 1 Male 62 4 1
## 633 2 Male 35 3 1
## 634 1 Male 77 2 1
## 635 3 Male 98 3 2
## 636 4 Female 66 2 3
## 637 4 Female 96 3 1
## 638 4 Male 61 3 1
## 639 3 Male 87 2 2
## 640 3 Female 36 3 1
## 641 1 Male 46 2 1
## 642 2 Male 48 3 2
## 643 2 Male 98 2 1
## 644 3 Female 95 4 2
## 645 4 Male 48 3 1
## 646 2 Female 48 2 1
## 647 1 Male 73 3 4
## 648 4 Male 78 2 3
## 649 3 Female 54 3 1
## 650 4 Female 72 3 4
## 651 4 Female 33 3 2
## 652 3 Female 35 3 2
## 653 1 Male 32 3 3
## 654 1 Male 98 3 4
## 655 4 Female 42 2 2
## 656 4 Male 57 3 1
## 657 1 Male 87 3 1
## 658 1 Female 62 2 1
## 659 2 Male 61 3 1
## 660 1 Male 98 3 2
## 661 4 Male 57 2 1
## 662 1 Female 55 3 2
## 663 3 Female 49 2 1
## 664 4 Female 65 3 1
## 665 3 Male 68 3 2
## 666 4 Female 65 2 1
## 667 2 Female 34 3 2
## 668 2 Female 57 1 1
## 669 3 Female 77 3 1
## 670 4 Male 70 3 1
## 671 2 Female 76 3 1
## 672 2 Male 83 3 1
## 673 3 Female 68 2 2
## 674 3 Male 100 2 1
## 675 3 Female 37 2 3
## 676 2 Male 41 2 1
## 677 4 Female 51 3 2
## 678 1 Female 51 3 3
## 679 4 Male 51 3 1
## 680 4 Female 45 3 2
## 681 2 Male 65 3 1
## 682 4 Female 80 3 3
## 683 3 Female 70 2 1
## 684 3 Male 36 2 1
## 685 1 Male 67 2 3
## 686 3 Male 52 2 2
## 687 4 Male 59 3 1
## 688 3 Male 79 3 1
## 689 4 Male 37 2 1
## 690 1 Male 84 3 1
## 691 4 Female 41 3 2
## 692 4 Male 35 3 1
## 693 3 Female 93 3 2
## 694 3 Male 51 2 3
## 695 2 Female 42 2 2
## 696 1 Male 46 2 3
## 697 3 Male 57 3 2
## 698 3 Female 84 3 1
## 699 3 Female 86 3 2
## 700 4 Male 70 3 4
## 701 4 Male 51 3 1
## 702 3 Male 45 3 4
## 703 3 Male 62 3 3
## 704 3 Female 85 3 2
## 705 4 Male 92 3 3
## 706 1 Male 41 4 3
## 707 2 Female 100 4 4
## 708 3 Male 64 4 2
## 709 1 Male 84 3 2
## 710 3 Male 46 2 1
## 711 4 Male 38 3 4
## 712 4 Female 92 2 1
## 713 2 Female 53 3 1
## 714 4 Female 91 3 1
## 715 4 Male 66 3 4
## 716 3 Female 84 4 2
## 717 1 Male 64 3 5
## 718 3 Female 37 3 1
## 719 4 Male 42 3 2
## 720 4 Female 83 3 2
## 721 1 Female 48 3 1
## 722 4 Male 95 3 4
## 723 3 Male 66 3 1
## 724 4 Male 75 3 3
## 725 4 Female 41 2 2
## 726 3 Male 39 2 1
## 727 3 Female 96 3 2
## 728 2 Male 73 3 1
## 729 3 Female 56 3 3
## 730 3 Female 57 3 3
## 731 2 Female 73 4 3
## 732 4 Female 98 2 1
## 733 2 Female 60 3 1
## 734 4 Male 32 4 2
## 735 2 Male 94 1 1
## 736 1 Male 97 2 2
## 737 3 Male 78 2 3
## 738 4 Female 42 3 2
## 739 4 Female 65 2 4
## 740 1 Female 47 3 2
## 741 2 Male 45 3 1
## 742 4 Male 90 3 5
## 743 1 Male 64 3 1
## 744 3 Female 69 2 4
## 745 1 Female 61 1 2
## 746 3 Male 62 3 2
## 747 2 Female 55 1 5
## 748 2 Male 42 3 2
## 749 2 Male 79 1 2
## 750 1 Female 57 1 5
## 751 4 Female 53 4 4
## 752 4 Female 95 3 2
## 753 3 Female 43 4 1
## 754 4 Female 82 3 3
## 755 2 Female 88 2 1
## 756 4 Female 90 3 4
## 757 4 Female 69 3 1
## 758 2 Male 75 4 2
## 759 2 Male 66 3 3
## 760 2 Male 36 3 1
## 761 3 Female 38 2 3
## 762 1 Male 81 3 2
## 763 1 Male 57 3 1
## 764 3 Female 87 3 1
## 765 4 Male 74 3 1
## 766 3 Male 44 3 1
## 767 2 Male 62 3 5
## 768 4 Female 35 3 2
## 769 3 Male 74 3 2
## 770 1 Female 66 2 1
## 771 4 Male 40 3 5
## 772 3 Female 41 2 3
## 773 1 Female 63 3 1
## 774 4 Female 51 2 3
## 775 3 Male 40 2 4
## 776 3 Male 79 2 3
## 777 4 Female 54 3 1
## 778 3 Female 36 2 1
## 779 4 Female 74 2 2
## 780 1 Male 34 3 1
## 781 2 Male 72 2 3
## 782 1 Male 59 2 1
## 783 3 Male 85 3 2
## 784 2 Female 43 4 1
## 785 3 Female 65 2 3
## 786 1 Male 61 3 3
## 787 1 Male 87 1 1
## 788 4 Male 65 3 3
## 789 3 Female 59 3 2
## 790 2 Male 91 2 3
## 791 4 Male 34 2 3
## 792 4 Male 86 3 3
## 793 1 Female 54 2 2
## 794 1 Male 50 3 1
## 795 1 Male 45 3 2
## 796 4 Female 88 2 2
## 797 4 Male 32 3 1
## 798 1 Male 37 3 1
## 799 1 Male 55 2 1
## 800 4 Male 35 3 4
## 801 1 Male 45 2 1
## 802 4 Male 81 3 2
## 803 4 Female 30 3 2
## 804 3 Male 40 2 1
## 805 1 Male 35 4 4
## 806 2 Female 65 2 2
## 807 2 Male 87 3 3
## 808 3 Male 73 2 3
## 809 3 Female 93 3 1
## 810 4 Female 93 3 3
## 811 1 Male 52 3 4
## 812 4 Male 32 3 3
## 813 3 Female 83 3 3
## 814 1 Male 84 3 4
## 815 3 Male 44 2 5
## 816 4 Female 70 2 1
## 817 3 Male 70 3 2
## 818 1 Male 78 3 2
## 819 3 Male 67 4 1
## 820 1 Male 67 2 1
## 821 4 Male 54 3 2
## 822 4 Male 56 2 4
## 823 4 Male 95 3 2
## 824 4 Female 61 3 1
## 825 2 Male 56 1 2
## 826 2 Male 79 2 2
## 827 3 Male 37 4 1
## 828 3 Male 39 2 1
## 829 3 Male 80 3 1
## 830 1 Female 77 3 2
## 831 2 Male 46 3 1
## 832 3 Male 72 3 1
## 833 3 Female 52 2 2
## 834 4 Male 55 2 1
## 835 2 Female 70 3 2
## 836 3 Male 100 3 1
## 837 4 Female 45 2 3
## 838 2 Female 88 3 3
## 839 3 Male 44 3 4
## 840 2 Male 45 3 2
## 841 4 Male 58 2 1
## 842 4 Male 49 3 1
## 843 3 Female 79 3 1
## 844 1 Male 82 4 1
## 845 3 Male 72 2 2
## 846 3 Female 92 2 2
## 847 3 Male 44 2 3
## 848 4 Male 97 2 2
## 849 4 Male 47 2 1
## 850 1 Female 85 1 2
## 851 3 Female 76 3 1
## 852 4 Female 92 3 5
## 853 2 Female 54 3 1
## 854 3 Male 54 3 1
## 855 1 Female 83 3 1
## 856 4 Female 56 2 2
## 857 1 Male 87 2 1
## 858 3 Male 96 3 1
## 859 4 Female 50 3 5
## 860 2 Female 69 1 1
## 861 3 Male 48 2 1
## 862 3 Female 69 3 4
## 863 4 Male 74 1 1
## 864 2 Male 99 3 1
## 865 1 Male 95 2 1
## 866 3 Male 61 3 2
## 867 2 Male 62 3 2
## 868 4 Female 30 3 4
## 869 4 Male 78 2 1
## 870 4 Male 52 3 5
## 871 3 Male 94 3 2
## 872 4 Female 97 3 1
## 873 2 Female 82 3 2
## 874 3 Male 60 1 1
## 875 3 Male 49 3 2
## 876 4 Male 32 3 2
## 877 3 Male 43 4 1
## 878 4 Male 60 3 2
## 879 4 Male 79 4 2
## 880 2 Male 52 4 2
## 881 3 Female 77 2 1
## 882 4 Female 38 3 2
## 883 3 Female 77 1 3
## 884 1 Male 60 3 1
## 885 2 Female 84 2 2
## 886 3 Male 57 3 2
## 887 4 Male 63 3 1
## 888 1 Female 60 3 3
## 889 3 Female 78 2 3
## 890 1 Male 42 3 1
## 891 4 Female 53 3 3
## 892 1 Female 72 4 1
## 893 1 Female 96 2 1
## 894 1 Female 97 3 1
## 895 4 Male 85 3 4
## 896 3 Male 80 3 2
## 897 3 Female 96 2 2
## 898 3 Female 48 2 2
## 899 3 Male 96 1 5
## 900 1 Male 85 2 5
## 901 3 Male 46 3 1
## 902 4 Male 76 4 1
## 903 1 Male 76 3 1
## 904 3 Male 82 3 2
## 905 4 Male 76 2 5
## 906 4 Female 87 2 4
## 907 3 Female 89 4 1
## 908 2 Male 88 3 5
## 909 4 Male 82 4 3
## 910 2 Female 57 4 1
## 911 4 Male 47 3 1
## 912 3 Male 73 1 1
## 913 3 Male 53 3 1
## 914 1 Female 94 1 5
## 915 4 Male 37 2 4
## 916 1 Female 45 2 1
## 917 4 Female 33 2 5
## 918 3 Female 86 3 2
## 919 4 Male 83 3 5
## 920 4 Male 67 3 3
## 921 3 Female 46 3 2
## 922 4 Male 44 3 1
## 923 3 Male 92 4 5
## 924 3 Male 56 2 2
## 925 3 Male 66 3 1
## 926 2 Female 78 4 2
## 927 4 Female 56 2 3
## 928 3 Female 53 3 2
## 929 1 Female 73 3 3
## 930 4 Male 52 2 1
## 931 2 Female 40 2 1
## 932 3 Female 72 3 2
## 933 2 Female 39 3 1
## 934 4 Male 84 3 1
## 935 4 Female 40 3 1
## 936 3 Male 56 4 2
## 937 2 Female 83 3 5
## 938 3 Female 94 2 4
## 939 1 Male 88 3 1
## 940 4 Male 79 3 2
## 941 3 Male 93 3 1
## 942 1 Female 48 2 2
## 943 4 Female 63 3 3
## 944 4 Female 44 3 1
## 945 3 Female 55 1 2
## 946 4 Female 43 3 4
## 947 4 Male 57 2 3
## 948 2 Male 64 3 3
## 949 2 Female 95 3 3
## 950 1 Male 32 3 2
## 951 4 Female 57 3 3
## 952 3 Male 40 1 2
## 953 4 Female 54 3 1
## 954 1 Male 89 3 1
## 955 3 Male 37 3 4
## 956 4 Female 58 1 5
## 957 4 Male 99 3 5
## 958 3 Male 74 3 1
## 959 4 Male 86 3 3
## 960 3 Male 98 3 2
## 961 3 Female 66 3 2
## 962 3 Male 87 3 2
## 963 3 Male 84 3 4
## 964 2 Female 31 3 2
## 965 3 Female 66 3 2
## 966 4 Male 67 3 1
## 967 3 Female 53 2 3
## 968 2 Male 88 3 1
## 969 1 Female 83 4 2
## 970 4 Male 54 3 3
## 971 3 Female 98 4 1
## 972 4 Female 82 2 4
## 973 4 Female 97 3 1
## 974 4 Female 60 2 2
## 975 4 Male 43 1 2
## 976 1 Male 85 4 4
## 977 4 Male 68 3 4
## 978 1 Female 92 2 1
## 979 2 Female 89 4 2
## 980 2 Male 86 3 2
## 981 3 Female 90 2 1
## 982 4 Female 67 3 2
## 983 4 Male 57 4 1
## 984 3 Female 98 3 2
## 985 3 Male 48 2 2
## 986 4 Male 98 3 2
## 987 1 Male 44 2 2
## 988 2 Male 52 3 3
## 989 4 Female 75 3 2
## 990 3 Male 54 3 1
## 991 2 Male 61 3 2
## 992 3 Male 36 3 2
## 993 3 Male 71 3 3
## 994 1 Male 93 4 2
## 995 4 Female 59 4 4
## 996 1 Female 73 2 2
## 997 4 Female 98 2 2
## 998 4 Female 51 3 1
## 999 1 Male 36 2 1
## 1000 3 Female 31 3 4
## 1001 3 Female 54 3 1
## 1002 1 Female 94 3 1
## 1003 3 Male 60 3 3
## 1004 1 Male 81 3 1
## 1005 3 Male 100 4 1
## 1006 2 Male 51 2 3
## 1007 1 Male 97 2 2
## 1008 3 Female 84 3 3
## 1009 4 Female 54 3 4
## 1010 4 Female 76 3 5
## 1011 2 Male 81 4 4
## 1012 1 Female 99 3 2
## 1013 2 Female 50 1 1
## 1014 4 Female 73 3 2
## 1015 1 Female 93 3 4
## 1016 4 Male 91 3 1
## 1017 1 Female 34 2 1
## 1018 2 Male 91 3 1
## 1019 1 Male 37 2 2
## 1020 3 Female 98 2 2
## 1021 1 Male 74 3 1
## 1022 1 Male 68 2 1
## 1023 3 Male 84 4 1
## 1024 1 Female 90 3 1
## 1025 1 Female 82 3 4
## 1026 4 Female 42 3 2
## 1027 4 Male 97 3 2
## 1028 4 Female 86 2 1
## 1029 2 Male 90 4 1
## 1030 3 Male 81 3 2
## 1031 1 Male 31 3 3
## 1032 1 Male 52 3 3
## 1033 1 Female 54 2 1
## 1034 3 Female 100 4 3
## 1035 2 Male 95 1 3
## 1036 4 Female 96 4 1
## 1037 2 Male 94 3 1
## 1038 2 Male 55 3 3
## 1039 3 Male 96 3 2
## 1040 1 Female 52 3 1
## 1041 4 Male 55 2 3
## 1042 4 Male 84 3 2
## 1043 3 Male 90 2 1
## 1044 4 Male 39 4 4
## 1045 1 Male 96 3 2
## 1046 3 Male 68 3 1
## 1047 4 Male 49 3 1
## 1048 4 Male 54 3 2
## 1049 4 Male 81 1 2
## 1050 4 Male 96 3 2
## 1051 1 Female 74 3 1
## 1052 1 Female 79 3 2
## 1053 3 Male 64 3 1
## 1054 2 Male 93 4 2
## 1055 3 Male 35 3 3
## 1056 2 Male 71 3 4
## 1057 1 Male 92 3 1
## 1058 1 Female 51 3 2
## 1059 1 Female 40 2 2
## 1060 4 Male 76 3 1
## 1061 2 Male 89 3 1
## 1062 4 Female 78 3 1
## 1063 2 Male 86 3 3
## 1064 3 Male 77 2 2
## 1065 3 Male 46 3 1
## 1066 4 Male 30 3 2
## 1067 1 Female 82 2 1
## 1068 3 Female 78 3 2
## 1069 3 Male 38 2 1
## 1070 1 Male 72 2 1
## 1071 3 Male 55 3 2
## 1072 3 Female 43 2 2
## 1073 4 Female 97 3 1
## 1074 3 Male 96 1 2
## 1075 4 Male 69 3 2
## 1076 3 Male 64 3 3
## 1077 2 Female 87 3 4
## 1078 1 Male 100 2 1
## 1079 4 Male 32 3 4
## 1080 2 Female 32 3 3
## 1081 3 Female 51 3 4
## 1082 4 Female 91 2 3
## 1083 1 Male 97 3 2
## 1084 4 Male 86 3 1
## 1085 4 Male 64 2 3
## 1086 4 Female 33 3 1
## 1087 3 Male 88 1 4
## 1088 2 Male 55 3 1
## 1089 3 Male 68 2 1
## 1090 1 Male 47 3 2
## 1091 3 Female 39 1 2
## 1092 4 Male 44 2 2
## 1093 4 Male 97 3 1
## 1094 4 Male 40 3 3
## 1095 1 Male 47 3 2
## 1096 2 Male 79 3 2
## 1097 3 Male 38 3 4
## 1098 3 Male 57 2 1
## 1099 4 Male 72 3 2
## 1100 1 Male 66 3 3
## 1101 2 Female 98 2 1
## 1102 4 Female 67 2 2
## 1103 3 Male 70 3 1
## 1104 3 Female 96 3 2
## 1105 3 Male 91 4 1
## 1106 1 Male 46 3 2
## 1107 2 Male 64 2 2
## 1108 3 Male 71 3 2
## 1109 4 Male 68 2 1
## 1110 3 Male 33 3 3
## 1111 1 Female 69 3 1
## 1112 3 Female 78 2 3
## 1113 3 Male 81 3 2
## 1114 4 Male 62 3 2
## 1115 3 Female 65 3 1
## 1116 1 Male 35 3 1
## 1117 3 Male 60 2 5
## 1118 2 Male 45 3 2
## 1119 1 Female 89 3 1
## 1120 3 Male 80 3 2
## 1121 2 Female 90 3 2
## 1122 2 Female 73 3 2
## 1123 2 Male 87 3 1
## 1124 1 Female 51 3 2
## 1125 4 Male 38 4 3
## 1126 1 Male 87 3 2
## 1127 3 Male 59 3 5
## 1128 4 Male 45 4 1
## 1129 1 Male 80 4 2
## 1130 4 Male 93 2 5
## 1131 2 Male 46 4 2
## 1132 4 Male 92 2 2
## 1133 4 Female 84 3 2
## 1134 4 Male 87 4 1
## 1135 3 Male 63 2 1
## 1136 4 Male 56 4 4
## 1137 3 Male 51 3 1
## 1138 2 Female 85 2 1
## 1139 2 Male 41 3 4
## 1140 2 Female 35 4 1
## 1141 2 Female 31 3 5
## 1142 2 Male 48 2 1
## 1143 3 Female 50 1 2
## 1144 1 Male 52 2 2
## 1145 4 Male 54 3 2
## 1146 3 Female 76 3 2
## 1147 3 Male 42 4 2
## 1148 3 Female 84 3 1
## 1149 2 Male 76 3 2
## 1150 4 Male 67 2 1
## 1151 2 Male 48 4 2
## 1152 2 Female 39 1 2
## 1153 3 Male 97 3 1
## 1154 2 Female 70 3 1
## 1155 4 Female 98 3 5
## 1156 3 Male 76 2 2
## 1157 1 Female 80 2 3
## 1158 3 Female 52 3 2
## 1159 3 Male 85 3 2
## 1160 1 Female 81 3 2
## 1161 4 Female 59 2 2
## 1162 4 Female 54 2 3
## 1163 4 Male 55 2 3
## 1164 2 Female 71 3 1
## 1165 3 Female 84 3 3
## 1166 1 Male 37 3 2
## 1167 3 Male 89 2 4
## 1168 1 Male 59 1 2
## 1169 1 Female 32 3 1
## 1170 2 Female 86 4 1
## 1171 4 Male 87 3 1
## 1172 1 Male 73 3 1
## 1173 3 Male 42 2 2
## 1174 2 Female 42 3 3
## 1175 4 Male 77 4 2
## 1176 4 Male 66 3 2
## 1177 1 Female 72 3 4
## 1178 4 Female 50 2 3
## 1179 3 Female 31 3 1
## 1180 4 Female 66 3 2
## 1181 1 Male 77 3 1
## 1182 3 Female 41 2 4
## 1183 4 Female 33 2 2
## 1184 4 Male 79 4 2
## 1185 2 Female 91 3 4
## 1186 3 Male 65 2 4
## 1187 4 Male 36 3 2
## 1188 4 Male 90 3 2
## 1189 1 Male 43 2 2
## 1190 4 Male 93 3 2
## 1191 4 Male 45 3 2
## 1192 1 Female 67 3 2
## 1193 4 Female 74 3 1
## 1194 4 Female 42 2 1
## 1195 2 Female 47 4 4
## 1196 3 Male 36 3 4
## 1197 4 Male 80 3 3
## 1198 4 Male 54 3 1
## 1199 3 Female 36 3 2
## 1200 1 Male 80 3 2
## 1201 3 Female 44 3 1
## 1202 4 Male 93 2 1
## 1203 4 Female 87 3 1
## 1204 2 Male 48 4 3
## 1205 4 Female 95 3 1
## 1206 4 Male 95 3 1
## 1207 4 Male 76 3 1
## 1208 1 Male 94 2 1
## 1209 2 Male 57 2 2
## 1210 3 Male 92 1 3
## 1211 2 Male 79 4 1
## 1212 3 Male 31 1 2
## 1213 2 Female 35 2 1
## 1214 3 Male 99 3 1
## 1215 3 Female 96 4 3
## 1216 1 Male 79 2 1
## 1217 4 Male 73 3 2
## 1218 3 Male 62 4 1
## 1219 4 Male 35 3 3
## 1220 4 Female 43 3 1
## 1221 3 Female 51 3 2
## 1222 3 Male 74 2 3
## 1223 4 Male 58 1 1
## 1224 3 Male 82 1 4
## 1225 4 Male 62 1 1
## 1226 4 Female 48 2 4
## 1227 1 Male 56 3 1
## 1228 2 Male 69 3 1
## 1229 3 Male 60 1 2
## 1230 2 Female 92 3 2
## 1231 2 Male 91 3 1
## 1232 3 Male 34 3 2
## 1233 4 Male 49 3 2
## 1234 2 Male 33 3 1
## 1235 3 Male 87 3 2
## 1236 3 Male 74 3 3
## 1237 2 Male 96 2 2
## 1238 1 Male 34 1 2
## 1239 3 Female 51 3 1
## 1240 4 Female 30 3 2
## 1241 4 Male 77 3 2
## 1242 4 Male 80 1 3
## 1243 2 Male 88 3 5
## 1244 3 Female 56 1 3
## 1245 4 Female 78 2 1
## 1246 1 Male 59 3 1
## 1247 3 Female 66 2 1
## 1248 1 Male 51 3 2
## 1249 3 Female 67 3 1
## 1250 2 Female 52 1 1
## 1251 4 Male 70 4 2
## 1252 3 Male 94 2 3
## 1253 4 Male 97 4 1
## 1254 4 Female 62 3 2
## 1255 4 Female 80 3 2
## 1256 1 Female 74 3 3
## 1257 3 Female 75 2 1
## 1258 1 Male 70 3 3
## 1259 4 Female 91 2 1
## 1260 3 Male 58 4 2
## 1261 2 Male 65 3 1
## 1262 2 Male 60 1 2
## 1263 3 Male 38 2 1
## 1264 2 Male 57 3 1
## 1265 3 Male 60 2 5
## 1266 4 Male 90 3 2
## 1267 3 Male 94 3 1
## 1268 4 Male 87 3 2
## 1269 1 Female 60 2 4
## 1270 2 Male 97 3 1
## 1271 4 Female 32 1 2
## 1272 2 Male 31 3 1
## 1273 4 Female 40 2 1
## 1274 3 Female 79 3 1
## 1275 1 Female 91 2 2
## 1276 1 Female 53 3 3
## 1277 2 Male 46 2 2
## 1278 3 Male 46 3 5
## 1279 4 Male 90 3 3
## 1280 3 Male 99 3 1
## 1281 3 Male 89 3 2
## 1282 3 Male 84 3 2
## 1283 4 Male 90 4 1
## 1284 3 Male 82 3 1
## 1285 3 Male 65 3 3
## 1286 2 Male 98 2 2
## 1287 3 Female 99 3 1
## 1288 2 Male 95 4 2
## 1289 2 Male 90 3 2
## 1290 1 Male 100 3 1
## 1291 4 Male 93 3 2
## 1292 4 Male 58 3 2
## 1293 3 Male 83 3 2
## 1294 1 Male 52 3 1
## 1295 2 Male 85 4 2
## 1296 3 Female 81 3 3
## 1297 1 Female 64 3 3
## 1298 4 Female 69 3 1
## 1299 4 Female 66 3 2
## 1300 3 Female 52 3 2
## 1301 2 Male 92 4 2
## 1302 2 Male 52 3 4
## 1303 2 Male 91 1 1
## 1304 3 Female 92 2 3
## 1305 2 Female 47 3 2
## 1306 4 Female 68 3 2
## 1307 1 Female 68 3 3
## 1308 3 Female 67 3 1
## 1309 2 Female 77 1 2
## 1310 3 Male 46 3 2
## 1311 1 Male 87 3 4
## 1312 2 Female 33 3 1
## 1313 4 Male 89 4 1
## 1314 1 Male 56 2 1
## 1315 3 Female 67 3 2
## 1316 4 Female 73 3 2
## 1317 1 Male 92 3 2
## 1318 4 Female 53 3 1
## 1319 4 Male 40 3 1
## 1320 4 Male 79 3 2
## 1321 3 Male 38 3 1
## 1322 2 Female 64 3 1
## 1323 4 Male 82 3 3
## 1324 3 Male 43 3 1
## 1325 4 Male 93 1 2
## 1326 4 Male 81 3 1
## 1327 3 Male 82 2 2
## 1328 1 Female 45 4 4
## 1329 2 Female 36 2 2
## 1330 4 Male 87 2 1
## 1331 1 Female 81 2 5
## 1332 4 Male 91 2 5
## 1333 4 Male 73 2 1
## 1334 3 Female 64 3 3
## 1335 4 Female 77 2 1
## 1336 4 Male 41 3 2
## 1337 2 Male 98 2 1
## 1338 2 Female 78 3 1
## 1339 2 Male 89 3 1
## 1340 4 Male 75 3 1
## 1341 2 Female 63 2 2
## 1342 2 Male 89 3 2
## 1343 3 Male 64 3 3
## 1344 4 Male 59 3 1
## 1345 4 Male 78 3 2
## 1346 4 Female 44 2 2
## 1347 2 Female 93 2 2
## 1348 2 Male 94 2 2
## 1349 1 Male 98 3 4
## 1350 2 Female 90 2 1
## 1351 1 Female 89 4 2
## 1352 4 Female 58 3 4
## 1353 2 Male 78 4 2
## 1354 4 Male 85 1 1
## 1355 1 Male 97 3 1
## 1356 3 Male 33 2 2
## 1357 3 Female 54 3 2
## 1358 3 Male 83 3 3
## 1359 3 Female 86 3 2
## 1360 4 Female 75 2 2
## 1361 1 Female 62 4 1
## 1362 3 Male 61 4 1
## 1363 2 Male 44 3 2
## 1364 2 Male 43 3 2
## 1365 3 Male 42 2 2
## 1366 3 Male 45 4 1
## 1367 1 Female 32 1 2
## 1368 2 Male 41 3 1
## 1369 3 Male 86 2 2
## 1370 4 Female 84 3 2
## 1371 3 Male 64 3 2
## 1372 4 Female 89 2 2
## 1373 1 Male 87 3 2
## 1374 4 Female 58 2 2
## 1375 4 Female 72 3 4
## 1376 1 Female 47 4 1
## 1377 2 Male 71 3 1
## 1378 2 Male 42 3 5
## 1379 2 Male 77 3 2
## 1380 1 Female 58 2 1
## 1381 2 Male 71 3 2
## 1382 3 Male 49 3 1
## 1383 3 Male 48 3 1
## 1384 1 Male 66 4 1
## 1385 1 Male 32 3 3
## 1386 4 Male 39 3 3
## 1387 3 Male 95 3 1
## 1388 1 Male 89 3 2
## 1389 3 Female 34 3 2
## 1390 4 Male 48 1 2
## 1391 3 Male 32 2 1
## 1392 1 Male 59 2 1
## 1393 3 Female 55 3 2
## 1394 4 Male 44 3 2
## 1395 4 Male 56 2 2
## 1396 1 Male 63 3 2
## 1397 1 Male 66 3 3
## 1398 1 Female 67 3 2
## 1399 4 Male 36 3 2
## 1400 1 Male 30 3 3
## 1401 4 Male 88 3 1
## 1402 3 Male 71 4 5
## 1403 4 Female 59 1 1
## 1404 2 Male 77 3 4
## 1405 4 Male 37 2 2
## 1406 3 Female 40 3 3
## 1407 3 Female 77 3 2
## 1408 2 Male 45 2 2
## 1409 4 Male 78 3 1
## 1410 4 Female 73 3 2
## 1411 2 Female 92 3 2
## 1412 3 Female 82 3 1
## 1413 4 Male 76 3 1
## 1414 4 Male 57 3 1
## 1415 1 Male 84 3 3
## 1416 2 Male 59 2 1
## 1417 4 Male 86 3 2
## 1418 1 Male 54 3 1
## 1419 1 Male 72 2 2
## 1420 4 Male 35 3 2
## 1421 4 Male 76 3 1
## 1422 3 Female 98 3 3
## 1423 4 Male 43 3 1
## 1424 4 Male 63 3 1
## 1425 2 Male 48 3 2
## 1426 2 Female 95 3 2
## 1427 3 Female 49 2 1
## 1428 1 Male 83 3 1
## 1429 2 Male 68 2 1
## 1430 1 Male 52 3 2
## 1431 2 Female 99 1 3
## 1432 3 Female 48 3 3
## 1433 3 Female 42 4 3
## 1434 1 Female 85 3 2
## 1435 1 Male 40 3 1
## 1436 2 Male 42 3 1
## 1437 3 Male 58 3 1
## 1438 4 Male 87 3 5
## 1439 4 Male 33 3 1
## 1440 1 Female 94 2 3
## 1441 4 Female 97 3 2
## 1442 3 Male 57 3 2
## 1443 1 Male 36 3 1
## 1444 1 Male 56 3 5
## 1445 4 Male 72 3 1
## 1446 1 Female 60 2 4
## 1447 4 Female 95 2 2
## 1448 4 Male 88 1 2
## 1449 3 Male 57 2 2
## 1450 4 Male 78 3 1
## 1451 3 Female 31 3 3
## 1452 1 Female 100 3 2
## 1453 2 Male 94 3 2
## 1454 2 Female 100 2 2
## 1455 4 Female 50 3 2
## 1456 3 Male 52 2 1
## 1457 3 Male 80 3 2
## 1458 3 Female 98 3 1
## 1459 3 Female 62 1 1
## 1460 4 Male 46 2 2
## 1461 4 Female 73 2 1
## 1462 4 Male 39 2 3
## 1463 2 Female 60 2 4
## 1464 2 Male 74 3 2
## 1465 4 Female 30 2 1
## 1466 3 Male 41 4 2
## 1467 4 Male 42 2 3
## 1468 2 Male 87 4 2
## 1469 4 Male 63 2 2
## 1470 2 Male 82 4 2
## JobRole JobSatisfaction MaritalStatus MonthlyIncome
## 1 Sales Executive Very High Single 5993
## 2 Research Scientist Medium Married 5130
## 3 Laboratory Technician High Single 2090
## 4 Research Scientist High Married 2909
## 5 Laboratory Technician Medium Married 3468
## 6 Laboratory Technician Very High Single 3068
## 7 Laboratory Technician Low Married 2670
## 8 Laboratory Technician High Divorced 2693
## 9 Manufacturing Director High Single 9526
## 10 Healthcare Representative High Married 5237
## 11 Laboratory Technician Medium Married 2426
## 12 Laboratory Technician High Single 4193
## 13 Research Scientist High Divorced 2911
## 14 Laboratory Technician Very High Divorced 2661
## 15 Laboratory Technician High Single 2028
## 16 Manufacturing Director Low Divorced 9980
## 17 Research Scientist Medium Divorced 3298
## 18 Laboratory Technician Very High Divorced 2935
## 19 Manager Very High Married 15427
## 20 Research Scientist Very High Single 3944
## 21 Manufacturing Director High Divorced 4011
## 22 Sales Representative Low Single 3407
## 23 Research Director Medium Single 11994
## 24 Research Scientist Very High Single 1232
## 25 Research Scientist Low Single 2960
## 26 Manager High Divorced 19094
## 27 Research Scientist Low Single 3919
## 28 Sales Executive Medium Married 6825
## 29 Healthcare Representative Very High Married 10248
## 30 Manager Low Single 18947
## 31 Laboratory Technician Very High Single 2496
## 32 Healthcare Representative Very High Married 6465
## 33 Laboratory Technician High Single 2206
## 34 Sales Representative Very High Married 2086
## 35 Research Scientist Very High Married 2293
## 36 Research Scientist High Divorced 2645
## 37 Sales Representative High Married 2683
## 38 Sales Representative Very High Married 2014
## 39 Research Scientist Low Married 3419
## 40 Sales Executive Low Married 5376
## 41 Laboratory Technician Very High Divorced 1951
## 42 Laboratory Technician Low Divorced 2341
## 43 Laboratory Technician High Single 2293
## 44 Sales Executive High Single 8726
## 45 Laboratory Technician Very High Single 4011
## 46 Research Director High Married 19545
## 47 Sales Executive High Single 4568
## 48 Research Scientist Medium Married 3022
## 49 Sales Executive Very High Single 5772
## 50 Laboratory Technician Very High Married 2269
## 51 Laboratory Technician High Single 5381
## 52 Laboratory Technician High Single 3441
## 53 Sales Executive Low Divorced 5454
## 54 Healthcare Representative Low Married 9884
## 55 Sales Executive Very High Married 4157
## 56 Research Director Very High Single 13458
## 57 Sales Executive Low Married 9069
## 58 Laboratory Technician Low Married 4014
## 59 Laboratory Technician Very High Divorced 5915
## 60 Manufacturing Director High Divorced 5993
## 61 Manufacturing Director Very High Married 6162
## 62 Laboratory Technician Very High Single 2406
## 63 Research Director High Divorced 18740
## 64 Sales Executive Low Single 7637
## 65 Healthcare Representative High Divorced 10096
## 66 Manager High Divorced 14756
## 67 Manufacturing Director Medium Single 6499
## 68 Research Scientist Low Divorced 9724
## 69 Research Scientist Low Married 2194
## 70 Research Scientist High Married 3388
## 71 Sales Executive High Single 5473
## 72 Research Scientist Medium Married 2703
## 73 Research Scientist Medium Single 2501
## 74 Research Scientist Medium Married 6220
## 75 Laboratory Technician Very High Married 3038
## 76 Manufacturing Director Very High Single 4424
## 77 Sales Executive Low Single 4312
## 78 Research Director Low Married 13245
## 79 Research Director High Single 13664
## 80 Human Resources Medium Divorced 5021
## 81 Laboratory Technician Very High Married 5126
## 82 Research Scientist High Single 2859
## 83 Sales Executive Very High Married 10239
## 84 Research Scientist Very High Divorced 5329
## 85 Manufacturing Director Medium Married 4325
## 86 Manufacturing Director Very High Single 7260
## 87 Sales Representative Low Divorced 2322
## 88 Laboratory Technician Very High Married 2075
## 89 Healthcare Representative Very High Married 4152
## 90 Sales Executive Very High Single 9619
## 91 Healthcare Representative Medium Married 13503
## 92 Sales Executive Very High Single 5441
## 93 Sales Executive Medium Divorced 5209
## 94 Healthcare Representative Low Married 10673
## 95 Sales Executive High Single 5010
## 96 Research Director High Married 13549
## 97 Sales Executive High Married 4999
## 98 Sales Executive High Married 4221
## 99 Sales Executive High Single 13872
## 100 Laboratory Technician Medium Married 2042
## 101 Human Resources Low Divorced 2073
## 102 Research Scientist Low Single 2956
## 103 Laboratory Technician Very High Single 2926
## 104 Research Scientist High Single 4809
## 105 Healthcare Representative Very High Divorced 5163
## 106 Manager Very High Married 18844
## 107 Research Director Medium Married 18172
## 108 Sales Executive High Single 5744
## 109 Research Scientist Very High Married 2889
## 110 Laboratory Technician Very High Single 2871
## 111 Healthcare Representative Low Single 7484
## 112 Laboratory Technician High Single 6074
## 113 Manager Very High Single 17328
## 114 Laboratory Technician High Married 2774
## 115 Research Scientist Medium Divorced 4505
## 116 Sales Executive Very High Single 7428
## 117 Manager Low Single 11631
## 118 Sales Executive Very High Married 9738
## 119 Laboratory Technician Very High Divorced 2835
## 120 Manager Very High Married 16959
## 121 Research Scientist High Divorced 2613
## 122 Sales Executive Medium Married 6146
## 123 Research Scientist Medium Married 4963
## 124 Research Director High Single 19537
## 125 Sales Executive High Married 6172
## 126 Research Scientist Medium Married 2368
## 127 Healthcare Representative Very High Married 10312
## 128 Sales Representative High Single 1675
## 129 Laboratory Technician Very High Married 2523
## 130 Manufacturing Director Low Married 6567
## 131 Research Scientist High Single 4739
## 132 Sales Executive Very High Single 9208
## 133 Sales Executive High Married 4559
## 134 Sales Executive High Divorced 8189
## 135 Human Resources High Married 2942
## 136 Manufacturing Director Medium Divorced 4941
## 137 Manufacturing Director Very High Single 10650
## 138 Sales Executive High Married 5902
## 139 Sales Executive High Married 8639
## 140 Human Resources Very High Married 6347
## 141 Laboratory Technician Low Single 4200
## 142 Research Scientist Very High Single 3452
## 143 Research Scientist High Single 4317
## 144 Research Scientist High Single 2632
## 145 Sales Executive Very High Divorced 4668
## 146 Research Scientist Low Divorced 3204
## 147 Laboratory Technician Very High Single 2720
## 148 Manager Low Divorced 17181
## 149 Laboratory Technician Low Married 2238
## 150 Laboratory Technician Medium Single 1483
## 151 Research Scientist Medium Divorced 5605
## 152 Sales Executive Medium Married 7295
## 153 Sales Representative High Married 2306
## 154 Laboratory Technician Medium Divorced 2348
## 155 Sales Executive Very High Single 8998
## 156 Manufacturing Director High Married 4319
## 157 Manufacturing Director High Married 6132
## 158 Research Scientist Medium Married 3346
## 159 Sales Executive Very High Married 10855
## 160 Sales Representative High Married 2231
## 161 Research Scientist Very High Married 2323
## 162 Research Scientist Medium Divorced 2024
## 163 Research Scientist Very High Married 2713
## 164 Healthcare Representative Very High Divorced 9439
## 165 Research Scientist High Divorced 2566
## 166 Manager Medium Single 19926
## 167 Research Scientist High Divorced 2451
## 168 Sales Executive Very High Married 9419
## 169 Sales Executive Very High Single 8686
## 170 Research Scientist High Single 3038
## 171 Research Scientist Medium Married 3058
## 172 Sales Representative Low Single 2325
## 173 Laboratory Technician Medium Single 2088
## 174 Laboratory Technician Low Divorced 3072
## 175 Sales Executive Low Divorced 5006
## 176 Research Scientist Low Divorced 4257
## 177 Research Scientist Very High Single 2500
## 178 Laboratory Technician Very High Single 1102
## 179 Sales Executive Low Divorced 10453
## 180 Laboratory Technician Very High Single 2288
## 181 Research Scientist Very High Married 3929
## 182 Research Scientist Medium Single 2311
## 183 Sales Representative Medium Single 3140
## 184 Laboratory Technician High Married 3690
## 185 Manufacturing Director Low Divorced 4450
## 186 Research Scientist Medium Married 2756
## 187 Manager High Married 19033
## 188 Research Director Medium Single 18722
## 189 Manufacturing Director High Married 9547
## 190 Healthcare Representative Medium Single 13734
## 191 Manager High Married 19999
## 192 Research Scientist Medium Single 2279
## 193 Manufacturing Director High Married 5916
## 194 Research Scientist Very High Divorced 2089
## 195 Manager Very High Married 16792
## 196 Research Scientist Low Married 3564
## 197 Laboratory Technician Medium Single 4425
## 198 Manufacturing Director High Divorced 5265
## 199 Manufacturing Director High Married 6553
## 200 Manufacturing Director Very High Married 6261
## 201 Manufacturing Director Low Married 4298
## 202 Manufacturing Director Very High Divorced 6804
## 203 Research Scientist High Divorced 3815
## 204 Laboratory Technician Very High Married 2741
## 205 Healthcare Representative Low Married 6673
## 206 Sales Executive Very High Married 7639
## 207 Research Scientist Medium Divorced 2328
## 208 Laboratory Technician Very High Single 2153
## 209 Healthcare Representative Very High Married 4876
## 210 Healthcare Representative Low Divorced 9396
## 211 Sales Executive Very High Married 10400
## 212 Manufacturing Director High Single 8474
## 213 Sales Executive High Single 9981
## 214 Research Director Medium Married 12490
## 215 Research Scientist Low Single 2657
## 216 Manager Very High Single 13591
## 217 Sales Executive Low Single 6696
## 218 Research Scientist High Single 2058
## 219 Sales Executive Very High Single 8865
## 220 Sales Executive Low Married 5940
## 221 Laboratory Technician Medium Single 5914
## 222 Research Scientist Medium Married 2622
## 223 Research Director Very High Divorced 12185
## 224 Sales Executive High Divorced 10609
## 225 Manufacturing Director High Married 4345
## 226 Research Scientist Very High Married 2177
## 227 Sales Representative Very High Divorced 2793
## 228 Sales Executive Very High Married 7918
## 229 Sales Executive High Single 8789
## 230 Research Scientist Very High Single 2389
## 231 Laboratory Technician Very High Single 3212
## 232 Manager Very High Married 19232
## 233 Human Resources High Married 2267
## 234 Manager Very High Divorced 19517
## 235 Laboratory Technician Very High Married 2436
## 236 Manager Very High Married 16064
## 237 Laboratory Technician Low Married 2707
## 238 Manager High Single 19068
## 239 Sales Representative Medium Married 3931
## 240 Laboratory Technician High Single 3730
## 241 Laboratory Technician High Divorced 2232
## 242 Sales Executive Very High Married 4465
## 243 Research Scientist Low Divorced 3072
## 244 Research Scientist Very High Divorced 3319
## 245 Manager Very High Married 19202
## 246 Research Director High Divorced 13675
## 247 Research Scientist Very High Married 2911
## 248 Manufacturing Director Low Married 5957
## 249 Research Scientist Low Married 3920
## 250 Manufacturing Director High Married 6434
## 251 Manufacturing Director High Divorced 10048
## 252 Healthcare Representative High Single 10938
## 253 Research Scientist Very High Single 2340
## 254 Research Scientist Low Single 6545
## 255 Sales Executive Very High Divorced 6931
## 256 Manufacturing Director High Married 4898
## 257 Laboratory Technician Low Divorced 2593
## 258 Research Director High Divorced 19436
## 259 Research Scientist Very High Married 2723
## 260 Laboratory Technician Medium Single 3479
## 261 Laboratory Technician Medium Married 2794
## 262 Sales Executive Very High Married 5249
## 263 Laboratory Technician Low Single 2176
## 264 Manager Medium Married 16872
## 265 Laboratory Technician High Single 3485
## 266 Sales Executive Medium Married 6644
## 267 Healthcare Representative Very High Married 5582
## 268 Healthcare Representative Low Divorced 4000
## 269 Healthcare Representative Very High Married 13496
## 270 Laboratory Technician Very High Married 3210
## 271 Manager Low Single 19045
## 272 Manager Medium Married 11849
## 273 Research Scientist Very High Married 2070
## 274 Sales Executive Very High Married 6502
## 275 Research Scientist High Single 3230
## 276 Research Director Very High Divorced 13603
## 277 Manager Medium Divorced 11996
## 278 Sales Executive Low Divorced 5605
## 279 Manufacturing Director Medium Divorced 6397
## 280 Research Director Medium Divorced 19144
## 281 Research Director High Married 17584
## 282 Sales Executive High Married 4907
## 283 Sales Executive Very High Single 4554
## 284 Laboratory Technician Very High Married 5415
## 285 Healthcare Representative Low Married 4741
## 286 Research Scientist Very High Single 2115
## 287 Laboratory Technician High Divorced 3161
## 288 Healthcare Representative Very High Divorced 5745
## 289 Laboratory Technician Medium Divorced 2373
## 290 Research Scientist Very High Single 3310
## 291 Research Director Low Single 18665
## 292 Research Scientist Medium Single 4485
## 293 Sales Representative Medium Divorced 2789
## 294 Sales Executive Very High Single 5828
## 295 Research Scientist Very High Married 2326
## 296 Sales Executive Medium Married 13525
## 297 Laboratory Technician High Single 1420
## 298 Sales Executive Medium Married 8020
## 299 Laboratory Technician Very High Married 3688
## 300 Manufacturing Director Medium Divorced 5482
## 301 Manager Medium Single 16015
## 302 Sales Representative High Single 1200
## 303 Healthcare Representative Low Single 5661
## 304 Sales Executive Very High Married 6929
## 305 Healthcare Representative Very High Divorced 9613
## 306 Laboratory Technician Medium Married 5674
## 307 Sales Executive High Married 5484
## 308 Research Director Medium Married 12061
## 309 Healthcare Representative High Divorced 5660
## 310 Research Scientist Very High Married 4821
## 311 Human Resources Low Married 6410
## 312 Laboratory Technician Low Divorced 5210
## 313 Research Scientist Very High Divorced 2695
## 314 Manager Medium Married 11878
## 315 Manager Low Married 17068
## 316 Laboratory Technician Very High Single 2455
## 317 Healthcare Representative High Single 13964
## 318 Research Scientist Medium Married 4941
## 319 Research Scientist Medium Single 2478
## 320 Sales Executive Medium Married 5228
## 321 Sales Executive High Single 4478
## 322 Sales Executive Very High Divorced 7547
## 323 Research Scientist Very High Single 5055
## 324 Research Scientist Very High Married 3464
## 325 Research Scientist Very High Married 5775
## 326 Manufacturing Director High Married 8943
## 327 Manager Very High Married 19272
## 328 Sales Executive High Married 5238
## 329 Sales Executive Very High Single 4682
## 330 Research Director High Married 18300
## 331 Laboratory Technician High Divorced 5257
## 332 Sales Executive Medium Married 6349
## 333 Research Scientist High Single 4869
## 334 Healthcare Representative Low Married 9985
## 335 Research Scientist Very High Married 3697
## 336 Sales Executive Very High Married 7457
## 337 Laboratory Technician Low Married 2119
## 338 Laboratory Technician Very High Single 3983
## 339 Sales Executive High Divorced 6118
## 340 Sales Executive Medium Married 6214
## 341 Manufacturing Director Very High Divorced 6347
## 342 Research Director Very High Divorced 11510
## 343 Manufacturing Director Very High Single 7143
## 344 Sales Executive Medium Divorced 8268
## 345 Manufacturing Director Medium Single 8095
## 346 Research Scientist Very High Divorced 2904
## 347 Manufacturing Director Medium Single 6032
## 348 Sales Representative High Single 2976
## 349 Research Director Very High Single 15992
## 350 Sales Executive High Married 4649
## 351 Human Resources High Divorced 2696
## 352 Laboratory Technician Medium Married 2370
## 353 Manager High Married 12504
## 354 Research Scientist Low Divorced 5974
## 355 Sales Executive High Married 4736
## 356 Sales Executive High Married 5296
## 357 Healthcare Representative Very High Single 6781
## 358 Sales Representative Medium Single 2174
## 359 Sales Executive Very High Single 6653
## 360 Sales Executive Very High Married 9699
## 361 Healthcare Representative High Married 6755
## 362 Laboratory Technician High Married 2213
## 363 Sales Representative Very High Single 2610
## 364 Sales Representative High Single 2851
## 365 Laboratory Technician Low Married 3452
## 366 Manufacturing Director High Married 5258
## 367 Sales Executive Medium Single 9355
## 368 Healthcare Representative Very High Single 10496
## 369 Sales Executive High Married 6380
## 370 Research Scientist Medium Single 2657
## 371 Sales Representative Medium Single 2716
## 372 Research Scientist Very High Single 2201
## 373 Healthcare Representative Medium Single 6540
## 374 Laboratory Technician Medium Divorced 3816
## 375 Sales Executive Very High Single 5253
## 376 Healthcare Representative High Single 10965
## 377 Sales Executive Very High Married 4936
## 378 Research Scientist High Married 2543
## 379 Sales Executive Very High Single 5304
## 380 Manager Very High Single 16659
## 381 Sales Executive High Divorced 4260
## 382 Sales Representative Medium Married 2476
## 383 Research Scientist Low Single 3102
## 384 Research Scientist Medium Married 2244
## 385 Sales Executive High Married 7596
## 386 Research Scientist Very High Single 2285
## 387 Laboratory Technician Low Divorced 3034
## 388 Sales Executive Medium Divorced 5715
## 389 Laboratory Technician Low Divorced 2576
## 390 Manufacturing Director Medium Single 4197
## 391 Research Director Medium Divorced 14336
## 392 Laboratory Technician High Married 3448
## 393 Research Director Low Married 19406
## 394 Sales Executive High Married 6538
## 395 Manufacturing Director Low Married 4306
## 396 Laboratory Technician Very High Married 2258
## 397 Healthcare Representative High Divorced 4522
## 398 Sales Executive Very High Single 4487
## 399 Research Scientist High Married 4449
## 400 Laboratory Technician Low Married 2218
## 401 Manager High Divorced 19197
## 402 Sales Executive Low Married 13212
## 403 Sales Executive High Single 6577
## 404 Sales Executive Low Married 8392
## 405 Laboratory Technician Low Divorced 4558
## 406 Laboratory Technician Low Married 4031
## 407 Manufacturing Director High Married 7969
## 408 Research Scientist Very High Married 2654
## 409 Manager Very High Married 16555
## 410 Research Scientist High Divorced 4556
## 411 Manufacturing Director Very High Single 6091
## 412 Manager Low Married 19566
## 413 Manufacturing Director High Divorced 4810
## 414 Healthcare Representative Very High Married 4523
## 415 Sales Representative Medium Single 3202
## 416 Sales Representative High Divorced 2351
## 417 Laboratory Technician Very High Married 1702
## 418 Manager High Married 18041
## 419 Research Scientist Very High Divorced 2886
## 420 Laboratory Technician Very High Married 2097
## 421 Research Director High Married 11935
## 422 Research Scientist Medium Married 2546
## 423 Human Resources Very High Single 2564
## 424 Sales Executive Low Married 8412
## 425 Manager Very High Divorced 14118
## 426 Manager High Married 17046
## 427 Laboratory Technician Very High Single 2564
## 428 Sales Executive Low Married 10266
## 429 Manufacturing Director Very High Divorced 5070
## 430 Research Director High Married 17861
## 431 Laboratory Technician High Single 4230
## 432 Laboratory Technician High Single 3780
## 433 Research Scientist High Divorced 2768
## 434 Sales Executive Very High Married 9071
## 435 Manufacturing Director Medium Divorced 10648
## 436 Manager High Married 13610
## 437 Laboratory Technician Very High Divorced 3408
## 438 Sales Representative Medium Single 2983
## 439 Healthcare Representative High Married 7632
## 440 Healthcare Representative High Married 9824
## 441 Human Resources Low Divorced 9950
## 442 Laboratory Technician High Married 2093
## 443 Sales Executive Very High Single 9980
## 444 Laboratory Technician High Single 3894
## 445 Sales Executive Very High Married 4051
## 446 Manager Medium Single 16835
## 447 Sales Executive Very High Single 6230
## 448 Sales Executive High Married 4717
## 449 Manufacturing Director High Single 13237
## 450 Laboratory Technician High Married 3755
## 451 Sales Executive Very High Single 6582
## 452 Manufacturing Director Low Married 7406
## 453 Sales Executive Medium Married 4805
## 454 Human Resources High Divorced 2741
## 455 Manufacturing Director Very High Divorced 4262
## 456 Research Director High Divorced 16184
## 457 Manager Very High Divorced 11557
## 458 Sales Representative Medium Single 1878
## 459 Sales Executive Low Divorced 10932
## 460 Healthcare Representative High Single 6811
## 461 Sales Executive High Divorced 4306
## 462 Sales Executive High Single 4859
## 463 Sales Executive Very High Single 5337
## 464 Laboratory Technician Very High Single 2340
## 465 Manufacturing Director Very High Single 7491
## 466 Healthcare Representative High Married 10527
## 467 Manager Low Married 16595
## 468 Sales Executive Medium Divorced 8834
## 469 Research Scientist Low Divorced 5577
## 470 Sales Executive High Married 4707
## 471 Sales Representative Very High Married 2400
## 472 Healthcare Representative High Married 9824
## 473 Manufacturing Director Medium Married 6447
## 474 Research Director High Divorced 19502
## 475 Research Scientist Very High Married 2725
## 476 Sales Executive Medium Married 6272
## 477 Laboratory Technician Medium Married 2127
## 478 Manager Medium Married 18200
## 479 Sales Representative High Married 2096
## 480 Laboratory Technician High Married 2886
## 481 Sales Representative Low Married 2033
## 482 Research Scientist Very High Married 3622
## 483 Sales Executive Low Divorced 4233
## 484 Laboratory Technician Very High Single 3681
## 485 Sales Executive Very High Divorced 5460
## 486 Research Scientist High Divorced 2187
## 487 Sales Executive High Married 9602
## 488 Research Scientist Medium Single 2836
## 489 Healthcare Representative Very High Married 4089
## 490 Research Director Very High Divorced 16627
## 491 Research Scientist Low Single 2619
## 492 Laboratory Technician High Divorced 5679
## 493 Manager Low Married 15402
## 494 Human Resources High Single 5985
## 495 Sales Representative High Divorced 2579
## 496 Sales Representative Low Divorced 3041
## 497 Sales Representative High Single 3447
## 498 Manager Very High Married 19513
## 499 Research Scientist High Married 2773
## 500 Sales Executive High Divorced 7104
## 501 Research Scientist Very High Married 6322
## 502 Research Scientist High Divorced 2083
## 503 Sales Executive Low Single 8381
## 504 Research Scientist Very High Married 2691
## 505 Sales Executive Low Married 4286
## 506 Laboratory Technician Very High Married 2659
## 507 Manufacturing Director High Married 9434
## 508 Sales Executive High Married 5561
## 509 Research Scientist Very High Single 6646
## 510 Healthcare Representative Very High Divorced 7725
## 511 Human Resources Medium Married 10725
## 512 Manufacturing Director Medium Divorced 8847
## 513 Research Scientist Very High Single 2045
## 514 Research Scientist High Single 1009
## 515 Research Scientist Low Single 3348
## 516 Laboratory Technician High Married 1281
## 517 Research Scientist Low Married 2819
## 518 Sales Executive Medium Married 4851
## 519 Sales Executive Very High Single 4028
## 520 Research Scientist Very High Divorced 2720
## 521 Sales Executive Medium Married 8120
## 522 Sales Executive Very High Divorced 4647
## 523 Research Scientist Very High Single 4680
## 524 Laboratory Technician High Married 3221
## 525 Healthcare Representative Medium Single 8621
## 526 Sales Executive High Single 4577
## 527 Healthcare Representative High Single 4553
## 528 Sales Executive Very High Single 5396
## 529 Sales Executive High Married 6796
## 530 Healthcare Representative Very High Single 7625
## 531 Manufacturing Director Low Married 7412
## 532 Research Director Very High Single 11159
## 533 Sales Executive Low Single 4960
## 534 Sales Executive Low Married 10475
## 535 Research Director High Married 14814
## 536 Manager Very High Divorced 19141
## 537 Sales Executive Low Single 5405
## 538 Manufacturing Director Low Divorced 8793
## 539 Manager High Married 19189
## 540 Sales Representative Medium Married 3875
## 541 Research Scientist Medium Single 2216
## 542 Research Director Low Married 11713
## 543 Manufacturing Director High Single 7861
## 544 Laboratory Technician High Single 3708
## 545 Sales Executive High Divorced 13770
## 546 Sales Executive Very High Divorced 5304
## 547 Sales Representative High Single 2642
## 548 Research Scientist High Divorced 2759
## 549 Sales Executive Very High Married 6804
## 550 Healthcare Representative High Single 6142
## 551 Laboratory Technician Low Married 2500
## 552 Human Resources Medium Married 6389
## 553 Healthcare Representative Very High Married 11103
## 554 Research Scientist Very High Single 2342
## 555 Healthcare Representative Low Single 6811
## 556 Sales Representative Medium Divorced 2297
## 557 Laboratory Technician Very High Single 2450
## 558 Healthcare Representative Low Divorced 5093
## 559 Laboratory Technician Very High Married 5309
## 560 Research Scientist High Married 3057
## 561 Manufacturing Director Low Divorced 5121
## 562 Manager Low Married 16856
## 563 Research Scientist Very High Single 2686
## 564 Sales Executive Very High Single 6180
## 565 Sales Representative High Single 6632
## 566 Research Scientist High Single 3505
## 567 Sales Executive High Single 6397
## 568 Sales Executive Very High Single 6274
## 569 Manager Low Married 19859
## 570 Sales Executive Low Single 7587
## 571 Research Scientist Very High Married 4258
## 572 Laboratory Technician Very High Divorced 4364
## 573 Healthcare Representative High Married 4335
## 574 Sales Executive Low Single 5326
## 575 Research Scientist Very High Single 3280
## 576 Manufacturing Director Low Divorced 5485
## 577 Sales Executive Very High Married 4342
## 578 Research Scientist Low Divorced 2782
## 579 Manufacturing Director Low Single 5980
## 580 Research Scientist Low Single 4381
## 581 Sales Representative Very High Married 2572
## 582 Laboratory Technician High Married 3833
## 583 Healthcare Representative Medium Married 4244
## 584 Sales Executive Low Married 6500
## 585 Manager Very High Divorced 18430
## 586 Laboratory Technician Low Married 1601
## 587 Laboratory Technician Medium Divorced 2694
## 588 Laboratory Technician High Married 3149
## 589 Research Director High Married 17639
## 590 Laboratory Technician Low Married 2319
## 591 Research Director High Married 11691
## 592 Sales Executive Low Single 5324
## 593 Manager Very High Married 16752
## 594 Manufacturing Director Medium Married 5228
## 595 Research Scientist High Married 2700
## 596 Research Director Medium Single 19246
## 597 Research Scientist High Single 2506
## 598 Manufacturing Director Very High Married 6062
## 599 Research Scientist High Single 4382
## 600 Human Resources Medium Married 2143
## 601 Manufacturing Director High Married 6162
## 602 Laboratory Technician High Single 5094
## 603 Manufacturing Director Very High Single 6877
## 604 Research Scientist High Single 2274
## 605 Manufacturing Director Medium Married 4434
## 606 Healthcare Representative Low Divorced 6288
## 607 Research Scientist Very High Single 2553
## 608 Sales Executive Very High Married 7654
## 609 Sales Executive Very High Single 5160
## 610 Research Director Low Married 17159
## 611 Research Director Very High Divorced 12808
## 612 Manufacturing Director High Single 10221
## 613 Sales Executive Medium Married 4779
## 614 Human Resources Very High Married 3737
## 615 Research Scientist High Married 2366
## 616 Research Scientist Very High Married 1706
## 617 Manager High Married 16307
## 618 Healthcare Representative Medium Single 5933
## 619 Research Scientist Low Single 3424
## 620 Sales Executive Low Divorced 4037
## 621 Research Scientist Low Single 2559
## 622 Sales Executive Very High Married 6201
## 623 Sales Executive Very High Divorced 4403
## 624 Research Scientist Very High Divorced 3761
## 625 Sales Executive Very High Married 10934
## 626 Sales Executive Low Divorced 10761
## 627 Research Scientist High Married 5175
## 628 Manufacturing Director Very High Married 13826
## 629 Sales Executive High Divorced 6334
## 630 Human Resources Very High Divorced 4936
## 631 Manufacturing Director Very High Married 4775
## 632 Laboratory Technician Very High Married 2818
## 633 Research Scientist Very High Single 2515
## 634 Human Resources Low Married 2342
## 635 Sales Executive Low Married 4194
## 636 Manufacturing Director High Married 10685
## 637 Research Scientist Medium Divorced 2022
## 638 Laboratory Technician Very High Divorced 2314
## 639 Sales Executive Low Married 4256
## 640 Research Scientist Low Married 3580
## 641 Laboratory Technician Very High Married 3162
## 642 Sales Executive Medium Married 6524
## 643 Sales Representative Medium Married 2899
## 644 Laboratory Technician Very High Married 5231
## 645 Research Scientist Very High Married 2356
## 646 Sales Representative High Divorced 2800
## 647 Sales Executive Very High Married 11836
## 648 Manufacturing Director Medium Married 10903
## 649 Sales Representative Very High Married 2973
## 650 Research Director Very High Single 14275
## 651 Healthcare Representative Very High Married 5562
## 652 Sales Executive Very High Married 4537
## 653 Sales Executive Medium Single 7642
## 654 Manager Low Divorced 17924
## 655 Human Resources Very High Married 5204
## 656 Human Resources Medium Divorced 2277
## 657 Laboratory Technician Very High Single 2795
## 658 Laboratory Technician Very High Divorced 2532
## 659 Research Scientist Low Married 2559
## 660 Sales Executive Very High Single 4908
## 661 Laboratory Technician Very High Divorced 2380
## 662 Manufacturing Director Medium Divorced 4765
## 663 Sales Representative High Single 2044
## 664 Research Scientist Very High Single 2693
## 665 Healthcare Representative Very High Married 6586
## 666 Sales Representative Very High Single 3294
## 667 Manufacturing Director High Married 4171
## 668 Laboratory Technician Very High Divorced 2778
## 669 Research Scientist High Divorced 2377
## 670 Laboratory Technician Low Married 2404
## 671 Research Scientist High Single 2318
## 672 Laboratory Technician Medium Divorced 2008
## 673 Sales Executive High Single 6244
## 674 Research Scientist Low Single 2799
## 675 Healthcare Representative Medium Divorced 10552
## 676 Sales Representative High Married 2329
## 677 Healthcare Representative Very High Married 4014
## 678 Laboratory Technician Medium Married 7403
## 679 Research Scientist High Married 2259
## 680 Sales Executive High Married 6932
## 681 Research Scientist Very High Single 4678
## 682 Research Director Low Married 13582
## 683 Laboratory Technician Medium Married 2332
## 684 Sales Representative Medium Married 2413
## 685 Sales Executive Medium Divorced 9705
## 686 Sales Executive Low Single 4294
## 687 Laboratory Technician Low Single 4721
## 688 Laboratory Technician High Single 2519
## 689 Sales Representative Medium Single 2121
## 690 Laboratory Technician Low Single 2973
## 691 Healthcare Representative Very High Married 5855
## 692 Research Scientist Medium Divorced 3617
## 693 Manufacturing Director Low Married 6725
## 694 Sales Executive Very High Married 10325
## 695 Healthcare Representative Very High Single 6949
## 696 Sales Executive High Married 10609
## 697 Laboratory Technician Medium Married 4447
## 698 Sales Representative Very High Married 2157
## 699 Sales Executive High Married 4601
## 700 Manager Very High Married 17099
## 701 Research Scientist High Single 2479
## 702 Manager High Divorced 14852
## 703 Sales Executive High Divorced 7264
## 704 Sales Executive Very High Single 5666
## 705 Sales Executive Very High Divorced 7823
## 706 Sales Executive High Single 7880
## 707 Sales Executive Medium Single 13194
## 708 Manufacturing Director High Divorced 5067
## 709 Sales Executive Very High Divorced 5079
## 710 Research Scientist Low Single 2321
## 711 Manager High Single 17444
## 712 Research Scientist Low Single 2404
## 713 Research Scientist Very High Single 3452
## 714 Laboratory Technician Very High Divorced 2270
## 715 Research Director Very High Divorced 17399
## 716 Healthcare Representative Medium Married 5488
## 717 Research Director High Divorced 19419
## 718 Laboratory Technician Medium Married 2811
## 719 Laboratory Technician Low Married 3633
## 720 Sales Executive Very High Single 4163
## 721 Research Scientist High Married 2132
## 722 Manufacturing Director High Married 13973
## 723 Research Scientist High Married 2684
## 724 Manufacturing Director High Divorced 10845
## 725 Manufacturing Director High Divorced 4377
## 726 Laboratory Technician Medium Divorced 3743
## 727 Manufacturing Director Low Married 4148
## 728 Research Scientist Very High Single 1051
## 729 Manufacturing Director High Married 10739
## 730 Healthcare Representative High Divorced 10388
## 731 Research Director Low Married 11416
## 732 Research Scientist Low Single 2600
## 733 Laboratory Technician Medium Single 2422
## 734 Manufacturing Director Very High Married 5472
## 735 Laboratory Technician Low Married 2451
## 736 Healthcare Representative High Single 4240
## 737 Healthcare Representative High Single 10999
## 738 Manufacturing Director High Single 5003
## 739 Manufacturing Director Very High Married 12742
## 740 Manufacturing Director Very High Married 4227
## 741 Laboratory Technician Very High Divorced 3917
## 742 Manager High Married 18303
## 743 Laboratory Technician Very High Married 2380
## 744 Manufacturing Director Very High Single 13726
## 745 Healthcare Representative Medium Married 4777
## 746 Healthcare Representative Medium Married 6385
## 747 Research Director High Divorced 19973
## 748 Sales Executive Very High Single 6861
## 749 Sales Executive Low Single 4969
## 750 Manager Very High Married 19845
## 751 Sales Executive Very High Married 13320
## 752 Sales Executive High Married 6347
## 753 Laboratory Technician Low Single 2743
## 754 Manufacturing Director Low Single 10880
## 755 Sales Representative Very High Single 2342
## 756 Manager Very High Married 17650
## 757 Laboratory Technician High Single 4025
## 758 Sales Executive Very High Divorced 9725
## 759 Manager Very High Married 11904
## 760 Human Resources Medium Single 2177
## 761 Sales Executive Medium Married 7525
## 762 Laboratory Technician High Divorced 4834
## 763 Research Scientist Low Married 2042
## 764 Sales Representative High Married 2220
## 765 Sales Representative Medium Married 1052
## 766 Research Scientist High Married 2821
## 767 Research Director High Married 19237
## 768 Healthcare Representative Medium Single 4107
## 769 Sales Executive Low Married 8396
## 770 Research Scientist High Divorced 2007
## 771 Research Director Very High Divorced 19627
## 772 Sales Executive High Married 10686
## 773 Research Scientist High Married 2942
## 774 Manufacturing Director Very High Single 8858
## 775 Manager Low Single 16756
## 776 Sales Executive Very High Divorced 10798
## 777 Sales Representative Very High Single 2323
## 778 Laboratory Technician Low Single 1416
## 779 Research Scientist Low Divorced 4615
## 780 Research Scientist High Married 2461
## 781 Healthcare Representative Low Single 8722
## 782 Laboratory Technician Low Married 3955
## 783 Manufacturing Director Low Married 9957
## 784 Research Scientist High Married 3376
## 785 Healthcare Representative High Married 8823
## 786 Healthcare Representative Very High Married 10322
## 787 Laboratory Technician High Married 4621
## 788 Manufacturing Director Medium Married 10976
## 789 Research Scientist High Single 3660
## 790 Human Resources Low Married 10482
## 791 Healthcare Representative Very High Divorced 7119
## 792 Sales Executive Low Single 9582
## 793 Research Scientist High Single 4508
## 794 Laboratory Technician High Divorced 2207
## 795 Healthcare Representative Very High Single 7756
## 796 Sales Executive Very High Divorced 6694
## 797 Laboratory Technician Very High Married 3691
## 798 Laboratory Technician High Divorced 2377
## 799 Research Scientist Medium Single 2313
## 800 Manager Low Married 17665
## 801 Laboratory Technician Medium Divorced 2596
## 802 Sales Executive High Single 4728
## 803 Sales Executive Medium Married 4302
## 804 Research Scientist Very High Married 2979
## 805 Manager Very High Single 16885
## 806 Sales Executive High Married 5593
## 807 Healthcare Representative Medium Single 10445
## 808 Sales Executive High Divorced 8740
## 809 Research Scientist Very High Divorced 2514
## 810 Manufacturing Director Medium Divorced 7655
## 811 Manager High Married 17465
## 812 Sales Executive Medium Single 7351
## 813 Manufacturing Director Low Married 10820
## 814 Healthcare Representative Very High Divorced 12169
## 815 Research Director High Single 19626
## 816 Research Scientist Medium Single 2070
## 817 Laboratory Technician Medium Single 6782
## 818 Manufacturing Director Very High Single 7779
## 819 Sales Representative Very High Married 2791
## 820 Research Scientist Medium Married 3201
## 821 Sales Executive Very High Divorced 4968
## 822 Sales Executive Medium Married 13120
## 823 Manufacturing Director High Single 4033
## 824 Research Scientist Medium Divorced 3291
## 825 Laboratory Technician Very High Single 4272
## 826 Manufacturing Director Very High Married 5056
## 827 Human Resources High Married 2844
## 828 Research Scientist High Divorced 2703
## 829 Laboratory Technician High Single 1904
## 830 Sales Executive Low Single 8224
## 831 Laboratory Technician Very High Married 4766
## 832 Laboratory Technician High Married 2610
## 833 Healthcare Representative Very High Divorced 5731
## 834 Research Scientist High Married 2539
## 835 Sales Executive High Married 5714
## 836 Human Resources High Single 4323
## 837 Sales Executive Low Married 7336
## 838 Research Director High Single 13499
## 839 Sales Executive Low Single 13758
## 840 Sales Executive Low Single 5155
## 841 Laboratory Technician High Married 2258
## 842 Laboratory Technician Medium Single 3597
## 843 Laboratory Technician Very High Married 2515
## 844 Laboratory Technician Very High Married 4420
## 845 Sales Executive High Married 6578
## 846 Research Scientist Very High Married 4422
## 847 Manufacturing Director Medium Divorced 10274
## 848 Healthcare Representative Low Single 5343
## 849 Laboratory Technician Very High Married 2376
## 850 Sales Executive High Single 5346
## 851 Sales Representative Low Divorced 2827
## 852 Manager Low Divorced 19943
## 853 Laboratory Technician Very High Married 3131
## 854 Research Scientist Low Single 2552
## 855 Research Scientist High Married 4477
## 856 Manufacturing Director Very High Married 6474
## 857 Laboratory Technician High Single 3033
## 858 Research Scientist High Single 2936
## 859 Manager High Divorced 18606
## 860 Research Scientist Very High Married 2168
## 861 Research Scientist Very High Married 2853
## 862 Manager Low Married 17048
## 863 Research Scientist High Single 2290
## 864 Human Resources High Married 3600
## 865 Research Scientist Low Divorced 2107
## 866 Sales Executive Low Divorced 4115
## 867 Sales Executive Medium Married 4327
## 868 Manager Low Married 17856
## 869 Laboratory Technician Low Married 3196
## 870 Research Director Medium Married 19081
## 871 Sales Executive Low Married 8966
## 872 Laboratory Technician Medium Married 2210
## 873 Sales Executive High Married 4539
## 874 Laboratory Technician High Divorced 2741
## 875 Laboratory Technician High Divorced 3491
## 876 Research Scientist Very High Single 4541
## 877 Sales Representative Very High Single 2678
## 878 Manufacturing Director Very High Divorced 7379
## 879 Human Resources Low Married 6272
## 880 Sales Executive Very High Divorced 5220
## 881 Laboratory Technician Medium Married 2743
## 882 Research Scientist High Single 4998
## 883 Manufacturing Director Low Divorced 10252
## 884 Research Scientist Very High Married 2781
## 885 Sales Executive Medium Divorced 6852
## 886 Sales Executive Very High Single 4950
## 887 Research Scientist Medium Married 3579
## 888 Research Director Low Married 13191
## 889 Sales Executive Very High Married 10377
## 890 Research Scientist Low Married 2235
## 891 Manufacturing Director High Divorced 10502
## 892 Research Scientist Very High Married 2011
## 893 Research Scientist Medium Single 1859
## 894 Research Scientist Very High Divorced 3760
## 895 Research Director Very High Married 17779
## 896 Healthcare Representative Low Married 6833
## 897 Healthcare Representative Low Single 6812
## 898 Sales Executive Very High Single 5171
## 899 Research Director Very High Married 19740
## 900 Manager High Married 18711
## 901 Research Scientist Medium Married 3692
## 902 Laboratory Technician Medium Single 2559
## 903 Research Scientist High Divorced 2517
## 904 Healthcare Representative Very High Divorced 6623
## 905 Research Director Very High Single 18265
## 906 Research Director Very High Divorced 16124
## 907 Research Scientist High Married 2585
## 908 Manager Medium Married 18213
## 909 Sales Executive High Divorced 8380
## 910 Research Scientist Very High Single 2994
## 911 Research Scientist High Married 1223
## 912 Sales Representative Very High Single 1118
## 913 Research Scientist Very High Single 2875
## 914 Manager Medium Single 18824
## 915 Healthcare Representative Medium Divorced 13577
## 916 Laboratory Technician High Single 2625
## 917 Manager Medium Married 18789
## 918 Sales Executive Low Single 4538
## 919 Manager Medium Divorced 19847
## 920 Manufacturing Director Very High Single 10512
## 921 Laboratory Technician Medium Divorced 4444
## 922 Laboratory Technician High Single 2154
## 923 Manager Low Divorced 19190
## 924 Human Resources Medium Married 4490
## 925 Research Scientist High Married 3506
## 926 Research Scientist Medium Married 2372
## 927 Sales Executive Very High Single 10231
## 928 Manufacturing Director Medium Single 5410
## 929 Healthcare Representative Very High Married 7978
## 930 Laboratory Technician Very High Married 3867
## 931 Laboratory Technician High Single 2838
## 932 Manufacturing Director High Single 4695
## 933 Laboratory Technician High Divorced 3339
## 934 Research Scientist Low Single 2080
## 935 Research Scientist Medium Single 2096
## 936 Sales Executive Very High Married 6209
## 937 Manager Medium Single 18061
## 938 Manager Medium Divorced 17123
## 939 Research Scientist High Divorced 2372
## 940 Laboratory Technician High Married 4883
## 941 Research Scientist Low Single 3904
## 942 Laboratory Technician Very High Married 4627
## 943 Healthcare Representative High Married 7094
## 944 Human Resources Low Single 3423
## 945 Laboratory Technician Very High Married 6674
## 946 Research Director Low Married 16880
## 947 Sales Executive Medium Single 9094
## 948 Sales Executive Medium Single 8446
## 949 Manager Low Married 11916
## 950 Manufacturing Director High Single 4534
## 951 Sales Executive High Divorced 9852
## 952 Sales Executive Medium Single 6151
## 953 Sales Representative Medium Single 2302
## 954 Laboratory Technician Low Married 2362
## 955 Manager High Married 17861
## 956 Manager High Married 19187
## 957 Manager Medium Single 19717
## 958 Research Scientist High Divorced 3544
## 959 Healthcare Representative Very High Divorced 8500
## 960 Research Scientist Very High Single 4661
## 961 Sales Executive Low Divorced 4103
## 962 Research Scientist High Single 4249
## 963 Manager Medium Divorced 14026
## 964 Sales Executive Low Divorced 6893
## 965 Sales Executive Low Single 6125
## 966 Laboratory Technician Very High Married 3669
## 967 Manufacturing Director Low Married 10008
## 968 Laboratory Technician Medium Married 2387
## 969 Sales Executive Low Married 4639
## 970 Manufacturing Director Very High Single 7898
## 971 Sales Representative Very High Married 2534
## 972 Manufacturing Director Medium Single 13142
## 973 Laboratory Technician Very High Single 1611
## 974 Laboratory Technician Very High Married 5363
## 975 Sales Executive Very High Single 5071
## 976 Sales Executive High Single 13695
## 977 Manufacturing Director Medium Married 13402
## 978 Research Scientist High Divorced 2029
## 979 Healthcare Representative High Divorced 6377
## 980 Laboratory Technician High Married 5429
## 981 Sales Representative Very High Single 2785
## 982 Sales Executive High Married 4614
## 983 Research Scientist High Divorced 2610
## 984 Healthcare Representative Very High Single 6687
## 985 Sales Executive Low Married 4724
## 986 Manufacturing Director High Married 6179
## 987 Sales Executive Very High Married 6120
## 988 Sales Executive Medium Married 10596
## 989 Research Scientist Very High Divorced 5467
## 990 Research Scientist High Married 2996
## 991 Sales Executive Very High Married 9998
## 992 Sales Executive Medium Married 4078
## 993 Healthcare Representative High Married 10920
## 994 Sales Executive High Married 6232
## 995 Manufacturing Director High Married 13247
## 996 Research Scientist High Single 4081
## 997 Sales Executive Very High Married 5769
## 998 Research Scientist High Single 2394
## 999 Research Scientist Very High Single 3904
## 1000 Manager Low Married 16799
## 1001 Laboratory Technician Low Married 2950
## 1002 Laboratory Technician High Single 3629
## 1003 Manufacturing Director Very High Single 9362
## 1004 Laboratory Technician Very High Married 3229
## 1005 Laboratory Technician Low Single 3578
## 1006 Human Resources Low Single 7988
## 1007 Laboratory Technician Low Single 4284
## 1008 Healthcare Representative Very High Single 7553
## 1009 Research Director Very High Single 17328
## 1010 Research Director Low Married 19701
## 1011 Research Director Very High Divorced 14732
## 1012 Sales Executive Medium Single 9278
## 1013 Sales Representative High Single 1359
## 1014 Sales Executive Low Divorced 4779
## 1015 Research Director Medium Single 16422
## 1016 Research Scientist Low Divorced 2996
## 1017 Research Scientist Medium Single 1261
## 1018 Laboratory Technician Low Married 2099
## 1019 Laboratory Technician Very High Single 5810
## 1020 Sales Executive Low Married 5647
## 1021 Research Scientist Very High Married 3420
## 1022 Sales Representative Low Married 4400
## 1023 Laboratory Technician High Single 3500
## 1024 Research Scientist Low Married 2066
## 1025 Research Director High Married 17169
## 1026 Sales Executive High Married 4162
## 1027 Sales Executive Very High Married 9204
## 1028 Laboratory Technician Medium Married 3294
## 1029 Research Scientist High Married 2127
## 1030 Laboratory Technician High Divorced 3975
## 1031 Sales Executive Very High Divorced 10793
## 1032 Sales Executive Very High Divorced 10096
## 1033 Laboratory Technician Low Single 3646
## 1034 Manufacturing Director Medium Single 7446
## 1035 Healthcare Representative Low Divorced 10851
## 1036 Human Resources Medium Single 2109
## 1037 Laboratory Technician Very High Married 3722
## 1038 Manufacturing Director Very High Married 9380
## 1039 Sales Executive Low Divorced 5486
## 1040 Human Resources High Married 2742
## 1041 Research Director Medium Divorced 13757
## 1042 Sales Executive Low Single 8463
## 1043 Laboratory Technician High Single 3162
## 1044 Research Director Medium Single 16598
## 1045 Healthcare Representative High Married 6651
## 1046 Research Scientist High Divorced 2345
## 1047 Research Scientist Medium Single 3420
## 1048 Sales Executive Low Married 4373
## 1049 Sales Executive Low Single 4759
## 1050 Sales Executive High Married 5301
## 1051 Laboratory Technician Very High Single 3673
## 1052 Sales Executive High Married 4768
## 1053 Research Scientist High Divorced 1274
## 1054 Research Scientist High Married 4900
## 1055 Healthcare Representative Medium Divorced 10466
## 1056 Research Director Low Divorced 17007
## 1057 Sales Representative High Married 2909
## 1058 Sales Executive Medium Single 5765
## 1059 Sales Executive Medium Single 4599
## 1060 Sales Representative High Married 2404
## 1061 Laboratory Technician Low Single 3172
## 1062 Sales Representative Medium Married 2033
## 1063 Manufacturing Director High Single 10209
## 1064 Sales Executive High Divorced 8620
## 1065 Human Resources High Divorced 2064
## 1066 Healthcare Representative High Married 4035
## 1067 Laboratory Technician Medium Married 3838
## 1068 Sales Executive High Married 4591
## 1069 Laboratory Technician Low Single 2561
## 1070 Research Scientist High Divorced 1563
## 1071 Sales Executive Low Single 4898
## 1072 Laboratory Technician Low Married 4789
## 1073 Laboratory Technician Medium Married 3180
## 1074 Manufacturing Director Medium Married 6549
## 1075 Healthcare Representative High Single 6388
## 1076 Manager Very High Single 11244
## 1077 Manager Very High Divorced 16032
## 1078 Research Scientist Low Single 2362
## 1079 Research Director Low Married 16328
## 1080 Manufacturing Director Medium Single 8376
## 1081 Manager Medium Married 16606
## 1082 Healthcare Representative Medium Single 8606
## 1083 Laboratory Technician High Single 2272
## 1084 Laboratory Technician Low Single 2018
## 1085 Sales Executive High Married 7083
## 1086 Research Scientist High Single 4084
## 1087 Research Director Very High Single 14411
## 1088 Sales Representative High Married 2308
## 1089 Laboratory Technician Medium Married 4841
## 1090 Research Scientist Very High Married 4285
## 1091 Healthcare Representative Low Married 9715
## 1092 Manufacturing Director Medium Single 4320
## 1093 Research Scientist Very High Married 2132
## 1094 Healthcare Representative Very High Married 10124
## 1095 Sales Executive Low Married 5473
## 1096 Laboratory Technician High Married 5207
## 1097 Manager Very High Single 16437
## 1098 Laboratory Technician Low Divorced 2296
## 1099 Healthcare Representative Very High Divorced 4069
## 1100 Healthcare Representative Medium Divorced 7441
## 1101 Sales Representative High Married 2430
## 1102 Research Scientist Medium Married 5878
## 1103 Sales Representative Very High Single 2644
## 1104 Sales Executive High Divorced 6439
## 1105 Research Scientist High Married 2451
## 1106 Sales Executive Low Married 6392
## 1107 Sales Executive Low Married 9714
## 1108 Human Resources High Married 6077
## 1109 Laboratory Technician Low Single 2450
## 1110 Sales Executive Medium Married 9250
## 1111 Laboratory Technician Low Divorced 2074
## 1112 Manufacturing Director Very High Married 10169
## 1113 Manufacturing Director Medium Married 4855
## 1114 Research Scientist Low Married 4087
## 1115 Research Scientist Low Married 2367
## 1116 Research Scientist Very High Single 2972
## 1117 Manager Very High Married 19586
## 1118 Research Scientist Very High Married 5484
## 1119 Research Scientist Very High Married 2061
## 1120 Sales Executive Medium Married 9924
## 1121 Sales Executive Medium Single 4198
## 1122 Sales Executive High Single 6815
## 1123 Laboratory Technician Low Single 4723
## 1124 Healthcare Representative High Single 6142
## 1125 Sales Executive High Married 8237
## 1126 Healthcare Representative Very High Divorced 8853
## 1127 Manager High Married 19331
## 1128 Research Scientist High Married 2073
## 1129 Laboratory Technician Low Married 5562
## 1130 Manager Very High Single 19613
## 1131 Laboratory Technician High Married 3407
## 1132 Healthcare Representative High Married 5063
## 1133 Sales Executive Low Married 4639
## 1134 Laboratory Technician Medium Divorced 4876
## 1135 Laboratory Technician Very High Married 2690
## 1136 Manager Low Single 17567
## 1137 Laboratory Technician Medium Married 2408
## 1138 Research Scientist High Married 2814
## 1139 Healthcare Representative High Married 11245
## 1140 Research Scientist Very High Married 3312
## 1141 Research Director Very High Divorced 19049
## 1142 Research Scientist Medium Married 2141
## 1143 Laboratory Technician Low Single 5769
## 1144 Sales Executive Low Married 4385
## 1145 Sales Executive Low Single 5332
## 1146 Manufacturing Director High Married 4663
## 1147 Manufacturing Director Very High Divorced 4724
## 1148 Laboratory Technician Low Married 3211
## 1149 Manufacturing Director Low Married 5377
## 1150 Laboratory Technician Low Divorced 4066
## 1151 Research Scientist Low Married 5208
## 1152 Manufacturing Director Low Divorced 4877
## 1153 Research Scientist Very High Single 3117
## 1154 Sales Representative Very High Single 1569
## 1155 Manager High Married 19658
## 1156 Laboratory Technician High Divorced 3069
## 1157 Manufacturing Director High Married 10435
## 1158 Healthcare Representative High Married 4148
## 1159 Manufacturing Director High Married 5768
## 1160 Manufacturing Director High Single 5042
## 1161 Manufacturing Director Very High Divorced 5770
## 1162 Manufacturing Director High Married 7756
## 1163 Sales Executive Low Married 10306
## 1164 Research Scientist Medium Married 3936
## 1165 Manufacturing Director Very High Single 7945
## 1166 Human Resources Very High Married 5743
## 1167 Manager Very High Married 15202
## 1168 Sales Executive Very High Divorced 5440
## 1169 Research Scientist Very High Single 3760
## 1170 Research Scientist High Married 3517
## 1171 Research Scientist Very High Single 2580
## 1172 Laboratory Technician Low Single 2166
## 1173 Sales Executive High Single 5869
## 1174 Healthcare Representative Low Married 8008
## 1175 Manufacturing Director High Divorced 5206
## 1176 Manufacturing Director Medium Married 5295
## 1177 Research Director Medium Married 16413
## 1178 Research Director Low Divorced 13269
## 1179 Sales Representative High Single 2783
## 1180 Research Scientist Medium Divorced 5433
## 1181 Laboratory Technician Medium Single 2013
## 1182 Healthcare Representative High Married 13966
## 1183 Manufacturing Director High Married 4374
## 1184 Healthcare Representative Low Divorced 6842
## 1185 Manager High Married 17426
## 1186 Research Director High Married 17603
## 1187 Sales Executive Very High Single 4581
## 1188 Research Scientist Very High Married 4735
## 1189 Sales Executive Medium Divorced 4187
## 1190 Sales Executive Very High Divorced 5505
## 1191 Research Scientist Medium Divorced 5470
## 1192 Sales Executive Very High Married 5476
## 1193 Laboratory Technician Low Divorced 2587
## 1194 Laboratory Technician Medium Single 2440
## 1195 Manager Medium Divorced 15972
## 1196 Manager High Single 15379
## 1197 Sales Executive High Single 7082
## 1198 Sales Representative Low Single 2728
## 1199 Sales Executive Very High Divorced 5368
## 1200 Healthcare Representative High Married 5347
## 1201 Human Resources Very High Divorced 3195
## 1202 Laboratory Technician High Single 3989
## 1203 Laboratory Technician High Married 3306
## 1204 Healthcare Representative Very High Married 7005
## 1205 Sales Representative High Married 2655
## 1206 Laboratory Technician Medium Single 1393
## 1207 Laboratory Technician Very High Single 2570
## 1208 Research Scientist Medium Divorced 3537
## 1209 Laboratory Technician Very High Married 3986
## 1210 Healthcare Representative Very High Divorced 10883
## 1211 Laboratory Technician Medium Married 2028
## 1212 Sales Executive Very High Divorced 9525
## 1213 Research Scientist Very High Married 2929
## 1214 Sales Representative Very High Divorced 2275
## 1215 Healthcare Representative Very High Married 7879
## 1216 Research Scientist Very High Single 4930
## 1217 Sales Executive Very High Married 7847
## 1218 Research Scientist High Married 4401
## 1219 Sales Executive High Single 9241
## 1220 Laboratory Technician High Married 2974
## 1221 Sales Representative Very High Single 4502
## 1222 Healthcare Representative High Married 10748
## 1223 Human Resources High Married 1555
## 1224 Sales Executive High Married 12936
## 1225 Laboratory Technician High Married 2305
## 1226 Research Director Medium Single 16704
## 1227 Research Scientist High Married 3433
## 1228 Laboratory Technician High Married 3477
## 1229 Human Resources Medium Married 6430
## 1230 Manufacturing Director Low Married 6516
## 1231 Laboratory Technician Low Divorced 3907
## 1232 Healthcare Representative Medium Single 5562
## 1233 Manufacturing Director High Married 6883
## 1234 Research Scientist Very High Married 2862
## 1235 Sales Executive Medium Married 4978
## 1236 Sales Executive Very High Divorced 10368
## 1237 Sales Executive Low Divorced 6134
## 1238 Sales Executive Medium Single 6735
## 1239 Laboratory Technician Medium Single 3295
## 1240 Manufacturing Director Very High Single 5238
## 1241 Laboratory Technician Very High Married 6472
## 1242 Sales Executive High Married 9610
## 1243 Manager Medium Single 19833
## 1244 Human Resources High Married 9756
## 1245 Research Scientist Low Single 4968
## 1246 Human Resources Very High Married 2145
## 1247 Human Resources Very High Divorced 2180
## 1248 Sales Executive High Married 8346
## 1249 Research Scientist Very High Single 3445
## 1250 Sales Representative Medium Single 2760
## 1251 Healthcare Representative High Single 6294
## 1252 Sales Executive Low Divorced 7140
## 1253 Research Scientist Very High Married 2932
## 1254 Sales Executive Medium Single 5147
## 1255 Sales Executive Very High Single 4507
## 1256 Sales Executive Low Single 8564
## 1257 Laboratory Technician Medium Married 2468
## 1258 Sales Executive High Married 8161
## 1259 Research Scientist Low Divorced 2109
## 1260 Healthcare Representative High Married 5294
## 1261 Research Scientist Medium Single 2718
## 1262 Healthcare Representative Very High Married 5811
## 1263 Research Scientist High Married 2437
## 1264 Laboratory Technician Medium Divorced 2766
## 1265 Research Director Low Married 19038
## 1266 Research Scientist Medium Divorced 3055
## 1267 Laboratory Technician Low Divorced 2289
## 1268 Sales Executive High Divorced 4001
## 1269 Manufacturing Director High Married 12965
## 1270 Human Resources Very High Single 3539
## 1271 Sales Executive Very High Single 6029
## 1272 Sales Representative Medium Single 2679
## 1273 Laboratory Technician High Married 3702
## 1274 Laboratory Technician Low Married 2398
## 1275 Sales Executive Very High Married 5468
## 1276 Manager High Married 13116
## 1277 Sales Executive Medium Married 4189
## 1278 Research Director Very High Divorced 19328
## 1279 Healthcare Representative Low Married 8321
## 1280 Research Scientist Medium Divorced 2342
## 1281 Human Resources Medium Divorced 4071
## 1282 Sales Executive Very High Single 5813
## 1283 Research Scientist Low Married 3143
## 1284 Research Scientist Very High Married 2044
## 1285 Research Director High Single 13464
## 1286 Sales Executive Medium Single 7991
## 1287 Laboratory Technician Low Married 3377
## 1288 Healthcare Representative Low Married 5538
## 1289 Manufacturing Director Very High Divorced 5762
## 1290 Human Resources Medium Divorced 2592
## 1291 Laboratory Technician Low Married 5346
## 1292 Manufacturing Director Low Single 4213
## 1293 Sales Executive Very High Divorced 4127
## 1294 Research Scientist High Single 2438
## 1295 Healthcare Representative Medium Single 6870
## 1296 Sales Executive High Divorced 10447
## 1297 Manufacturing Director High Single 9667
## 1298 Human Resources Medium Married 2148
## 1299 Healthcare Representative Medium Married 8926
## 1300 Healthcare Representative Very High Divorced 6513
## 1301 Sales Executive High Married 6799
## 1302 Manager Medium Divorced 16291
## 1303 Laboratory Technician High Married 2705
## 1304 Manufacturing Director Medium Divorced 10333
## 1305 Healthcare Representative Low Divorced 4448
## 1306 Research Scientist Very High Married 6854
## 1307 Sales Executive Low Married 9637
## 1308 Research Scientist Low Married 3591
## 1309 Sales Representative Very High Married 5405
## 1310 Sales Executive Very High Single 4684
## 1311 Research Director High Married 15787
## 1312 Research Scientist High Single 1514
## 1313 Human Resources Low Married 2956
## 1314 Human Resources Low Divorced 2335
## 1315 Sales Executive High Married 5154
## 1316 Research Scientist Medium Married 6962
## 1317 Sales Executive Very High Married 5675
## 1318 Laboratory Technician Very High Single 2379
## 1319 Laboratory Technician Very High Married 3812
## 1320 Sales Executive Very High Single 4648
## 1321 Research Scientist High Married 2936
## 1322 Laboratory Technician High Single 2105
## 1323 Manufacturing Director Very High Divorced 8578
## 1324 Human Resources Very High Divorced 2706
## 1325 Healthcare Representative High Divorced 6384
## 1326 Laboratory Technician High Single 3968
## 1327 Sales Executive Medium Single 9907
## 1328 Sales Executive Low Divorced 13225
## 1329 Sales Representative High Married 3540
## 1330 Human Resources Medium Married 2804
## 1331 Manager High Married 19392
## 1332 Research Director Medium Married 19665
## 1333 Research Scientist Very High Single 2439
## 1334 Sales Executive Medium Married 7314
## 1335 Research Scientist Low Married 4774
## 1336 Research Scientist Very High Divorced 3902
## 1337 Research Scientist Very High Married 2662
## 1338 Sales Representative Medium Married 2856
## 1339 Sales Representative Very High Single 1081
## 1340 Research Scientist Medium Single 2472
## 1341 Sales Executive High Married 5673
## 1342 Laboratory Technician High Divorced 4197
## 1343 Sales Executive Very High Married 9713
## 1344 Laboratory Technician Low Single 2062
## 1345 Research Scientist Low Married 4284
## 1346 Manufacturing Director Medium Married 4788
## 1347 Manufacturing Director Very High Married 5906
## 1348 Human Resources Very High Single 3886
## 1349 Manager Low Divorced 16823
## 1350 Research Scientist High Married 2933
## 1351 Sales Executive High Single 6500
## 1352 Manager Very High Divorced 17174
## 1353 Healthcare Representative Low Married 5033
## 1354 Research Scientist Low Married 2307
## 1355 Laboratory Technician Very High Single 2587
## 1356 Sales Executive Medium Married 5507
## 1357 Sales Executive Medium Married 4393
## 1358 Research Director Low Married 13348
## 1359 Sales Executive Very High Divorced 6583
## 1360 Sales Executive Very High Married 8103
## 1361 Laboratory Technician High Divorced 3978
## 1362 Laboratory Technician Very High Married 2544
## 1363 Healthcare Representative High Single 5399
## 1364 Sales Executive High Single 5487
## 1365 Sales Executive Very High Married 6834
## 1366 Sales Representative Low Single 1091
## 1367 Sales Executive High Married 5736
## 1368 Research Scientist Medium Married 2226
## 1369 Research Scientist Very High Married 5747
## 1370 Sales Executive High Single 9854
## 1371 Research Scientist Medium Married 5467
## 1372 Sales Executive Low Married 5380
## 1373 Manufacturing Director Low Married 5151
## 1374 Research Scientist Medium Divorced 2133
## 1375 Manager Very High Married 17875
## 1376 Research Scientist High Single 2432
## 1377 Research Scientist Very High Divorced 4771
## 1378 Research Director Very High Married 19161
## 1379 Sales Executive Very High Divorced 5087
## 1380 Human Resources Medium Married 2863
## 1381 Sales Executive Low Married 5561
## 1382 Research Scientist High Single 2144
## 1383 Research Scientist Low Divorced 3065
## 1384 Laboratory Technician Medium Married 2810
## 1385 Sales Executive Very High Single 9888
## 1386 Sales Executive High Divorced 8628
## 1387 Laboratory Technician Low Single 2867
## 1388 Healthcare Representative Low Married 5373
## 1389 Healthcare Representative Very High Divorced 6667
## 1390 Research Scientist Low Married 5003
## 1391 Laboratory Technician Very High Divorced 2367
## 1392 Sales Representative Low Single 2858
## 1393 Sales Executive Very High Married 5204
## 1394 Sales Executive Very High Single 4105
## 1395 Manufacturing Director Very High Single 9679
## 1396 Sales Executive Very High Married 5617
## 1397 Sales Executive Low Single 10448
## 1398 Research Scientist High Married 2897
## 1399 Healthcare Representative High Divorced 5968
## 1400 Healthcare Representative High Married 7510
## 1401 Human Resources Medium Married 2991
## 1402 Manager Medium Married 19636
## 1403 Laboratory Technician Very High Divorced 1129
## 1404 Sales Executive Low Single 13341
## 1405 Research Scientist High Single 4332
## 1406 Research Director High Married 11031
## 1407 Manufacturing Director Low Single 4440
## 1408 Healthcare Representative High Single 4617
## 1409 Laboratory Technician Very High Single 2647
## 1410 Laboratory Technician High Married 6323
## 1411 Sales Executive Medium Married 5677
## 1412 Human Resources Medium Married 2187
## 1413 Laboratory Technician Medium Married 3748
## 1414 Laboratory Technician High Divorced 3977
## 1415 Healthcare Representative High Single 8633
## 1416 Laboratory Technician High Divorced 2008
## 1417 Sales Executive Medium Married 4440
## 1418 Sales Representative High Married 3067
## 1419 Manufacturing Director High Married 5321
## 1420 Research Scientist Low Divorced 5410
## 1421 Research Scientist Very High Married 2782
## 1422 Research Director Medium Married 11957
## 1423 Laboratory Technician High Married 2660
## 1424 Research Scientist High Single 3375
## 1425 Research Scientist High Single 5098
## 1426 Healthcare Representative Very High Married 4878
## 1427 Laboratory Technician Medium Single 2837
## 1428 Laboratory Technician Very High Married 2406
## 1429 Sales Representative Medium Married 2269
## 1430 Research Scientist Very High Single 4108
## 1431 Research Director High Married 13206
## 1432 Sales Executive Very High Married 10422
## 1433 Research Director Very High Married 13744
## 1434 Sales Executive High Divorced 4907
## 1435 Sales Representative Very High Divorced 3482
## 1436 Research Scientist Very High Single 2436
## 1437 Sales Representative Low Single 2380
## 1438 Manager Very High Single 19431
## 1439 Sales Representative Low Married 1790
## 1440 Sales Executive Very High Married 7644
## 1441 Manufacturing Director Medium Divorced 5131
## 1442 Healthcare Representative High Divorced 6306
## 1443 Research Scientist Very High Married 4787
## 1444 Manager High Married 18880
## 1445 Laboratory Technician High Married 2339
## 1446 Manufacturing Director Medium Married 13570
## 1447 Sales Executive High Married 6712
## 1448 Sales Executive Very High Divorced 5406
## 1449 Sales Executive Medium Divorced 8938
## 1450 Research Scientist Low Single 2439
## 1451 Human Resources Very High Single 8837
## 1452 Sales Executive Very High Married 5343
## 1453 Sales Executive High Divorced 6728
## 1454 Sales Executive Very High Married 6652
## 1455 Sales Executive High Single 4850
## 1456 Research Scientist High Single 2809
## 1457 Healthcare Representative High Married 5689
## 1458 Research Scientist High Married 2001
## 1459 Research Scientist Very High Married 2977
## 1460 Laboratory Technician Medium Married 4025
## 1461 Research Scientist Low Single 3785
## 1462 Sales Executive Low Divorced 10854
## 1463 Sales Executive Very High Married 12031
## 1464 Manufacturing Director Low Single 9936
## 1465 Sales Representative High Single 2966
## 1466 Laboratory Technician Very High Married 2571
## 1467 Healthcare Representative Low Married 9991
## 1468 Manufacturing Director Medium Married 6142
## 1469 Sales Executive Medium Married 5390
## 1470 Laboratory Technician High Married 4404
## MonthlyRate NumCompaniesWorked Over18 OverTime PercentSalaryHike
## 1 19479 8 Y Yes 11
## 2 24907 1 Y No 23
## 3 2396 6 Y Yes 15
## 4 23159 1 Y Yes 11
## 5 16632 9 Y No 12
## 6 11864 0 Y No 13
## 7 9964 4 Y Yes 20
## 8 13335 1 Y No 22
## 9 8787 0 Y No 21
## 10 16577 6 Y No 13
## 11 16479 0 Y No 13
## 12 12682 0 Y Yes 12
## 13 15170 1 Y No 17
## 14 8758 0 Y No 11
## 15 12947 5 Y Yes 14
## 16 10195 1 Y No 11
## 17 15053 0 Y Yes 12
## 18 7324 1 Y Yes 13
## 19 22021 2 Y No 16
## 20 4306 5 Y Yes 11
## 21 8232 0 Y No 18
## 22 6986 7 Y No 23
## 23 21293 0 Y No 11
## 24 19281 1 Y No 14
## 25 17102 2 Y No 11
## 26 10735 4 Y No 11
## 27 4681 1 Y Yes 22
## 28 21173 0 Y No 11
## 29 2094 3 Y No 14
## 30 22822 3 Y No 12
## 31 6670 4 Y No 11
## 32 19121 2 Y Yes 13
## 33 16117 1 Y No 13
## 34 3335 3 Y No 14
## 35 3020 2 Y Yes 16
## 36 21923 1 Y No 12
## 37 3810 1 Y Yes 14
## 38 9687 1 Y No 13
## 39 13072 9 Y Yes 14
## 40 3193 2 Y No 19
## 41 10910 1 Y No 12
## 42 19715 1 Y No 13
## 43 10558 1 Y No 12
## 44 2975 1 Y No 15
## 45 10781 1 Y No 23
## 46 16280 1 Y No 12
## 47 10034 0 Y No 20
## 48 10227 4 Y No 21
## 49 20445 4 Y Yes 21
## 50 4892 1 Y No 19
## 51 19294 9 Y Yes 13
## 52 11179 1 Y Yes 13
## 53 4009 5 Y Yes 21
## 54 8302 2 Y Yes 13
## 55 21436 7 Y Yes 19
## 56 15146 1 Y Yes 12
## 57 11031 1 Y No 22
## 58 16002 3 Y Yes 15
## 59 9528 3 Y No 22
## 60 2689 1 Y No 18
## 61 10877 1 Y Yes 22
## 62 5456 1 Y No 11
## 63 16701 5 Y Yes 12
## 64 2354 7 Y No 11
## 65 8202 1 Y No 13
## 66 19730 2 Y Yes 14
## 67 22656 1 Y No 13
## 68 18787 2 Y No 17
## 69 5868 4 Y No 13
## 70 21777 0 Y Yes 17
## 71 24668 7 Y No 11
## 72 4956 0 Y No 23
## 73 18775 1 Y No 17
## 74 7346 1 Y No 17
## 75 22002 3 Y No 12
## 76 20682 1 Y No 23
## 77 23016 0 Y No 14
## 78 15067 4 Y Yes 14
## 79 25258 4 Y No 13
## 80 10425 8 Y Yes 22
## 81 15998 1 Y Yes 12
## 82 26278 1 Y No 18
## 83 18092 3 Y No 14
## 84 15717 7 Y Yes 12
## 85 17736 1 Y No 15
## 86 21698 4 Y No 11
## 87 9518 3 Y No 13
## 88 18725 3 Y No 23
## 89 15830 1 Y No 19
## 90 13596 1 Y No 16
## 91 14115 1 Y No 22
## 92 8423 0 Y Yes 22
## 93 19760 1 Y Yes 12
## 94 3142 2 Y Yes 13
## 95 24301 1 Y No 16
## 96 24001 9 Y No 12
## 97 17519 0 Y No 21
## 98 8863 1 Y No 15
## 99 24409 0 Y No 13
## 100 25043 4 Y No 12
## 101 23648 4 Y Yes 22
## 102 15178 1 Y No 13
## 103 19783 1 Y Yes 18
## 104 12482 1 Y No 14
## 105 15850 5 Y No 14
## 106 21922 9 Y No 21
## 107 9755 3 Y Yes 19
## 108 26959 1 Y Yes 11
## 109 26897 1 Y No 11
## 110 23785 1 Y No 15
## 111 25796 3 Y No 20
## 112 22887 1 Y Yes 24
## 113 13871 2 Y Yes 12
## 114 13257 0 Y No 12
## 115 15000 6 Y No 15
## 116 14506 2 Y No 12
## 117 5615 2 Y No 12
## 118 22952 0 Y No 14
## 119 2561 5 Y No 22
## 120 19494 1 Y Yes 12
## 121 22310 1 Y No 25
## 122 15480 0 Y No 13
## 123 4510 9 Y Yes 18
## 124 6462 7 Y No 13
## 125 20739 4 Y Yes 18
## 126 23300 1 Y No 19
## 127 3465 1 Y No 12
## 128 26820 1 Y Yes 19
## 129 19299 0 Y No 14
## 130 5549 1 Y No 14
## 131 16090 4 Y No 12
## 132 6645 4 Y No 11
## 133 24788 3 Y Yes 11
## 134 21196 3 Y Yes 13
## 135 8916 1 Y No 23
## 136 2819 6 Y No 20
## 137 25150 2 Y No 15
## 138 14590 4 Y No 14
## 139 24835 2 Y No 18
## 140 13982 0 Y Yes 19
## 141 10224 7 Y No 22
## 142 9752 5 Y No 13
## 143 2302 3 Y Yes 20
## 144 23910 1 Y No 14
## 145 22812 0 Y No 17
## 146 10415 5 Y No 14
## 147 11162 0 Y No 13
## 148 12888 4 Y No 13
## 149 6961 2 Y No 21
## 150 16102 1 Y No 14
## 151 8504 1 Y No 11
## 152 11439 1 Y No 13
## 153 16047 2 Y Yes 20
## 154 10901 8 Y No 18
## 155 15589 1 Y No 14
## 156 26283 1 Y No 13
## 157 13983 2 Y No 17
## 158 11873 4 Y Yes 20
## 159 8552 7 Y No 11
## 160 11314 6 Y No 18
## 161 11992 1 Y No 24
## 162 5970 6 Y No 18
## 163 6672 1 Y No 11
## 164 23402 3 Y Yes 16
## 165 25326 1 Y Yes 15
## 166 17053 3 Y No 15
## 167 4609 4 Y No 12
## 168 8053 2 Y No 12
## 169 12930 4 Y No 22
## 170 12430 3 Y No 20
## 171 13364 0 Y Yes 16
## 172 20989 0 Y No 21
## 173 15062 4 Y No 12
## 174 11012 1 Y No 11
## 175 6319 4 Y Yes 11
## 176 13939 4 Y Yes 18
## 177 10515 0 Y No 14
## 178 9241 1 Y No 22
## 179 2137 1 Y No 25
## 180 6319 1 Y No 12
## 181 6984 8 Y Yes 23
## 182 5711 2 Y No 15
## 183 21728 1 Y Yes 22
## 184 3425 2 Y No 15
## 185 26250 1 Y No 11
## 186 4673 1 Y No 13
## 187 6499 1 Y No 14
## 188 13339 8 Y No 11
## 189 14074 1 Y No 17
## 190 7192 3 Y No 18
## 191 5678 0 Y No 14
## 192 11781 1 Y No 16
## 193 15497 3 Y Yes 13
## 194 5228 4 Y No 14
## 195 20462 9 Y No 23
## 196 22977 1 Y Yes 12
## 197 15986 5 Y No 11
## 198 16439 2 Y No 16
## 199 7259 9 Y No 14
## 200 4185 3 Y No 18
## 201 9679 5 Y No 19
## 202 23793 1 Y Yes 15
## 203 5972 1 Y Yes 17
## 204 16523 8 Y Yes 15
## 205 11354 7 Y Yes 19
## 206 24525 1 Y No 22
## 207 12392 1 Y Yes 16
## 208 7703 1 Y No 13
## 209 14242 9 Y No 14
## 210 12368 7 Y No 16
## 211 25812 1 Y No 11
## 212 20925 1 Y No 22
## 213 12916 1 Y No 14
## 214 15736 5 Y No 16
## 215 8556 5 Y Yes 11
## 216 14674 3 Y Yes 18
## 217 22967 5 Y No 15
## 218 19757 0 Y No 14
## 219 16840 6 Y No 12
## 220 17011 2 Y No 14
## 221 9945 8 Y No 16
## 222 13248 6 Y No 21
## 223 10056 1 Y Yes 14
## 224 9647 0 Y No 12
## 225 4381 0 Y No 12
## 226 8456 3 Y No 17
## 227 2539 4 Y No 17
## 228 6599 1 Y No 14
## 229 9096 1 Y No 14
## 230 14961 1 Y Yes 13
## 231 3300 7 Y No 15
## 232 4933 1 Y No 11
## 233 25657 8 Y No 17
## 234 24118 3 Y No 11
## 235 22149 5 Y Yes 13
## 236 7744 5 Y Yes 22
## 237 21509 7 Y No 20
## 238 21030 1 Y Yes 18
## 239 20990 2 Y No 11
## 240 9571 0 Y Yes 14
## 241 15417 7 Y No 14
## 242 12069 0 Y No 18
## 243 19877 2 Y No 16
## 244 24447 1 Y No 17
## 245 15970 0 Y No 11
## 246 13523 9 Y No 12
## 247 14776 1 Y No 13
## 248 23687 6 Y No 13
## 249 18697 2 Y No 14
## 250 5118 4 Y No 17
## 251 22573 6 Y No 11
## 252 6420 0 Y No 25
## 253 22673 1 Y No 19
## 254 23016 3 Y Yes 13
## 255 10732 2 Y No 14
## 256 7505 0 Y No 12
## 257 8007 0 Y Yes 11
## 258 5949 0 Y No 19
## 259 23231 1 Y No 11
## 260 11652 0 Y No 11
## 261 26062 1 Y No 20
## 262 19682 3 Y No 18
## 263 19737 4 Y No 13
## 264 14977 3 Y Yes 12
## 265 14935 2 Y No 11
## 266 3687 2 Y No 19
## 267 14408 0 Y No 21
## 268 18384 1 Y No 12
## 269 7501 0 Y Yes 14
## 270 20251 0 Y No 11
## 271 18938 0 Y Yes 14
## 272 10268 1 Y Yes 12
## 273 2613 1 Y No 23
## 274 22825 4 Y No 14
## 275 10531 1 Y No 17
## 276 11677 2 Y Yes 18
## 277 19100 7 Y No 18
## 278 19191 1 Y Yes 24
## 279 26767 1 Y No 20
## 280 15815 3 Y No 14
## 281 21016 3 Y Yes 16
## 282 24532 1 Y No 25
## 283 20260 1 Y No 18
## 284 15972 3 Y Yes 19
## 285 22722 1 Y Yes 13
## 286 15881 1 Y No 12
## 287 19920 3 Y Yes 22
## 288 18899 9 Y No 14
## 289 14180 2 Y Yes 13
## 290 4488 1 Y No 21
## 291 25594 9 Y Yes 11
## 292 26285 4 Y No 12
## 293 3909 1 Y No 11
## 294 8450 1 Y Yes 12
## 295 11411 1 Y Yes 12
## 296 14864 5 Y No 14
## 297 25233 1 Y No 13
## 298 5100 0 Y No 15
## 299 7122 4 Y No 18
## 300 16321 5 Y No 18
## 301 15896 1 Y No 19
## 302 9724 1 Y No 12
## 303 4824 0 Y No 19
## 304 12241 4 Y No 11
## 305 10942 0 Y No 17
## 306 6927 7 Y No 15
## 307 16985 1 Y No 14
## 308 26707 3 Y No 17
## 309 17056 2 Y Yes 13
## 310 10077 0 Y Yes 12
## 311 17822 3 Y No 12
## 312 20308 1 Y No 18
## 313 7747 0 Y Yes 18
## 314 23364 6 Y No 11
## 315 5355 1 Y Yes 14
## 316 10675 0 Y No 19
## 317 17810 7 Y Yes 12
## 318 17747 2 Y No 15
## 319 20938 1 Y Yes 12
## 320 24624 1 Y Yes 11
## 321 5242 1 Y Yes 11
## 322 7143 4 Y No 12
## 323 10557 7 Y No 16
## 324 24737 5 Y Yes 13
## 325 11934 1 Y No 13
## 326 14034 1 Y No 24
## 327 21141 1 Y No 15
## 328 17778 4 Y Yes 18
## 329 4317 3 Y No 14
## 330 16375 4 Y No 11
## 331 6227 1 Y No 11
## 332 22107 0 Y Yes 13
## 333 16885 3 Y No 12
## 334 9262 8 Y No 16
## 335 9278 9 Y No 14
## 336 13273 2 Y Yes 22
## 337 4759 1 Y Yes 11
## 338 7621 0 Y No 17
## 339 5431 1 Y No 13
## 340 3415 1 Y No 18
## 341 23177 7 Y No 16
## 342 15682 0 Y Yes 14
## 343 25713 1 Y Yes 14
## 344 11866 1 Y Yes 14
## 345 18264 0 Y No 13
## 346 16092 1 Y No 12
## 347 10110 6 Y Yes 15
## 348 25751 3 Y No 19
## 349 15901 2 Y No 14
## 350 16928 1 Y No 14
## 351 24017 0 Y Yes 11
## 352 3956 1 Y No 13
## 353 23978 3 Y No 21
## 354 17001 4 Y Yes 13
## 355 6069 7 Y Yes 12
## 356 20156 1 Y No 17
## 357 17078 3 Y No 23
## 358 9150 1 Y Yes 11
## 359 15276 4 Y No 15
## 360 7246 4 Y No 11
## 361 2967 2 Y No 11
## 362 22495 3 Y Yes 13
## 363 2851 1 Y No 24
## 364 9150 1 Y Yes 13
## 365 17663 6 Y No 20
## 366 16044 2 Y No 14
## 367 9558 1 Y No 18
## 368 2755 6 Y No 15
## 369 6110 2 Y Yes 12
## 370 7551 0 Y Yes 16
## 371 25422 1 Y No 15
## 372 18168 9 Y No 16
## 373 19394 9 Y No 19
## 374 17881 1 Y No 11
## 375 20750 1 Y No 16
## 376 12066 8 Y No 24
## 377 14862 4 Y No 11
## 378 11868 4 Y No 13
## 379 4652 8 Y Yes 13
## 380 23258 2 Y Yes 13
## 381 5915 1 Y Yes 12
## 382 17434 1 Y No 18
## 383 6582 0 Y No 22
## 384 24440 1 Y No 13
## 385 3809 1 Y No 13
## 386 3427 9 Y Yes 23
## 387 26914 1 Y No 12
## 388 22553 7 Y No 12
## 389 20490 3 Y No 16
## 390 21123 2 Y Yes 12
## 391 4345 1 Y No 11
## 392 13436 6 Y No 22
## 393 8509 4 Y No 11
## 394 12740 9 Y No 15
## 395 4156 1 Y No 12
## 396 15238 7 Y No 20
## 397 2227 4 Y Yes 14
## 398 12090 1 Y Yes 11
## 399 23866 3 Y Yes 15
## 400 16193 1 Y No 12
## 401 8213 1 Y Yes 14
## 402 18256 9 Y No 11
## 403 19558 0 Y No 11
## 404 19566 1 Y No 16
## 405 13535 1 Y No 12
## 406 9396 5 Y No 13
## 407 19609 2 Y Yes 14
## 408 9655 3 Y No 21
## 409 10310 2 Y No 13
## 410 12932 2 Y No 11
## 411 24793 2 Y No 20
## 412 3854 5 Y No 11
## 413 26314 2 Y No 14
## 414 4386 0 Y No 11
## 415 21972 1 Y Yes 16
## 416 12253 0 Y No 16
## 417 12106 1 Y Yes 23
## 418 13022 0 Y No 14
## 419 3032 1 Y No 22
## 420 16734 4 Y No 15
## 421 21526 1 Y No 18
## 422 18300 5 Y No 16
## 423 18437 1 Y No 12
## 424 2890 0 Y No 11
## 425 22102 3 Y No 12
## 426 9314 0 Y No 15
## 427 7181 0 Y No 14
## 428 2845 4 Y No 19
## 429 7389 5 Y No 13
## 430 2288 6 Y No 13
## 431 19225 0 Y No 15
## 432 23428 7 Y No 11
## 433 8416 3 Y No 12
## 434 11563 2 Y Yes 19
## 435 14394 1 Y No 25
## 436 24619 7 Y Yes 12
## 437 6705 7 Y No 13
## 438 18398 0 Y No 14
## 439 14295 4 Y Yes 12
## 440 22908 3 Y No 12
## 441 11533 9 Y Yes 15
## 442 9260 4 Y No 17
## 443 15318 1 Y No 14
## 444 9129 5 Y No 16
## 445 19658 2 Y No 14
## 446 9873 3 Y No 23
## 447 13430 7 Y No 14
## 448 18659 9 Y No 11
## 449 20364 7 Y No 15
## 450 17872 1 Y No 11
## 451 8346 4 Y Yes 13
## 452 6950 1 Y Yes 21
## 453 16177 0 Y No 19
## 454 22808 0 Y Yes 11
## 455 22645 4 Y No 12
## 456 22578 4 Y No 19
## 457 25291 9 Y No 21
## 458 8059 1 Y Yes 14
## 459 11373 3 Y No 15
## 460 2112 2 Y Yes 17
## 461 4267 5 Y No 12
## 462 6698 1 Y No 16
## 463 19921 1 Y No 12
## 464 23213 1 Y Yes 18
## 465 23848 4 Y No 17
## 466 8984 5 Y No 11
## 467 5626 7 Y No 16
## 468 24666 1 Y No 13
## 469 22087 3 Y Yes 12
## 470 23914 8 Y No 12
## 471 5530 0 Y No 13
## 472 22174 3 Y No 19
## 473 15701 6 Y No 12
## 474 2125 1 Y Yes 17
## 475 21630 1 Y Yes 11
## 476 7428 1 Y No 20
## 477 9100 1 Y No 21
## 478 7999 1 Y No 11
## 479 26376 1 Y No 11
## 480 14168 1 Y Yes 16
## 481 14470 1 Y No 18
## 482 22794 1 Y Yes 13
## 483 11512 2 Y No 17
## 484 14004 4 Y No 14
## 485 6219 4 Y No 22
## 486 5013 0 Y No 12
## 487 3010 4 Y Yes 11
## 488 11757 1 Y No 13
## 489 5718 1 Y No 13
## 490 2671 4 Y Yes 14
## 491 14561 3 Y No 17
## 492 19627 3 Y Yes 13
## 493 17997 7 Y No 11
## 494 26894 4 Y No 11
## 495 2912 1 Y Yes 18
## 496 16346 0 Y No 11
## 497 24444 1 Y No 11
## 498 9358 4 Y Yes 12
## 499 12145 0 Y No 20
## 500 20431 0 Y No 12
## 501 18089 1 Y Yes 12
## 502 22653 1 Y No 20
## 503 7507 7 Y No 20
## 504 7660 1 Y No 12
## 505 5630 2 Y No 14
## 506 17759 1 Y Yes 13
## 507 9606 1 Y No 15
## 508 3487 1 Y No 14
## 509 19368 1 Y No 13
## 510 5335 3 Y No 23
## 511 6729 2 Y No 15
## 512 13934 2 Y Yes 11
## 513 15174 0 Y No 13
## 514 26999 1 Y Yes 11
## 515 3164 1 Y Yes 11
## 516 16900 1 Y No 18
## 517 8544 2 Y No 16
## 518 15678 0 Y No 22
## 519 7791 0 Y No 20
## 520 18959 1 Y No 18
## 521 18597 3 Y No 12
## 522 16673 1 Y Yes 20
## 523 15232 3 Y No 17
## 524 3297 1 Y Yes 11
## 525 17654 1 Y No 14
## 526 24785 9 Y No 14
## 527 20978 1 Y No 11
## 528 21703 1 Y No 12
## 529 23452 3 Y Yes 14
## 530 19383 0 Y No 13
## 531 6009 1 Y No 11
## 532 19373 3 Y No 15
## 533 11825 2 Y No 12
## 534 23772 5 Y Yes 21
## 535 13514 3 Y No 19
## 536 8861 3 Y No 15
## 537 11924 8 Y No 14
## 538 4809 1 Y No 21
## 539 19562 1 Y No 12
## 540 9983 7 Y No 15
## 541 3872 7 Y Yes 13
## 542 20335 9 Y No 14
## 543 15397 4 Y Yes 14
## 544 2104 2 Y No 14
## 545 10225 9 Y Yes 12
## 546 25275 7 Y No 23
## 547 2755 1 Y No 11
## 548 20366 6 Y Yes 12
## 549 23683 3 Y No 18
## 550 7360 3 Y No 11
## 551 4344 1 Y No 14
## 552 18767 9 Y No 15
## 553 20420 7 Y No 11
## 554 22929 0 Y Yes 20
## 555 23398 8 Y No 19
## 556 17967 1 Y No 14
## 557 10919 2 Y No 17
## 558 4761 2 Y No 11
## 559 21146 1 Y No 15
## 560 20471 6 Y Yes 13
## 561 4187 3 Y No 14
## 562 10084 1 Y No 11
## 563 5207 1 Y Yes 13
## 564 22807 1 Y No 23
## 565 12388 0 Y No 13
## 566 19630 1 Y No 18
## 567 10339 4 Y Yes 12
## 568 18686 1 Y No 22
## 569 21199 5 Y Yes 13
## 570 14229 1 Y No 15
## 571 26589 0 Y No 18
## 572 5288 3 Y No 14
## 573 25549 4 Y No 12
## 574 3064 6 Y No 17
## 575 13551 2 Y No 16
## 576 22670 9 Y Yes 11
## 577 24008 0 Y No 19
## 578 19905 0 Y Yes 13
## 579 26085 6 Y Yes 12
## 580 7530 1 Y No 11
## 581 20317 1 Y No 16
## 582 24375 3 Y No 21
## 583 9931 1 Y No 24
## 584 13305 5 Y No 17
## 585 16225 1 Y No 13
## 586 3445 1 Y Yes 21
## 587 26551 1 Y No 11
## 588 21821 8 Y No 20
## 589 6881 5 Y No 16
## 590 6689 1 Y Yes 11
## 591 25995 0 Y No 11
## 592 26507 5 Y No 15
## 593 12982 1 Y Yes 11
## 594 23361 0 Y No 15
## 595 23779 1 Y No 24
## 596 25761 7 Y Yes 12
## 597 13301 3 Y No 13
## 598 4051 9 Y Yes 13
## 599 16374 6 Y No 17
## 600 25527 4 Y No 13
## 601 19124 1 Y No 12
## 602 11983 6 Y No 14
## 603 20234 5 Y Yes 24
## 604 6153 1 Y No 14
## 605 11806 1 Y No 13
## 606 4284 2 Y No 15
## 607 8306 1 Y No 16
## 608 5860 1 Y No 18
## 609 21519 4 Y No 16
## 610 5200 6 Y No 24
## 611 8842 1 Y Yes 16
## 612 18869 3 Y No 21
## 613 3698 1 Y Yes 20
## 614 2243 0 Y No 19
## 615 20898 1 Y Yes 14
## 616 16571 1 Y No 11
## 617 5594 2 Y No 14
## 618 5197 9 Y No 12
## 619 21632 7 Y No 13
## 620 21816 1 Y No 22
## 621 17852 1 Y No 11
## 622 2823 1 Y Yes 14
## 623 9250 2 Y No 11
## 624 2373 9 Y No 12
## 625 20715 7 Y Yes 18
## 626 19239 4 Y Yes 12
## 627 22162 5 Y No 12
## 628 19028 3 Y No 22
## 629 24558 4 Y No 19
## 630 23965 1 Y No 13
## 631 19146 6 Y No 22
## 632 5044 2 Y Yes 24
## 633 9068 5 Y Yes 14
## 634 8635 0 Y No 21
## 635 14363 1 Y Yes 18
## 636 23457 1 Y Yes 20
## 637 16612 1 Y Yes 19
## 638 9148 0 Y No 12
## 639 18154 1 Y No 12
## 640 10554 2 Y No 16
## 641 10778 0 Y No 17
## 642 8891 1 Y No 14
## 643 12102 0 Y No 19
## 644 23726 2 Y Yes 13
## 645 14871 3 Y Yes 19
## 646 23522 6 Y Yes 19
## 647 22789 5 Y No 14
## 648 9129 3 Y No 16
## 649 21222 5 Y No 15
## 650 20206 6 Y No 18
## 651 21782 4 Y No 13
## 652 17783 0 Y Yes 22
## 653 4814 1 Y Yes 13
## 654 4544 1 Y No 11
## 655 7790 8 Y No 11
## 656 22650 3 Y Yes 11
## 657 18016 1 Y Yes 24
## 658 6054 6 Y No 14
## 659 7508 1 Y Yes 13
## 660 24252 1 Y No 14
## 661 13384 9 Y Yes 14
## 662 23814 4 Y No 21
## 663 22052 1 Y No 13
## 664 8870 1 Y No 19
## 665 4821 0 Y Yes 17
## 666 13137 1 Y Yes 18
## 667 10022 0 Y Yes 19
## 668 17725 4 Y Yes 13
## 669 9834 5 Y No 18
## 670 4303 7 Y Yes 21
## 671 17808 1 Y No 19
## 672 6896 1 Y No 14
## 673 7824 7 Y No 17
## 674 3339 3 Y Yes 11
## 675 9255 2 Y Yes 13
## 676 11737 3 Y No 15
## 677 19170 1 Y Yes 25
## 678 22477 4 Y No 11
## 679 5543 4 Y No 17
## 680 24406 1 Y No 13
## 681 23293 2 Y No 18
## 682 16292 1 Y No 13
## 683 3974 6 Y No 20
## 684 18798 1 Y Yes 18
## 685 20652 2 Y No 12
## 686 11148 1 Y No 12
## 687 3119 2 Y Yes 13
## 688 12287 4 Y No 21
## 689 9947 1 Y Yes 13
## 690 13008 1 Y No 19
## 691 17369 0 Y Yes 11
## 692 25063 8 Y Yes 14
## 693 13554 1 Y No 12
## 694 5518 1 Y Yes 11
## 695 12291 0 Y No 14
## 696 14922 5 Y No 11
## 697 23163 1 Y No 12
## 698 18203 1 Y No 15
## 699 6179 1 Y No 16
## 700 13829 2 Y No 15
## 701 26227 4 Y No 24
## 702 13938 6 Y No 13
## 703 9977 5 Y No 11
## 704 19899 1 Y Yes 13
## 705 6812 6 Y No 13
## 706 2560 0 Y No 18
## 707 17071 4 Y Yes 16
## 708 6759 1 Y Yes 19
## 709 25952 4 Y No 13
## 710 10322 0 Y Yes 22
## 711 20489 1 Y No 11
## 712 11479 6 Y Yes 20
## 713 17241 3 Y No 18
## 714 11005 3 Y No 14
## 715 6615 9 Y No 22
## 716 20161 1 Y Yes 13
## 717 3735 2 Y No 17
## 718 12086 9 Y No 14
## 719 14039 1 Y Yes 15
## 720 8571 1 Y Yes 17
## 721 11539 4 Y Yes 11
## 722 4161 3 Y Yes 18
## 723 12127 0 Y No 17
## 724 24208 6 Y No 13
## 725 24117 1 Y No 15
## 726 10074 1 Y Yes 24
## 727 11275 1 Y No 12
## 728 13493 1 Y No 15
## 729 13943 8 Y No 11
## 730 6975 1 Y Yes 11
## 731 17802 0 Y Yes 12
## 732 18275 1 Y Yes 15
## 733 25725 0 Y No 17
## 734 3334 1 Y No 12
## 735 6881 1 Y No 15
## 736 13119 2 Y No 13
## 737 22245 7 Y No 14
## 738 23371 6 Y No 14
## 739 7060 1 Y No 16
## 740 4658 0 Y No 18
## 741 9541 1 Y No 20
## 742 7770 6 Y No 13
## 743 20165 4 Y No 18
## 744 21829 3 Y Yes 13
## 745 14382 5 Y No 15
## 746 12992 3 Y Yes 14
## 747 20284 1 Y No 22
## 748 4981 8 Y Yes 12
## 749 21813 8 Y No 18
## 750 25846 1 Y No 15
## 751 11737 3 Y Yes 18
## 752 24920 0 Y No 12
## 753 8269 1 Y No 16
## 754 5083 1 Y Yes 13
## 755 21437 0 Y No 19
## 756 5404 3 Y No 13
## 757 11135 9 Y No 12
## 758 12278 0 Y No 11
## 759 11038 3 Y Yes 14
## 760 8318 1 Y No 16
## 761 23537 2 Y No 12
## 762 7858 7 Y No 14
## 763 15346 6 Y Yes 14
## 764 18410 1 Y Yes 19
## 765 23384 1 Y No 22
## 766 2997 3 Y No 16
## 767 12853 2 Y Yes 11
## 768 13848 3 Y No 15
## 769 22217 1 Y No 14
## 770 25265 1 Y No 13
## 771 21445 9 Y No 17
## 772 8392 6 Y No 11
## 773 12154 2 Y No 19
## 774 15669 0 Y No 11
## 775 17323 7 Y No 15
## 776 5268 5 Y No 13
## 777 17205 1 Y Yes 14
## 778 17258 1 Y No 13
## 779 21029 8 Y Yes 23
## 780 10332 9 Y Yes 12
## 781 12355 1 Y No 12
## 782 11141 1 Y No 16
## 783 9096 0 Y No 15
## 784 18863 1 Y No 13
## 785 24608 0 Y No 18
## 786 26542 4 Y No 20
## 787 5869 1 Y No 19
## 788 15813 3 Y No 18
## 789 7909 3 Y No 13
## 790 2326 9 Y No 14
## 791 21214 4 Y No 15
## 792 10333 0 Y Yes 22
## 793 3129 1 Y No 22
## 794 22482 1 Y No 16
## 795 22266 0 Y No 17
## 796 24223 2 Y Yes 14
## 797 4605 1 Y Yes 15
## 798 19373 1 Y No 20
## 799 2993 4 Y Yes 20
## 800 14399 0 Y No 17
## 801 7160 1 Y No 15
## 802 17251 3 Y Yes 14
## 803 13401 0 Y No 17
## 804 22478 3 Y No 17
## 805 16154 2 Y No 22
## 806 17970 1 Y No 13
## 807 15322 7 Y No 19
## 808 5569 0 Y Yes 14
## 809 26968 4 Y No 22
## 810 8039 0 Y No 17
## 811 15596 3 Y No 12
## 812 20619 7 Y No 16
## 813 11535 8 Y No 11
## 814 13547 7 Y No 11
## 815 17544 1 Y No 14
## 816 25326 1 Y Yes 11
## 817 8770 9 Y No 15
## 818 23238 2 Y No 20
## 819 21981 0 Y No 12
## 820 19911 0 Y No 17
## 821 18500 1 Y No 11
## 822 11879 6 Y No 17
## 823 15834 2 Y No 11
## 824 17940 0 Y No 14
## 825 9558 4 Y No 19
## 826 17689 1 Y Yes 15
## 827 6004 1 Y No 13
## 828 22088 1 Y Yes 14
## 829 13556 1 Y No 12
## 830 18385 0 Y Yes 17
## 831 9051 3 Y Yes 11
## 832 6233 1 Y No 12
## 833 17171 7 Y No 13
## 834 7950 1 Y No 13
## 835 5829 1 Y No 20
## 836 7108 1 Y No 17
## 837 11162 1 Y No 13
## 838 13782 9 Y No 17
## 839 2447 0 Y Yes 12
## 840 2253 7 Y No 13
## 841 16340 6 Y No 12
## 842 6409 8 Y No 22
## 843 22955 1 Y Yes 11
## 844 13421 1 Y No 22
## 845 2706 1 Y No 18
## 846 21203 3 Y Yes 13
## 847 19588 2 Y No 18
## 848 25755 0 Y No 20
## 849 26537 1 Y No 13
## 850 9489 8 Y No 13
## 851 14947 1 Y No 12
## 852 18575 4 Y No 13
## 853 26342 1 Y No 13
## 854 7172 1 Y No 25
## 855 20100 4 Y Yes 19
## 856 9961 1 Y No 13
## 857 12828 1 Y No 12
## 858 10826 1 Y Yes 11
## 859 18640 3 Y No 18
## 860 26933 0 Y Yes 18
## 861 4223 0 Y Yes 11
## 862 24097 8 Y No 23
## 863 4279 2 Y No 13
## 864 8429 1 Y No 13
## 865 20293 6 Y No 17
## 866 13192 8 Y No 19
## 867 25440 5 Y No 12
## 868 9490 2 Y No 22
## 869 12449 1 Y No 12
## 870 10849 5 Y No 11
## 871 21026 3 Y Yes 15
## 872 3372 1 Y No 13
## 873 4905 1 Y No 12
## 874 6865 1 Y No 14
## 875 11309 1 Y No 13
## 876 7744 1 Y No 25
## 877 5050 1 Y No 17
## 878 17433 2 Y No 11
## 879 12858 7 Y No 16
## 880 10893 0 Y Yes 18
## 881 7331 1 Y No 20
## 882 2338 4 Y Yes 14
## 883 4235 2 Y Yes 21
## 884 6311 0 Y No 13
## 885 11591 7 Y No 12
## 886 20623 0 Y No 14
## 887 9369 0 Y Yes 21
## 888 23281 3 Y Yes 17
## 889 13755 4 Y Yes 11
## 890 14377 1 Y Yes 14
## 891 9659 7 Y No 17
## 892 19982 1 Y No 13
## 893 6148 1 Y Yes 25
## 894 5598 1 Y No 15
## 895 23474 3 Y No 14
## 896 17089 1 Y Yes 12
## 897 17198 1 Y No 19
## 898 16490 5 Y No 17
## 899 18625 3 Y No 14
## 900 12124 2 Y No 13
## 901 9256 1 Y No 12
## 902 16620 5 Y No 11
## 903 3208 1 Y No 11
## 904 4204 1 Y Yes 11
## 905 8733 6 Y No 12
## 906 3423 3 Y No 14
## 907 21643 0 Y No 17
## 908 8751 7 Y No 11
## 909 21708 0 Y Yes 14
## 910 21221 1 Y Yes 12
## 911 16901 1 Y No 22
## 912 8040 1 Y Yes 14
## 913 9973 1 Y Yes 20
## 914 2493 2 Y Yes 16
## 915 25592 1 Y Yes 15
## 916 25308 1 Y No 20
## 917 9946 2 Y No 14
## 918 6039 0 Y Yes 12
## 919 19196 4 Y Yes 24
## 920 20002 6 Y No 12
## 921 22534 4 Y No 13
## 922 6842 0 Y Yes 11
## 923 17477 1 Y No 14
## 924 21833 4 Y No 11
## 925 6020 0 Y Yes 14
## 926 5628 6 Y Yes 16
## 927 20364 3 Y No 14
## 928 2323 9 Y Yes 11
## 929 14075 1 Y No 11
## 930 14222 1 Y Yes 12
## 931 4257 0 Y No 14
## 932 12858 7 Y Yes 18
## 933 17285 3 Y Yes 13
## 934 4732 2 Y No 11
## 935 18830 1 Y No 18
## 936 11693 1 Y No 15
## 937 13035 3 Y No 22
## 938 17334 6 Y Yes 13
## 939 26076 1 Y No 12
## 940 22845 1 Y No 18
## 941 22154 0 Y No 13
## 942 23631 0 Y No 12
## 943 5747 3 Y No 12
## 944 22957 6 Y No 12
## 945 16392 0 Y No 11
## 946 22422 4 Y Yes 11
## 947 17235 2 Y Yes 12
## 948 21534 9 Y Yes 19
## 949 25927 1 Y Yes 23
## 950 13352 0 Y No 11
## 951 8935 1 Y Yes 19
## 952 22074 1 Y No 13
## 953 8319 1 Y Yes 11
## 954 14669 4 Y No 12
## 955 26582 0 Y Yes 13
## 956 6992 4 Y No 14
## 957 4022 6 Y No 14
## 958 8532 9 Y No 16
## 959 5494 0 Y No 11
## 960 22455 1 Y No 13
## 961 4297 0 Y No 17
## 962 2690 1 Y Yes 11
## 963 17588 1 Y Yes 11
## 964 19461 3 Y No 15
## 965 23553 1 Y No 12
## 966 9075 3 Y No 11
## 967 12023 7 Y Yes 14
## 968 6762 3 Y No 22
## 969 2261 2 Y No 16
## 970 18706 1 Y No 11
## 971 6527 8 Y No 14
## 972 24439 3 Y No 16
## 973 19305 1 Y No 15
## 974 10846 0 Y No 12
## 975 20392 3 Y No 20
## 976 9277 6 Y Yes 17
## 977 18235 4 Y Yes 12
## 978 15891 1 Y No 20
## 979 13888 5 Y No 20
## 980 17491 4 Y No 13
## 981 11882 7 Y No 14
## 982 23288 0 Y Yes 18
## 983 15748 1 Y No 11
## 984 6163 1 Y No 11
## 985 24232 1 Y No 11
## 986 21057 1 Y Yes 15
## 987 3567 3 Y Yes 12
## 988 15395 2 Y No 11
## 989 13953 3 Y Yes 14
## 990 5182 7 Y Yes 15
## 991 19293 6 Y No 13
## 992 20497 0 Y Yes 13
## 993 3449 3 Y No 21
## 994 12477 2 Y No 11
## 995 9731 2 Y Yes 11
## 996 20003 1 Y Yes 14
## 997 7100 1 Y Yes 11
## 998 25681 1 Y Yes 13
## 999 4050 0 Y No 12
## 1000 16616 0 Y No 14
## 1001 17363 9 Y No 13
## 1002 19106 4 Y No 18
## 1003 19944 2 Y No 11
## 1004 4910 4 Y No 11
## 1005 23577 0 Y No 12
## 1006 9769 1 Y No 13
## 1007 22710 3 Y No 20
## 1008 22930 0 Y Yes 12
## 1009 5652 6 Y No 19
## 1010 22456 3 Y Yes 21
## 1011 12414 2 Y No 13
## 1012 20763 3 Y Yes 16
## 1013 16154 1 Y No 12
## 1014 12761 7 Y No 14
## 1015 8847 3 Y No 11
## 1016 20284 5 Y No 14
## 1017 22262 1 Y No 12
## 1018 7679 0 Y No 14
## 1019 22604 1 Y No 16
## 1020 13494 4 Y No 13
## 1021 10205 7 Y No 12
## 1022 15182 3 Y No 12
## 1023 25470 0 Y No 14
## 1024 10494 2 Y No 22
## 1025 26703 3 Y No 19
## 1026 15211 1 Y Yes 12
## 1027 23343 4 Y No 12
## 1028 3708 5 Y No 17
## 1029 5561 2 Y Yes 12
## 1030 23099 3 Y No 11
## 1031 8386 1 Y No 18
## 1032 15986 4 Y No 11
## 1033 17181 2 Y Yes 23
## 1034 8931 1 Y No 11
## 1035 19863 2 Y Yes 18
## 1036 24609 9 Y No 18
## 1037 21081 6 Y Yes 13
## 1038 14720 4 Y Yes 18
## 1039 24795 4 Y No 11
## 1040 3072 1 Y No 15
## 1041 25178 2 Y No 11
## 1042 23490 0 Y No 18
## 1043 7973 3 Y No 14
## 1044 19764 4 Y No 12
## 1045 21534 2 Y No 14
## 1046 8045 2 Y No 14
## 1047 21158 1 Y No 13
## 1048 17456 0 Y No 14
## 1049 15891 3 Y No 18
## 1050 2939 8 Y No 15
## 1051 16458 1 Y No 13
## 1052 9282 7 Y No 12
## 1053 7152 1 Y No 13
## 1054 2721 0 Y No 24
## 1055 20948 3 Y No 14
## 1056 11929 7 Y No 14
## 1057 15747 3 Y No 15
## 1058 17485 5 Y No 11
## 1059 7815 0 Y Yes 23
## 1060 16192 1 Y No 13
## 1061 16998 2 Y Yes 11
## 1062 7103 1 Y No 13
## 1063 19719 5 Y Yes 18
## 1064 23757 1 Y No 14
## 1065 15428 0 Y No 21
## 1066 16143 0 Y Yes 16
## 1067 8192 8 Y No 11
## 1068 24200 3 Y Yes 17
## 1069 5355 7 Y No 11
## 1070 12530 1 Y No 14
## 1071 11827 0 Y No 14
## 1072 23070 4 Y No 25
## 1073 4668 0 Y No 13
## 1074 3173 1 Y No 14
## 1075 22049 2 Y Yes 17
## 1076 21072 2 Y No 25
## 1077 24456 3 Y No 20
## 1078 7568 6 Y No 13
## 1079 22074 3 Y No 13
## 1080 9150 4 Y No 18
## 1081 11380 8 Y No 12
## 1082 21195 1 Y No 19
## 1083 24812 0 Y No 14
## 1084 21831 3 Y No 14
## 1085 12288 1 Y Yes 14
## 1086 4156 1 Y No 12
## 1087 24450 1 Y Yes 13
## 1088 4944 0 Y Yes 25
## 1089 24052 4 Y No 14
## 1090 3031 1 Y No 17
## 1091 7288 3 Y No 13
## 1092 24152 1 Y No 13
## 1093 4585 4 Y No 20
## 1094 18611 2 Y Yes 14
## 1095 19345 0 Y No 12
## 1096 22949 1 Y Yes 12
## 1097 17381 1 Y Yes 21
## 1098 10036 0 Y No 14
## 1099 8841 3 Y Yes 18
## 1100 20933 1 Y No 12
## 1101 26204 0 Y No 23
## 1102 15624 3 Y No 12
## 1103 17001 3 Y Yes 21
## 1104 13693 8 Y No 14
## 1105 22376 6 Y No 18
## 1106 10589 2 Y No 13
## 1107 5323 1 Y No 11
## 1108 14814 3 Y No 11
## 1109 21731 1 Y No 19
## 1110 17799 3 Y No 12
## 1111 26619 1 Y Yes 12
## 1112 14618 0 Y No 16
## 1113 7653 4 Y No 11
## 1114 25174 4 Y No 14
## 1115 16530 8 Y No 12
## 1116 22061 1 Y No 13
## 1117 23037 1 Y No 21
## 1118 13008 9 Y No 17
## 1119 11133 1 Y No 21
## 1120 12355 0 Y No 11
## 1121 16379 2 Y No 12
## 1122 21447 6 Y No 13
## 1123 16213 1 Y Yes 18
## 1124 4223 3 Y Yes 16
## 1125 4658 2 Y No 11
## 1126 24483 1 Y No 19
## 1127 19519 4 Y Yes 16
## 1128 12826 2 Y No 16
## 1129 19711 3 Y Yes 13
## 1130 26362 8 Y No 22
## 1131 25348 1 Y No 17
## 1132 15332 1 Y No 14
## 1133 11262 1 Y No 15
## 1134 5855 5 Y No 12
## 1135 7713 1 Y No 18
## 1136 3156 1 Y No 15
## 1137 7324 1 Y Yes 17
## 1138 10293 1 Y Yes 14
## 1139 20689 2 Y Yes 15
## 1140 18783 3 Y No 17
## 1141 3549 0 Y Yes 14
## 1142 5348 1 Y No 12
## 1143 23447 1 Y Yes 14
## 1144 24162 1 Y No 15
## 1145 21602 7 Y No 13
## 1146 12421 9 Y Yes 12
## 1147 17000 1 Y No 13
## 1148 22102 1 Y No 14
## 1149 3835 2 Y No 13
## 1150 16290 1 Y No 11
## 1151 26312 1 Y No 11
## 1152 20460 0 Y No 21
## 1153 26009 1 Y No 18
## 1154 18420 1 Y Yes 12
## 1155 5220 3 Y No 11
## 1156 10302 0 Y No 15
## 1157 25800 1 Y No 13
## 1158 12250 1 Y No 12
## 1159 26493 3 Y No 17
## 1160 3140 0 Y No 13
## 1161 5388 1 Y No 19
## 1162 14199 3 Y Yes 19
## 1163 21530 9 Y No 17
## 1164 9953 1 Y No 11
## 1165 19948 6 Y Yes 15
## 1166 10503 4 Y Yes 11
## 1167 5602 2 Y No 25
## 1168 22098 6 Y Yes 14
## 1169 17218 1 Y Yes 13
## 1170 22490 7 Y No 17
## 1171 6297 2 Y No 13
## 1172 3339 3 Y Yes 14
## 1173 23413 9 Y No 11
## 1174 22792 4 Y No 12
## 1175 4973 1 Y No 17
## 1176 7693 4 Y No 21
## 1177 3498 3 Y No 16
## 1178 21981 5 Y No 15
## 1179 13251 1 Y No 19
## 1180 19332 1 Y No 12
## 1181 10950 2 Y No 11
## 1182 11652 2 Y Yes 19
## 1183 15411 0 Y No 15
## 1184 26308 6 Y No 20
## 1185 18685 3 Y No 25
## 1186 3525 1 Y No 24
## 1187 10414 3 Y Yes 24
## 1188 9867 7 Y No 15
## 1189 3356 1 Y Yes 13
## 1190 3921 1 Y No 14
## 1191 25518 0 Y No 13
## 1192 22589 1 Y No 11
## 1193 24941 4 Y Yes 16
## 1194 23826 1 Y No 22
## 1195 21086 6 Y No 14
## 1196 22384 4 Y No 14
## 1197 11591 3 Y Yes 16
## 1198 21082 1 Y No 11
## 1199 16130 1 Y Yes 25
## 1200 7419 6 Y No 14
## 1201 4167 4 Y Yes 18
## 1202 20586 1 Y Yes 11
## 1203 26176 7 Y No 19
## 1204 3458 3 Y No 15
## 1205 11740 2 Y Yes 11
## 1206 24852 1 Y No 12
## 1207 11925 1 Y No 20
## 1208 23737 5 Y No 12
## 1209 11912 1 Y No 14
## 1210 20467 3 Y No 20
## 1211 13637 1 Y No 18
## 1212 7677 1 Y No 14
## 1213 20338 1 Y No 12
## 1214 25103 1 Y Yes 21
## 1215 14810 1 Y Yes 19
## 1216 13970 0 Y Yes 14
## 1217 6069 1 Y Yes 17
## 1218 17616 1 Y No 16
## 1219 15869 1 Y No 12
## 1220 25412 9 Y No 17
## 1221 7439 3 Y No 15
## 1222 3395 3 Y No 23
## 1223 11585 1 Y No 11
## 1224 24164 7 Y No 11
## 1225 6217 1 Y No 15
## 1226 17119 1 Y No 11
## 1227 17360 6 Y No 13
## 1228 18103 1 Y No 14
## 1229 20794 6 Y No 19
## 1230 5041 2 Y Yes 16
## 1231 3622 1 Y No 13
## 1232 9697 6 Y No 14
## 1233 5151 2 Y No 16
## 1234 3811 1 Y No 12
## 1235 3536 7 Y No 11
## 1236 5596 4 Y Yes 12
## 1237 8658 5 Y Yes 13
## 1238 12147 6 Y No 15
## 1239 12862 1 Y No 13
## 1240 6670 2 Y No 20
## 1241 8989 1 Y Yes 15
## 1242 3840 3 Y No 13
## 1243 4349 1 Y No 14
## 1244 6595 4 Y No 21
## 1245 26427 0 Y No 16
## 1246 2097 0 Y No 14
## 1247 9732 6 Y No 11
## 1248 20943 1 Y No 19
## 1249 6152 1 Y No 11
## 1250 14630 1 Y No 13
## 1251 23060 8 Y Yes 12
## 1252 3088 2 Y No 11
## 1253 5586 0 Y Yes 14
## 1254 10697 8 Y No 15
## 1255 8191 3 Y No 12
## 1256 10092 2 Y Yes 20
## 1257 15963 4 Y No 14
## 1258 19002 2 Y No 13
## 1259 10007 1 Y No 13
## 1260 9128 3 Y No 16
## 1261 17674 2 Y No 14
## 1262 24539 3 Y Yes 16
## 1263 15587 9 Y Yes 16
## 1264 8952 8 Y No 22
## 1265 19805 8 Y No 12
## 1266 6194 5 Y No 15
## 1267 20520 1 Y No 20
## 1268 12313 1 Y Yes 14
## 1269 22308 4 Y Yes 20
## 1270 5033 0 Y No 13
## 1271 25353 5 Y No 12
## 1272 4567 1 Y No 13
## 1273 16376 1 Y No 11
## 1274 15999 1 Y Yes 17
## 1275 13402 1 Y No 14
## 1276 22984 2 Y No 11
## 1277 8800 1 Y No 14
## 1278 14218 7 Y Yes 17
## 1279 25949 7 Y Yes 13
## 1280 11092 1 Y Yes 12
## 1281 12832 2 Y No 13
## 1282 13492 1 Y Yes 18
## 1283 6076 6 Y No 19
## 1284 5531 1 Y No 11
## 1285 7914 7 Y No 21
## 1286 25166 8 Y No 15
## 1287 25605 4 Y No 17
## 1288 5696 5 Y No 18
## 1289 24442 2 Y No 14
## 1290 7129 5 Y No 13
## 1291 6208 4 Y No 17
## 1292 4992 1 Y No 15
## 1293 19188 2 Y No 18
## 1294 24978 4 Y No 13
## 1295 15530 3 Y No 12
## 1296 26458 0 Y Yes 13
## 1297 2739 9 Y No 14
## 1298 6889 0 Y Yes 11
## 1299 10842 4 Y No 22
## 1300 9060 4 Y No 17
## 1301 22128 1 Y No 21
## 1302 22577 4 Y No 22
## 1303 9696 0 Y No 16
## 1304 19271 8 Y Yes 12
## 1305 10748 2 Y No 12
## 1306 15696 4 Y No 15
## 1307 8277 2 Y No 14
## 1308 12719 1 Y No 25
## 1309 4244 2 Y Yes 20
## 1310 9125 1 Y No 13
## 1311 21624 2 Y Yes 14
## 1312 8018 1 Y No 16
## 1313 21495 0 Y No 17
## 1314 3157 4 Y Yes 15
## 1315 19665 4 Y No 22
## 1316 19573 4 Y Yes 22
## 1317 19246 1 Y No 20
## 1318 19826 0 Y Yes 14
## 1319 7003 1 Y No 13
## 1320 26075 8 Y No 13
## 1321 6161 3 Y No 22
## 1322 5411 4 Y No 12
## 1323 19989 3 Y No 14
## 1324 10494 1 Y No 15
## 1325 21143 8 Y No 17
## 1326 13624 4 Y No 13
## 1327 26186 7 Y Yes 12
## 1328 7739 2 Y No 12
## 1329 7018 1 Y No 21
## 1330 15434 1 Y No 11
## 1331 22539 7 Y No 13
## 1332 13583 4 Y No 12
## 1333 14753 1 Y Yes 24
## 1334 14011 5 Y No 21
## 1335 23844 0 Y No 19
## 1336 5141 8 Y No 14
## 1337 7975 8 Y No 20
## 1338 3692 1 Y No 19
## 1339 16019 1 Y No 13
## 1340 26092 1 Y Yes 23
## 1341 6060 1 Y Yes 13
## 1342 18624 1 Y No 11
## 1343 24444 2 Y Yes 13
## 1344 19384 3 Y No 14
## 1345 13588 5 Y Yes 22
## 1346 25388 0 Y Yes 11
## 1347 23888 0 Y No 13
## 1348 4223 1 Y No 21
## 1349 18991 2 Y No 11
## 1350 14908 1 Y Yes 13
## 1351 26997 0 Y No 14
## 1352 2437 3 Y No 11
## 1353 9364 2 Y No 15
## 1354 14460 1 Y Yes 23
## 1355 10261 1 Y No 16
## 1356 16822 2 Y No 16
## 1357 26841 5 Y No 21
## 1358 14842 9 Y No 13
## 1359 20115 2 Y Yes 11
## 1360 16495 3 Y Yes 12
## 1361 16031 8 Y No 12
## 1362 7102 0 Y No 18
## 1363 14511 4 Y No 12
## 1364 10410 1 Y No 14
## 1365 19255 1 Y Yes 12
## 1366 10642 1 Y No 17
## 1367 3987 6 Y No 19
## 1368 6073 1 Y No 11
## 1369 26496 1 Y Yes 15
## 1370 23352 3 Y Yes 11
## 1371 2125 8 Y No 18
## 1372 20328 4 Y No 16
## 1373 12315 1 Y No 25
## 1374 18115 1 Y Yes 16
## 1375 11761 4 Y Yes 13
## 1376 15318 3 Y Yes 14
## 1377 14293 2 Y No 19
## 1378 13738 3 Y No 15
## 1379 2900 3 Y Yes 12
## 1380 19555 1 Y No 12
## 1381 15975 0 Y No 16
## 1382 2122 1 Y No 14
## 1383 3995 1 Y Yes 13
## 1384 9238 1 Y No 22
## 1385 6770 1 Y No 21
## 1386 22914 1 Y No 18
## 1387 20006 0 Y No 13
## 1388 6225 0 Y No 12
## 1389 16542 5 Y No 18
## 1390 5771 1 Y No 21
## 1391 18779 5 Y No 12
## 1392 11473 4 Y No 14
## 1393 13586 1 Y Yes 11
## 1394 5099 1 Y No 14
## 1395 10138 8 Y No 24
## 1396 21075 1 Y Yes 11
## 1397 5843 6 Y Yes 13
## 1398 22474 3 Y No 11
## 1399 18079 1 Y No 20
## 1400 16873 1 Y No 17
## 1401 5224 0 Y Yes 11
## 1402 25811 4 Y Yes 18
## 1403 17536 1 Y Yes 11
## 1404 25098 0 Y No 12
## 1405 14811 1 Y No 12
## 1406 26862 4 Y No 20
## 1407 25198 6 Y Yes 19
## 1408 14120 1 Y No 12
## 1409 13672 1 Y No 13
## 1410 26849 1 Y No 11
## 1411 4258 3 Y No 14
## 1412 19655 4 Y No 14
## 1413 4077 1 Y No 13
## 1414 7298 6 Y Yes 19
## 1415 13084 2 Y No 23
## 1416 20439 1 Y No 12
## 1417 7636 0 Y No 15
## 1418 6393 0 Y No 19
## 1419 14284 2 Y No 11
## 1420 11189 6 Y Yes 17
## 1421 21412 3 Y No 22
## 1422 17231 0 Y No 18
## 1423 20232 7 Y Yes 11
## 1424 17624 0 Y No 12
## 1425 18698 1 Y No 19
## 1426 21653 0 Y Yes 13
## 1427 15919 1 Y No 13
## 1428 4060 8 Y No 19
## 1429 18024 0 Y No 14
## 1430 5340 7 Y No 13
## 1431 3376 3 Y No 12
## 1432 24032 1 Y No 19
## 1433 15471 1 Y Yes 25
## 1434 13684 0 Y Yes 22
## 1435 19788 2 Y No 15
## 1436 13422 6 Y Yes 12
## 1437 25479 1 Y Yes 11
## 1438 15302 2 Y No 13
## 1439 26956 1 Y No 19
## 1440 12695 0 Y No 19
## 1441 9192 7 Y No 13
## 1442 26236 1 Y No 21
## 1443 26124 9 Y Yes 14
## 1444 17312 5 Y No 11
## 1445 3666 8 Y No 11
## 1446 5640 0 Y No 23
## 1447 8978 1 Y No 21
## 1448 10436 1 Y No 24
## 1449 12227 2 Y No 11
## 1450 11288 1 Y No 14
## 1451 16642 1 Y Yes 16
## 1452 5982 1 Y No 11
## 1453 14255 7 Y No 12
## 1454 14369 4 Y No 13
## 1455 23333 8 Y No 15
## 1456 2725 2 Y No 14
## 1457 24594 1 Y Yes 14
## 1458 12549 2 Y No 14
## 1459 8952 1 Y No 12
## 1460 23679 4 Y Yes 13
## 1461 8489 1 Y No 14
## 1462 16586 4 Y Yes 13
## 1463 8828 0 Y No 11
## 1464 3787 0 Y No 19
## 1465 21378 0 Y No 18
## 1466 12290 4 Y No 17
## 1467 21457 4 Y No 15
## 1468 5174 1 Y Yes 20
## 1469 13243 2 Y No 14
## 1470 10228 2 Y No 12
## PerformanceRating RelationshipSatisfaction StandardHours StockOptionLevel
## 1 3 1 80 0
## 2 4 4 80 1
## 3 3 2 80 0
## 4 3 3 80 0
## 5 3 4 80 1
## 6 3 3 80 0
## 7 4 1 80 3
## 8 4 2 80 1
## 9 4 2 80 0
## 10 3 2 80 2
## 11 3 3 80 1
## 12 3 4 80 0
## 13 3 4 80 1
## 14 3 3 80 1
## 15 3 2 80 0
## 16 3 3 80 1
## 17 3 4 80 2
## 18 3 2 80 2
## 19 3 3 80 0
## 20 3 3 80 0
## 21 3 4 80 1
## 22 4 2 80 0
## 23 3 3 80 0
## 24 3 4 80 0
## 25 3 3 80 0
## 26 3 4 80 1
## 27 4 2 80 0
## 28 3 4 80 1
## 29 3 4 80 1
## 30 3 4 80 0
## 31 3 4 80 0
## 32 3 4 80 0
## 33 3 1 80 0
## 34 3 3 80 1
## 35 3 1 80 1
## 36 3 4 80 2
## 37 3 3 80 0
## 38 3 1 80 0
## 39 3 4 80 1
## 40 3 1 80 2
## 41 3 3 80 1
## 42 3 4 80 1
## 43 3 3 80 0
## 44 3 4 80 0
## 45 4 4 80 0
## 46 3 4 80 0
## 47 4 3 80 0
## 48 4 1 80 0
## 49 4 3 80 0
## 50 3 4 80 0
## 51 3 4 80 0
## 52 3 3 80 0
## 53 4 3 80 1
## 54 3 3 80 1
## 55 3 3 80 1
## 56 3 3 80 0
## 57 4 4 80 1
## 58 3 3 80 1
## 59 4 4 80 1
## 60 3 3 80 1
## 61 4 2 80 1
## 62 3 4 80 0
## 63 3 4 80 1
## 64 3 4 80 0
## 65 3 2 80 3
## 66 3 3 80 3
## 67 3 3 80 0
## 68 3 3 80 1
## 69 3 4 80 1
## 70 3 1 80 1
## 71 3 4 80 0
## 72 4 4 80 1
## 73 3 2 80 0
## 74 3 2 80 2
## 75 3 2 80 0
## 76 4 4 80 0
## 77 3 2 80 0
## 78 3 2 80 0
## 79 3 1 80 0
## 80 4 4 80 1
## 81 3 3 80 2
## 82 3 1 80 0
## 83 3 4 80 1
## 84 3 4 80 3
## 85 3 3 80 0
## 86 3 1 80 0
## 87 3 3 80 1
## 88 4 2 80 2
## 89 3 1 80 3
## 90 3 4 80 0
## 91 4 4 80 1
## 92 4 4 80 0
## 93 3 2 80 3
## 94 3 3 80 1
## 95 3 1 80 0
## 96 3 1 80 1
## 97 4 1 80 1
## 98 3 2 80 0
## 99 3 3 80 0
## 100 3 3 80 1
## 101 4 4 80 0
## 102 3 4 80 0
## 103 3 2 80 0
## 104 3 3 80 0
## 105 3 4 80 1
## 106 4 4 80 1
## 107 3 1 80 0
## 108 3 4 80 0
## 109 3 3 80 2
## 110 3 3 80 0
## 111 4 3 80 0
## 112 4 4 80 0
## 113 3 3 80 0
## 114 3 3 80 1
## 115 3 3 80 1
## 116 3 1 80 0
## 117 3 4 80 0
## 118 3 3 80 1
## 119 4 1 80 1
## 120 3 4 80 2
## 121 4 3 80 3
## 122 3 1 80 1
## 123 3 1 80 3
## 124 3 3 80 0
## 125 3 2 80 0
## 126 3 3 80 0
## 127 3 4 80 1
## 128 3 4 80 0
## 129 3 3 80 1
## 130 3 3 80 0
## 131 3 4 80 0
## 132 3 4 80 0
## 133 3 3 80 1
## 134 3 3 80 1
## 135 4 4 80 1
## 136 4 4 80 2
## 137 3 4 80 0
## 138 3 3 80 1
## 139 3 4 80 0
## 140 3 4 80 0
## 141 4 1 80 0
## 142 3 2 80 0
## 143 4 2 80 0
## 144 3 3 80 0
## 145 3 4 80 3
## 146 3 4 80 1
## 147 3 4 80 0
## 148 3 2 80 1
## 149 4 4 80 1
## 150 3 4 80 0
## 151 3 1 80 1
## 152 3 1 80 2
## 153 4 4 80 1
## 154 3 3 80 1
## 155 3 4 80 0
## 156 3 1 80 1
## 157 3 3 80 0
## 158 4 2 80 1
## 159 3 1 80 1
## 160 3 4 80 1
## 161 4 1 80 2
## 162 3 4 80 1
## 163 3 3 80 1
## 164 3 2 80 1
## 165 3 4 80 1
## 166 3 2 80 0
## 167 3 1 80 1
## 168 3 3 80 1
## 169 4 3 80 0
## 170 4 1 80 0
## 171 3 4 80 1
## 172 4 1 80 0
## 173 3 3 80 0
## 174 3 3 80 2
## 175 3 1 80 1
## 176 3 3 80 1
## 177 3 1 80 0
## 178 4 3 80 0
## 179 4 3 80 3
## 180 3 3 80 0
## 181 4 3 80 1
## 182 3 4 80 0
## 183 4 4 80 0
## 184 3 4 80 1
## 185 3 3 80 2
## 186 3 4 80 1
## 187 3 2 80 1
## 188 3 4 80 0
## 189 3 3 80 0
## 190 3 3 80 0
## 191 3 1 80 1
## 192 3 4 80 0
## 193 3 1 80 0
## 194 3 4 80 3
## 195 4 4 80 1
## 196 3 1 80 1
## 197 3 4 80 0
## 198 3 2 80 1
## 199 3 2 80 0
## 200 3 1 80 1
## 201 3 3 80 1
## 202 3 1 80 2
## 203 3 4 80 1
## 204 3 3 80 1
## 205 3 2 80 0
## 206 4 4 80 3
## 207 3 1 80 1
## 208 3 1 80 0
## 209 3 4 80 1
## 210 3 3 80 1
## 211 3 3 80 0
## 212 4 3 80 0
## 213 3 4 80 0
## 214 3 4 80 2
## 215 3 3 80 0
## 216 3 3 80 0
## 217 3 3 80 0
## 218 3 4 80 0
## 219 3 4 80 0
## 220 3 4 80 1
## 221 3 4 80 0
## 222 4 4 80 0
## 223 3 3 80 3
## 224 3 3 80 2
## 225 3 4 80 1
## 226 3 1 80 1
## 227 3 3 80 1
## 228 3 4 80 1
## 229 3 1 80 0
## 230 3 3 80 0
## 231 3 2 80 0
## 232 3 4 80 0
## 233 3 4 80 0
## 234 3 3 80 1
## 235 3 3 80 1
## 236 4 3 80 1
## 237 4 1 80 0
## 238 3 4 80 0
## 239 3 1 80 1
## 240 3 4 80 0
## 241 3 3 80 3
## 242 3 1 80 0
## 243 3 1 80 2
## 244 3 1 80 2
## 245 3 3 80 1
## 246 3 1 80 1
## 247 3 3 80 1
## 248 3 2 80 1
## 249 3 1 80 1
## 250 3 4 80 1
## 251 3 2 80 2
## 252 4 4 80 0
## 253 3 1 80 0
## 254 3 3 80 0
## 255 3 4 80 1
## 256 3 4 80 2
## 257 3 3 80 1
## 258 3 4 80 1
## 259 3 2 80 0
## 260 3 2 80 0
## 261 4 3 80 0
## 262 3 4 80 1
## 263 3 4 80 0
## 264 3 2 80 1
## 265 3 3 80 0
## 266 3 2 80 2
## 267 4 2 80 1
## 268 3 4 80 2
## 269 3 2 80 0
## 270 3 3 80 1
## 271 3 3 80 0
## 272 3 4 80 1
## 273 4 4 80 1
## 274 3 2 80 1
## 275 3 1 80 0
## 276 3 1 80 2
## 277 3 2 80 1
## 278 4 3 80 1
## 279 4 1 80 1
## 280 3 1 80 2
## 281 3 4 80 3
## 282 4 3 80 0
## 283 3 1 80 0
## 284 3 4 80 1
## 285 3 3 80 1
## 286 3 2 80 0
## 287 4 4 80 1
## 288 3 2 80 1
## 289 3 4 80 1
## 290 4 4 80 0
## 291 3 4 80 0
## 292 3 4 80 0
## 293 3 3 80 1
## 294 3 2 80 0
## 295 3 3 80 3
## 296 3 4 80 1
## 297 3 3 80 0
## 298 3 3 80 2
## 299 3 4 80 2
## 300 3 4 80 1
## 301 3 2 80 0
## 302 3 1 80 0
## 303 3 3 80 0
## 304 3 2 80 1
## 305 3 1 80 3
## 306 3 3 80 1
## 307 3 3 80 1
## 308 3 3 80 1
## 309 3 4 80 1
## 310 3 3 80 1
## 311 3 4 80 0
## 312 3 1 80 1
## 313 3 2 80 1
## 314 3 2 80 2
## 315 3 4 80 0
## 316 3 1 80 0
## 317 3 4 80 0
## 318 3 1 80 0
## 319 3 2 80 0
## 320 3 4 80 0
## 321 3 1 80 0
## 322 3 4 80 3
## 323 3 3 80 0
## 324 3 4 80 0
## 325 3 4 80 2
## 326 4 1 80 1
## 327 3 1 80 1
## 328 3 1 80 0
## 329 3 3 80 0
## 330 3 2 80 1
## 331 3 2 80 1
## 332 3 4 80 1
## 333 3 4 80 0
## 334 3 1 80 1
## 335 3 1 80 2
## 336 4 3 80 3
## 337 3 4 80 0
## 338 3 3 80 0
## 339 3 3 80 3
## 340 3 1 80 1
## 341 3 3 80 2
## 342 3 2 80 1
## 343 3 3 80 0
## 344 3 1 80 2
## 345 3 4 80 0
## 346 3 3 80 2
## 347 3 4 80 0
## 348 3 1 80 0
## 349 3 2 80 0
## 350 3 1 80 1
## 351 3 3 80 1
## 352 3 3 80 1
## 353 4 2 80 1
## 354 3 1 80 2
## 355 3 2 80 1
## 356 3 2 80 1
## 357 4 2 80 0
## 358 3 3 80 0
## 359 3 2 80 0
## 360 3 1 80 1
## 361 3 3 80 0
## 362 3 3 80 1
## 363 4 3 80 0
## 364 3 2 80 0
## 365 4 2 80 1
## 366 3 3 80 0
## 367 3 3 80 0
## 368 3 4 80 0
## 369 3 1 80 2
## 370 3 4 80 0
## 371 3 4 80 0
## 372 3 4 80 0
## 373 3 3 80 0
## 374 3 2 80 1
## 375 3 4 80 0
## 376 4 3 80 0
## 377 3 3 80 1
## 378 3 2 80 1
## 379 3 2 80 0
## 380 3 3 80 0
## 381 3 4 80 1
## 382 3 1 80 1
## 383 4 3 80 0
## 384 3 4 80 1
## 385 3 2 80 2
## 386 4 3 80 0
## 387 3 3 80 1
## 388 3 3 80 2
## 389 3 2 80 1
## 390 3 4 80 0
## 391 3 3 80 1
## 392 4 2 80 1
## 393 3 3 80 1
## 394 3 1 80 1
## 395 3 2 80 1
## 396 4 1 80 1
## 397 3 4 80 0
## 398 3 2 80 0
## 399 3 1 80 2
## 400 3 3 80 1
## 401 3 3 80 1
## 402 3 4 80 3
## 403 3 2 80 0
## 404 3 3 80 1
## 405 3 4 80 1
## 406 3 3 80 1
## 407 3 3 80 0
## 408 4 4 80 2
## 409 3 4 80 0
## 410 3 2 80 1
## 411 4 3 80 0
## 412 3 4 80 0
## 413 3 3 80 1
## 414 3 4 80 3
## 415 3 2 80 0
## 416 3 4 80 1
## 417 4 3 80 1
## 418 3 4 80 0
## 419 4 2 80 2
## 420 3 3 80 1
## 421 3 3 80 0
## 422 3 2 80 0
## 423 3 3 80 0
## 424 3 3 80 0
## 425 3 3 80 1
## 426 3 2 80 1
## 427 3 3 80 0
## 428 3 4 80 0
## 429 3 3 80 3
## 430 3 3 80 0
## 431 3 3 80 0
## 432 3 3 80 0
## 433 3 3 80 1
## 434 3 3 80 1
## 435 4 4 80 1
## 436 3 4 80 0
## 437 3 1 80 3
## 438 3 1 80 0
## 439 3 3 80 0
## 440 3 1 80 0
## 441 3 3 80 3
## 442 3 4 80 1
## 443 3 4 80 0
## 444 3 3 80 0
## 445 3 1 80 1
## 446 4 4 80 0
## 447 3 4 80 0
## 448 3 3 80 0
## 449 3 3 80 0
## 450 3 1 80 1
## 451 3 3 80 0
## 452 4 4 80 1
## 453 3 2 80 1
## 454 3 2 80 1
## 455 3 2 80 2
## 456 3 3 80 1
## 457 4 3 80 1
## 458 3 4 80 0
## 459 3 3 80 1
## 460 3 1 80 0
## 461 3 1 80 2
## 462 3 4 80 0
## 463 3 4 80 0
## 464 3 2 80 0
## 465 3 4 80 0
## 466 3 4 80 0
## 467 3 2 80 1
## 468 3 4 80 1
## 469 3 2 80 2
## 470 3 4 80 0
## 471 3 3 80 2
## 472 3 3 80 1
## 473 3 2 80 1
## 474 3 3 80 1
## 475 3 2 80 2
## 476 4 4 80 2
## 477 4 4 80 1
## 478 3 3 80 1
## 479 3 3 80 0
## 480 3 4 80 1
## 481 3 3 80 1
## 482 3 4 80 1
## 483 3 3 80 0
## 484 3 4 80 0
## 485 4 4 80 2
## 486 3 3 80 2
## 487 3 3 80 1
## 488 3 4 80 0
## 489 3 2 80 2
## 490 3 3 80 1
## 491 3 4 80 0
## 492 3 2 80 1
## 493 3 1 80 1
## 494 3 2 80 0
## 495 3 4 80 2
## 496 3 2 80 1
## 497 3 3 80 0
## 498 3 1 80 1
## 499 4 4 80 0
## 500 3 4 80 0
## 501 3 4 80 1
## 502 4 3 80 1
## 503 4 4 80 0
## 504 3 4 80 1
## 505 3 4 80 2
## 506 3 3 80 1
## 507 3 3 80 1
## 508 3 1 80 1
## 509 3 2 80 0
## 510 4 3 80 1
## 511 3 3 80 1
## 512 3 3 80 1
## 513 3 4 80 0
## 514 3 4 80 0
## 515 3 1 80 0
## 516 3 3 80 2
## 517 3 1 80 1
## 518 4 3 80 1
## 519 4 1 80 0
## 520 3 4 80 1
## 521 3 4 80 0
## 522 4 2 80 2
## 523 3 1 80 0
## 524 3 3 80 3
## 525 3 2 80 0
## 526 3 1 80 0
## 527 3 1 80 0
## 528 3 4 80 0
## 529 3 1 80 1
## 530 3 3 80 0
## 531 3 4 80 0
## 532 3 4 80 0
## 533 3 4 80 0
## 534 4 3 80 1
## 535 3 3 80 0
## 536 3 2 80 3
## 537 3 4 80 0
## 538 4 3 80 2
## 539 3 2 80 1
## 540 3 4 80 1
## 541 3 4 80 0
## 542 3 1 80 1
## 543 3 4 80 0
## 544 3 3 80 0
## 545 3 4 80 2
## 546 4 4 80 1
## 547 3 3 80 0
## 548 3 4 80 0
## 549 3 3 80 1
## 550 3 4 80 0
## 551 3 4 80 1
## 552 3 3 80 1
## 553 3 3 80 0
## 554 4 4 80 0
## 555 3 1 80 0
## 556 3 4 80 2
## 557 3 4 80 0
## 558 3 1 80 1
## 559 3 4 80 2
## 560 3 2 80 1
## 561 3 3 80 1
## 562 3 1 80 0
## 563 3 3 80 0
## 564 4 2 80 0
## 565 3 1 80 0
## 566 3 4 80 0
## 567 3 4 80 0
## 568 4 3 80 0
## 569 3 4 80 1
## 570 3 2 80 0
## 571 3 1 80 1
## 572 3 1 80 1
## 573 3 1 80 1
## 574 3 3 80 0
## 575 3 3 80 0
## 576 3 2 80 2
## 577 3 2 80 1
## 578 3 2 80 2
## 579 3 4 80 0
## 580 3 3 80 0
## 581 3 2 80 1
## 582 4 3 80 2
## 583 4 4 80 1
## 584 3 2 80 1
## 585 3 2 80 1
## 586 4 3 80 2
## 587 3 3 80 3
## 588 4 2 80 1
## 589 3 4 80 0
## 590 3 4 80 1
## 591 3 4 80 0
## 592 3 3 80 0
## 593 3 3 80 1
## 594 3 1 80 1
## 595 4 3 80 1
## 596 3 4 80 0
## 597 3 3 80 0
## 598 3 4 80 1
## 599 3 4 80 0
## 600 3 2 80 1
## 601 3 3 80 1
## 602 3 4 80 0
## 603 4 2 80 0
## 604 3 4 80 0
## 605 3 4 80 1
## 606 3 3 80 1
## 607 3 3 80 0
## 608 3 1 80 2
## 609 3 3 80 0
## 610 4 3 80 1
## 611 3 2 80 1
## 612 4 2 80 0
## 613 4 1 80 0
## 614 3 3 80 1
## 615 3 1 80 1
## 616 3 3 80 3
## 617 3 3 80 1
## 618 3 4 80 0
## 619 3 3 80 0
## 620 4 1 80 1
## 621 3 4 80 0
## 622 3 4 80 1
## 623 3 3 80 1
## 624 3 2 80 1
## 625 3 4 80 1
## 626 3 3 80 1
## 627 3 3 80 1
## 628 4 3 80 0
## 629 3 4 80 2
## 630 3 4 80 1
## 631 4 1 80 2
## 632 4 3 80 1
## 633 3 4 80 0
## 634 4 3 80 0
## 635 3 4 80 0
## 636 4 2 80 1
## 637 3 1 80 1
## 638 3 2 80 1
## 639 3 1 80 0
## 640 3 2 80 1
## 641 3 4 80 0
## 642 3 4 80 1
## 643 3 4 80 1
## 644 3 2 80 1
## 645 3 2 80 1
## 646 3 3 80 3
## 647 3 3 80 1
## 648 3 1 80 0
## 649 3 2 80 1
## 650 3 3 80 0
## 651 3 2 80 1
## 652 4 1 80 1
## 653 3 4 80 0
## 654 3 4 80 1
## 655 3 3 80 2
## 656 3 3 80 1
## 657 4 3 80 0
## 658 3 3 80 3
## 659 3 4 80 0
## 660 3 2 80 0
## 661 3 4 80 1
## 662 4 3 80 1
## 663 3 4 80 0
## 664 3 1 80 0
## 665 3 1 80 1
## 666 3 1 80 0
## 667 3 1 80 1
## 668 3 3 80 1
## 669 3 2 80 1
## 670 4 4 80 0
## 671 3 3 80 0
## 672 3 2 80 2
## 673 3 1 80 0
## 674 3 2 80 0
## 675 3 4 80 1
## 676 3 2 80 0
## 677 4 4 80 1
## 678 3 3 80 1
## 679 3 1 80 2
## 680 3 4 80 1
## 681 3 3 80 0
## 682 3 2 80 1
## 683 4 3 80 0
## 684 3 3 80 3
## 685 3 2 80 1
## 686 3 2 80 0
## 687 3 3 80 0
## 688 4 3 80 0
## 689 3 2 80 0
## 690 3 2 80 0
## 691 3 3 80 2
## 692 3 4 80 1
## 693 3 3 80 1
## 694 3 1 80 1
## 695 3 1 80 0
## 696 3 3 80 0
## 697 3 2 80 0
## 698 3 2 80 1
## 699 3 2 80 0
## 700 3 2 80 1
## 701 4 1 80 0
## 702 3 3 80 1
## 703 3 1 80 1
## 704 3 2 80 0
## 705 3 2 80 1
## 706 3 4 80 0
## 707 3 4 80 0
## 708 3 3 80 0
## 709 3 4 80 2
## 710 4 1 80 0
## 711 3 4 80 0
## 712 4 3 80 0
## 713 3 1 80 0
## 714 3 4 80 2
## 715 4 3 80 1
## 716 3 1 80 1
## 717 3 2 80 1
## 718 3 2 80 1
## 719 3 3 80 1
## 720 3 3 80 0
## 721 3 2 80 0
## 722 3 4 80 1
## 723 3 2 80 1
## 724 3 2 80 1
## 725 3 2 80 2
## 726 4 4 80 1
## 727 3 3 80 1
## 728 3 4 80 0
## 729 3 3 80 1
## 730 3 3 80 1
## 731 3 3 80 3
## 732 3 1 80 0
## 733 3 1 80 0
## 734 3 2 80 0
## 735 3 1 80 1
## 736 3 4 80 0
## 737 3 2 80 0
## 738 3 2 80 0
## 739 3 3 80 1
## 740 3 2 80 1
## 741 4 1 80 1
## 742 3 2 80 0
## 743 3 2 80 0
## 744 3 1 80 0
## 745 3 1 80 0
## 746 3 4 80 2
## 747 4 2 80 2
## 748 3 3 80 0
## 749 3 4 80 0
## 750 3 4 80 1
## 751 3 3 80 1
## 752 3 1 80 1
## 753 3 3 80 0
## 754 3 3 80 0
## 755 3 4 80 0
## 756 3 2 80 1
## 757 3 2 80 0
## 758 3 4 80 1
## 759 3 3 80 1
## 760 3 1 80 0
## 761 3 1 80 1
## 762 3 2 80 1
## 763 3 2 80 1
## 764 3 4 80 1
## 765 4 2 80 0
## 766 3 1 80 1
## 767 3 4 80 1
## 768 3 1 80 0
## 769 3 2 80 1
## 770 3 3 80 2
## 771 3 4 80 2
## 772 3 2 80 1
## 773 3 2 80 1
## 774 3 2 80 0
## 775 3 2 80 0
## 776 3 3 80 1
## 777 3 2 80 0
## 778 3 1 80 0
## 779 4 1 80 3
## 780 3 3 80 3
## 781 3 1 80 0
## 782 3 1 80 2
## 783 3 3 80 1
## 784 3 3 80 0
## 785 3 1 80 1
## 786 4 4 80 1
## 787 3 4 80 3
## 788 3 2 80 1
## 789 3 4 80 0
## 790 3 4 80 1
## 791 3 3 80 1
## 792 4 1 80 0
## 793 4 2 80 0
## 794 3 4 80 1
## 795 3 3 80 0
## 796 3 3 80 3
## 797 3 2 80 1
## 798 4 3 80 1
## 799 4 2 80 0
## 800 3 4 80 1
## 801 3 1 80 2
## 802 3 4 80 0
## 803 3 3 80 1
## 804 3 4 80 3
## 805 4 3 80 0
## 806 3 4 80 1
## 807 3 4 80 0
## 808 3 2 80 2
## 809 4 1 80 1
## 810 3 2 80 3
## 811 3 4 80 1
## 812 3 3 80 0
## 813 3 3 80 1
## 814 3 4 80 3
## 815 3 1 80 0
## 816 3 3 80 0
## 817 3 3 80 0
## 818 4 1 80 0
## 819 3 1 80 1
## 820 3 1 80 0
## 821 3 4 80 1
## 822 3 2 80 1
## 823 3 4 80 0
## 824 3 4 80 2
## 825 3 1 80 0
## 826 3 3 80 1
## 827 3 4 80 1
## 828 3 4 80 1
## 829 3 4 80 0
## 830 3 1 80 0
## 831 3 1 80 1
## 832 3 3 80 1
## 833 3 3 80 2
## 834 3 3 80 1
## 835 4 1 80 0
## 836 3 2 80 0
## 837 3 1 80 1
## 838 3 3 80 0
## 839 3 2 80 0
## 840 3 4 80 0
## 841 3 2 80 1
## 842 4 4 80 0
## 843 3 4 80 0
## 844 4 2 80 1
## 845 3 1 80 1
## 846 3 4 80 1
## 847 3 2 80 1
## 848 4 3 80 0
## 849 3 2 80 1
## 850 3 2 80 0
## 851 3 3 80 3
## 852 3 4 80 1
## 853 3 1 80 1
## 854 4 3 80 0
## 855 3 3 80 1
## 856 3 2 80 1
## 857 3 1 80 0
## 858 3 3 80 0
## 859 3 2 80 1
## 860 3 1 80 1
## 861 3 2 80 1
## 862 4 1 80 0
## 863 3 4 80 0
## 864 3 4 80 1
## 865 3 1 80 1
## 866 3 3 80 3
## 867 3 4 80 3
## 868 4 3 80 1
## 869 3 3 80 3
## 870 3 1 80 1
## 871 3 4 80 3
## 872 3 1 80 1
## 873 3 1 80 1
## 874 3 3 80 1
## 875 3 1 80 3
## 876 4 2 80 0
## 877 3 4 80 0
## 878 3 3 80 1
## 879 3 1 80 1
## 880 3 2 80 1
## 881 4 3 80 1
## 882 3 4 80 0
## 883 4 3 80 1
## 884 3 2 80 1
## 885 3 2 80 1
## 886 3 2 80 0
## 887 4 1 80 1
## 888 3 3 80 0
## 889 3 2 80 1
## 890 3 4 80 2
## 891 3 1 80 1
## 892 3 4 80 1
## 893 4 2 80 0
## 894 3 1 80 3
## 895 3 1 80 0
## 896 3 4 80 0
## 897 3 2 80 0
## 898 3 4 80 0
## 899 3 2 80 1
## 900 3 3 80 1
## 901 3 3 80 0
## 902 3 3 80 0
## 903 3 2 80 3
## 904 3 2 80 2
## 905 3 3 80 0
## 906 3 2 80 2
## 907 3 4 80 0
## 908 3 3 80 1
## 909 3 4 80 2
## 910 3 4 80 0
## 911 4 4 80 1
## 912 3 4 80 0
## 913 4 2 80 0
## 914 3 1 80 0
## 915 3 4 80 1
## 916 4 3 80 0
## 917 3 3 80 1
## 918 3 4 80 0
## 919 4 1 80 1
## 920 3 4 80 0
## 921 3 3 80 2
## 922 3 3 80 0
## 923 3 4 80 2
## 924 3 4 80 2
## 925 3 4 80 0
## 926 3 4 80 0
## 927 3 4 80 0
## 928 3 4 80 0
## 929 3 4 80 1
## 930 3 2 80 1
## 931 3 2 80 0
## 932 3 3 80 0
## 933 3 1 80 2
## 934 3 2 80 0
## 935 3 4 80 0
## 936 3 3 80 2
## 937 4 3 80 0
## 938 3 4 80 2
## 939 3 4 80 2
## 940 3 1 80 1
## 941 3 1 80 0
## 942 3 1 80 1
## 943 3 1 80 0
## 944 3 3 80 0
## 945 3 1 80 3
## 946 3 2 80 0
## 947 3 3 80 0
## 948 3 3 80 0
## 949 4 4 80 2
## 950 3 1 80 0
## 951 3 1 80 1
## 952 3 1 80 0
## 953 3 1 80 0
## 954 3 3 80 0
## 955 3 4 80 0
## 956 3 4 80 1
## 957 3 1 80 0
## 958 3 2 80 1
## 959 3 4 80 1
## 960 3 3 80 0
## 961 3 4 80 1
## 962 3 2 80 0
## 963 3 2 80 1
## 964 3 4 80 1
## 965 3 4 80 0
## 966 3 3 80 3
## 967 3 4 80 0
## 968 4 3 80 1
## 969 3 4 80 1
## 970 3 3 80 0
## 971 3 2 80 1
## 972 3 2 80 0
## 973 3 3 80 0
## 974 3 2 80 1
## 975 4 2 80 0
## 976 3 3 80 0
## 977 3 1 80 1
## 978 4 3 80 3
## 979 4 2 80 3
## 980 3 1 80 2
## 981 3 3 80 0
## 982 3 3 80 1
## 983 3 4 80 3
## 984 3 4 80 0
## 985 3 3 80 1
## 986 3 4 80 2
## 987 3 4 80 2
## 988 3 2 80 0
## 989 3 1 80 2
## 990 3 4 80 0
## 991 3 1 80 0
## 992 3 1 80 3
## 993 4 2 80 1
## 994 3 2 80 0
## 995 3 2 80 1
## 996 3 1 80 0
## 997 3 4 80 0
## 998 3 4 80 0
## 999 3 4 80 0
## 1000 3 3 80 1
## 1001 3 3 80 0
## 1002 3 1 80 0
## 1003 3 3 80 0
## 1004 3 2 80 1
## 1005 3 4 80 0
## 1006 3 1 80 0
## 1007 4 1 80 0
## 1008 3 1 80 0
## 1009 3 4 80 0
## 1010 4 3 80 1
## 1011 3 4 80 2
## 1012 3 4 80 0
## 1013 3 2 80 0
## 1014 3 2 80 2
## 1015 3 3 80 0
## 1016 3 3 80 2
## 1017 3 3 80 0
## 1018 3 2 80 0
## 1019 3 3 80 0
## 1020 3 1 80 2
## 1021 3 3 80 1
## 1022 3 1 80 0
## 1023 3 1 80 0
## 1024 4 4 80 1
## 1025 3 2 80 2
## 1026 3 3 80 2
## 1027 3 3 80 1
## 1028 3 1 80 1
## 1029 3 1 80 0
## 1030 3 3 80 2
## 1031 3 1 80 1
## 1032 3 1 80 1
## 1033 4 2 80 0
## 1034 3 1 80 0
## 1035 3 2 80 1
## 1036 3 4 80 0
## 1037 3 3 80 1
## 1038 3 4 80 2
## 1039 3 1 80 3
## 1040 3 4 80 0
## 1041 3 3 80 1
## 1042 3 4 80 0
## 1043 3 4 80 0
## 1044 3 2 80 0
## 1045 3 2 80 1
## 1046 3 3 80 1
## 1047 3 3 80 0
## 1048 3 1 80 2
## 1049 3 4 80 0
## 1050 3 3 80 2
## 1051 3 3 80 0
## 1052 3 3 80 1
## 1053 3 2 80 2
## 1054 4 1 80 1
## 1055 3 2 80 2
## 1056 3 4 80 2
## 1057 3 4 80 1
## 1058 3 1 80 0
## 1059 4 3 80 0
## 1060 3 1 80 1
## 1061 3 3 80 0
## 1062 3 3 80 1
## 1063 3 2 80 0
## 1064 3 3 80 2
## 1065 4 1 80 1
## 1066 3 2 80 0
## 1067 3 4 80 0
## 1068 3 3 80 1
## 1069 3 3 80 0
## 1070 3 4 80 1
## 1071 3 4 80 0
## 1072 4 1 80 1
## 1073 3 3 80 3
## 1074 3 2 80 2
## 1075 3 1 80 0
## 1076 4 2 80 0
## 1077 4 1 80 1
## 1078 3 3 80 0
## 1079 3 3 80 1
## 1080 3 4 80 0
## 1081 3 4 80 1
## 1082 3 4 80 0
## 1083 3 2 80 0
## 1084 3 2 80 0
## 1085 3 4 80 0
## 1086 3 1 80 0
## 1087 3 4 80 0
## 1088 4 2 80 1
## 1089 3 2 80 1
## 1090 3 1 80 0
## 1091 3 3 80 1
## 1092 3 4 80 0
## 1093 4 4 80 1
## 1094 3 3 80 1
## 1095 3 4 80 0
## 1096 3 2 80 1
## 1097 4 4 80 0
## 1098 3 2 80 3
## 1099 3 3 80 0
## 1100 3 1 80 3
## 1101 4 1 80 2
## 1102 3 1 80 1
## 1103 4 4 80 0
## 1104 3 3 80 1
## 1105 3 1 80 2
## 1106 3 4 80 1
## 1107 3 4 80 1
## 1108 3 3 80 0
## 1109 3 2 80 0
## 1110 3 2 80 1
## 1111 3 4 80 1
## 1112 3 2 80 1
## 1113 3 1 80 2
## 1114 3 2 80 1
## 1115 3 4 80 1
## 1116 3 3 80 0
## 1117 4 3 80 1
## 1118 3 2 80 1
## 1119 4 1 80 0
## 1120 3 4 80 1
## 1121 3 2 80 0
## 1122 3 1 80 0
## 1123 3 4 80 0
## 1124 3 3 80 0
## 1125 3 1 80 1
## 1126 3 4 80 1
## 1127 3 3 80 1
## 1128 3 4 80 1
## 1129 3 4 80 1
## 1130 4 4 80 0
## 1131 3 4 80 2
## 1132 3 2 80 1
## 1133 3 3 80 1
## 1134 3 3 80 1
## 1135 3 4 80 1
## 1136 3 2 80 0
## 1137 3 3 80 3
## 1138 3 2 80 0
## 1139 3 3 80 1
## 1140 3 4 80 2
## 1141 3 4 80 1
## 1142 3 2 80 1
## 1143 3 1 80 0
## 1144 3 1 80 1
## 1145 3 4 80 0
## 1146 3 2 80 2
## 1147 3 1 80 1
## 1148 3 4 80 1
## 1149 3 4 80 3
## 1150 3 1 80 2
## 1151 3 4 80 0
## 1152 4 2 80 1
## 1153 3 3 80 0
## 1154 3 3 80 0
## 1155 3 3 80 1
## 1156 3 4 80 1
## 1157 3 4 80 2
## 1158 3 4 80 1
## 1159 3 1 80 3
## 1160 3 4 80 0
## 1161 3 1 80 2
## 1162 3 4 80 1
## 1163 3 3 80 0
## 1164 3 1 80 1
## 1165 3 4 80 0
## 1166 3 3 80 0
## 1167 4 2 80 1
## 1168 3 4 80 2
## 1169 3 3 80 0
## 1170 3 1 80 0
## 1171 3 3 80 0
## 1172 3 2 80 0
## 1173 3 3 80 0
## 1174 3 3 80 2
## 1175 3 3 80 2
## 1176 4 3 80 0
## 1177 3 2 80 2
## 1178 3 3 80 3
## 1179 3 1 80 0
## 1180 3 3 80 1
## 1181 3 3 80 0
## 1182 3 2 80 1
## 1183 3 3 80 0
## 1184 4 1 80 1
## 1185 4 3 80 1
## 1186 4 1 80 1
## 1187 4 1 80 0
## 1188 3 4 80 2
## 1189 3 2 80 1
## 1190 3 3 80 2
## 1191 3 3 80 2
## 1192 3 1 80 2
## 1193 3 2 80 1
## 1194 4 2 80 0
## 1195 3 3 80 3
## 1196 3 1 80 0
## 1197 3 4 80 0
## 1198 3 1 80 0
## 1199 4 3 80 1
## 1200 3 2 80 2
## 1201 3 1 80 3
## 1202 3 1 80 0
## 1203 3 4 80 1
## 1204 3 3 80 0
## 1205 3 3 80 2
## 1206 3 1 80 0
## 1207 4 3 80 0
## 1208 3 4 80 1
## 1209 3 3 80 1
## 1210 4 3 80 1
## 1211 3 4 80 3
## 1212 3 3 80 2
## 1213 3 2 80 0
## 1214 4 2 80 1
## 1215 3 2 80 1
## 1216 3 3 80 0
## 1217 3 1 80 1
## 1218 3 4 80 1
## 1219 3 2 80 0
## 1220 3 3 80 1
## 1221 3 3 80 0
## 1222 4 4 80 1
## 1223 3 3 80 1
## 1224 3 3 80 0
## 1225 3 3 80 3
## 1226 3 3 80 0
## 1227 3 1 80 1
## 1228 3 4 80 1
## 1229 3 2 80 1
## 1230 3 2 80 1
## 1231 3 2 80 3
## 1232 3 4 80 0
## 1233 3 2 80 1
## 1234 3 2 80 1
## 1235 3 4 80 1
## 1236 3 2 80 1
## 1237 3 2 80 3
## 1238 3 2 80 0
## 1239 3 3 80 0
## 1240 4 4 80 0
## 1241 3 4 80 1
## 1242 3 3 80 1
## 1243 3 2 80 0
## 1244 4 3 80 2
## 1245 3 4 80 0
## 1246 3 4 80 1
## 1247 3 3 80 1
## 1248 3 3 80 1
## 1249 3 3 80 0
## 1250 3 3 80 0
## 1251 3 4 80 0
## 1252 3 1 80 1
## 1253 3 1 80 3
## 1254 3 4 80 0
## 1255 3 3 80 0
## 1256 4 3 80 0
## 1257 3 2 80 1
## 1258 3 1 80 3
## 1259 3 3 80 1
## 1260 3 3 80 1
## 1261 3 2 80 0
## 1262 3 3 80 1
## 1263 3 4 80 1
## 1264 4 2 80 3
## 1265 3 2 80 3
## 1266 3 4 80 2
## 1267 4 2 80 2
## 1268 3 3 80 1
## 1269 4 4 80 3
## 1270 3 2 80 0
## 1271 3 1 80 0
## 1272 3 2 80 0
## 1273 3 2 80 1
## 1274 3 3 80 0
## 1275 3 1 80 2
## 1276 3 4 80 0
## 1277 3 1 80 2
## 1278 3 3 80 1
## 1279 3 4 80 1
## 1280 3 3 80 3
## 1281 3 3 80 0
## 1282 3 4 80 0
## 1283 3 2 80 1
## 1284 3 3 80 1
## 1285 4 3 80 0
## 1286 3 3 80 0
## 1287 3 4 80 1
## 1288 3 3 80 2
## 1289 3 3 80 1
## 1290 3 4 80 3
## 1291 3 3 80 1
## 1292 3 2 80 0
## 1293 3 4 80 1
## 1294 3 3 80 0
## 1295 3 1 80 0
## 1296 3 4 80 1
## 1297 3 2 80 0
## 1298 3 3 80 0
## 1299 4 4 80 1
## 1300 3 4 80 1
## 1301 4 3 80 2
## 1302 4 4 80 1
## 1303 3 2 80 1
## 1304 3 3 80 1
## 1305 3 2 80 1
## 1306 3 2 80 1
## 1307 3 4 80 2
## 1308 4 3 80 1
## 1309 4 1 80 2
## 1310 3 1 80 0
## 1311 3 2 80 0
## 1312 3 3 80 0
## 1313 3 3 80 0
## 1314 3 4 80 3
## 1315 4 2 80 2
## 1316 4 4 80 1
## 1317 4 3 80 1
## 1318 3 3 80 0
## 1319 3 2 80 0
## 1320 3 3 80 0
## 1321 4 2 80 2
## 1322 3 3 80 0
## 1323 3 3 80 1
## 1324 3 2 80 1
## 1325 3 4 80 2
## 1326 3 4 80 0
## 1327 3 3 80 0
## 1328 3 4 80 1
## 1329 4 4 80 1
## 1330 3 4 80 0
## 1331 3 4 80 0
## 1332 3 4 80 0
## 1333 4 2 80 0
## 1334 4 3 80 3
## 1335 3 4 80 1
## 1336 3 2 80 3
## 1337 4 2 80 1
## 1338 3 4 80 1
## 1339 3 3 80 0
## 1340 4 1 80 0
## 1341 3 1 80 1
## 1342 3 1 80 1
## 1343 3 4 80 3
## 1344 3 2 80 0
## 1345 4 3 80 1
## 1346 3 4 80 0
## 1347 3 4 80 2
## 1348 4 4 80 0
## 1349 3 1 80 1
## 1350 3 3 80 1
## 1351 3 2 80 0
## 1352 3 2 80 1
## 1353 3 4 80 1
## 1354 4 2 80 1
## 1355 3 4 80 0
## 1356 3 3 80 2
## 1357 4 3 80 1
## 1358 3 2 80 1
## 1359 3 4 80 1
## 1360 3 3 80 0
## 1361 3 2 80 1
## 1362 3 1 80 1
## 1363 3 3 80 0
## 1364 3 2 80 0
## 1365 3 3 80 1
## 1366 3 4 80 0
## 1367 3 3 80 1
## 1368 3 3 80 1
## 1369 3 2 80 0
## 1370 3 4 80 0
## 1371 3 3 80 1
## 1372 3 3 80 1
## 1373 4 4 80 1
## 1374 3 3 80 1
## 1375 3 3 80 1
## 1376 3 1 80 0
## 1377 3 4 80 2
## 1378 3 4 80 0
## 1379 3 3 80 2
## 1380 3 1 80 0
## 1381 3 4 80 1
## 1382 3 3 80 0
## 1383 3 4 80 1
## 1384 4 2 80 0
## 1385 4 1 80 0
## 1386 3 3 80 1
## 1387 3 4 80 0
## 1388 3 1 80 1
## 1389 3 2 80 1
## 1390 4 2 80 0
## 1391 3 1 80 1
## 1392 3 1 80 0
## 1393 3 4 80 0
## 1394 3 1 80 0
## 1395 4 2 80 0
## 1396 3 3 80 0
## 1397 3 2 80 0
## 1398 3 3 80 2
## 1399 4 3 80 3
## 1400 3 2 80 1
## 1401 3 2 80 1
## 1402 3 1 80 1
## 1403 3 3 80 3
## 1404 3 1 80 0
## 1405 3 4 80 0
## 1406 4 3 80 1
## 1407 3 4 80 0
## 1408 3 2 80 0
## 1409 3 3 80 0
## 1410 3 1 80 1
## 1411 3 3 80 1
## 1412 3 3 80 0
## 1413 3 3 80 0
## 1414 3 3 80 1
## 1415 4 2 80 0
## 1416 3 3 80 3
## 1417 3 1 80 2
## 1418 3 3 80 1
## 1419 3 4 80 1
## 1420 3 3 80 1
## 1421 4 1 80 1
## 1422 3 1 80 2
## 1423 3 3 80 1
## 1424 3 4 80 0
## 1425 3 2 80 0
## 1426 3 1 80 1
## 1427 3 3 80 0
## 1428 3 3 80 2
## 1429 3 2 80 1
## 1430 3 1 80 0
## 1431 3 1 80 1
## 1432 3 3 80 2
## 1433 4 1 80 1
## 1434 4 2 80 1
## 1435 3 2 80 2
## 1436 3 3 80 0
## 1437 3 4 80 0
## 1438 3 3 80 0
## 1439 3 1 80 1
## 1440 3 3 80 2
## 1441 3 2 80 3
## 1442 4 1 80 1
## 1443 3 2 80 3
## 1444 3 1 80 0
## 1445 3 4 80 1
## 1446 4 3 80 1
## 1447 4 4 80 2
## 1448 4 1 80 1
## 1449 3 3 80 1
## 1450 3 4 80 0
## 1451 3 3 80 0
## 1452 3 3 80 1
## 1453 3 4 80 2
## 1454 3 1 80 1
## 1455 3 3 80 0
## 1456 3 4 80 0
## 1457 3 4 80 2
## 1458 3 2 80 3
## 1459 3 4 80 1
## 1460 3 1 80 1
## 1461 3 2 80 0
## 1462 3 2 80 1
## 1463 3 1 80 1
## 1464 3 2 80 0
## 1465 3 4 80 0
## 1466 3 3 80 1
## 1467 3 1 80 1
## 1468 4 2 80 1
## 1469 3 4 80 0
## 1470 3 1 80 0
## TotalWorkingYears TrainingTimesLastYear WorkLifeBalance YearsAtCompany
## 1 8 0 1 6
## 2 10 3 3 10
## 3 7 3 3 0
## 4 8 3 3 8
## 5 6 3 3 2
## 6 8 2 2 7
## 7 12 3 2 1
## 8 1 2 3 1
## 9 10 2 3 9
## 10 17 3 2 7
## 11 6 5 3 5
## 12 10 3 3 9
## 13 5 1 2 5
## 14 3 2 3 2
## 15 6 4 3 4
## 16 10 1 3 10
## 17 7 5 2 6
## 18 1 2 2 1
## 19 31 3 3 25
## 20 6 3 3 3
## 21 5 5 2 4
## 22 10 4 3 5
## 23 13 4 3 12
## 24 0 6 3 0
## 25 8 2 3 4
## 26 26 3 2 14
## 27 10 5 3 10
## 28 10 2 3 9
## 29 24 4 3 22
## 30 22 2 2 2
## 31 7 3 3 1
## 32 9 5 4 4
## 33 10 5 3 10
## 34 19 6 4 1
## 35 6 2 2 2
## 36 6 3 2 5
## 37 3 2 3 3
## 38 2 3 3 2
## 39 6 3 4 1
## 40 10 3 3 5
## 41 1 3 3 1
## 42 1 6 3 1
## 43 1 2 2 1
## 44 9 0 3 9
## 45 12 2 3 12
## 46 23 0 3 22
## 47 10 2 3 9
## 48 8 1 3 1
## 49 14 4 3 9
## 50 1 2 3 1
## 51 23 2 3 1
## 52 2 3 2 2
## 53 9 2 2 4
## 54 10 3 3 4
## 55 5 2 2 2
## 56 15 1 3 15
## 57 9 3 2 9
## 58 4 3 3 2
## 59 10 3 2 7
## 60 7 2 4 7
## 61 9 3 3 9
## 62 10 2 3 10
## 63 29 2 2 27
## 64 28 3 2 21
## 65 17 2 3 17
## 66 21 2 3 5
## 67 6 3 3 6
## 68 25 2 3 1
## 69 5 2 2 3
## 70 2 0 2 1
## 71 20 2 2 4
## 72 6 3 3 5
## 73 1 4 3 1
## 74 10 3 3 10
## 75 5 3 3 1
## 76 11 2 3 11
## 77 16 2 3 15
## 78 17 3 4 0
## 79 16 3 4 5
## 80 16 2 3 4
## 81 10 1 2 10
## 82 6 3 3 6
## 83 24 4 3 1
## 84 17 3 3 13
## 85 5 2 3 5
## 86 37 3 2 6
## 87 3 3 3 0
## 88 10 4 3 4
## 89 11 3 3 11
## 90 9 3 3 9
## 91 22 3 2 22
## 92 11 2 1 10
## 93 11 4 2 11
## 94 21 5 2 10
## 95 12 0 3 11
## 96 16 5 1 4
## 97 4 2 2 3
## 98 5 3 4 5
## 99 38 1 2 37
## 100 17 3 4 3
## 101 7 3 3 3
## 102 1 2 3 1
## 103 1 5 3 1
## 104 16 3 3 16
## 105 17 2 4 1
## 106 30 3 3 3
## 107 28 1 2 8
## 108 6 1 3 6
## 109 2 2 3 2
## 110 1 5 3 0
## 111 23 1 2 13
## 112 9 3 3 9
## 113 23 3 3 5
## 114 6 2 3 5
## 115 12 3 3 1
## 116 12 3 3 5
## 117 14 6 3 11
## 118 10 6 3 9
## 119 7 2 3 1
## 120 25 3 4 25
## 121 10 2 2 10
## 122 8 2 4 7
## 123 7 2 3 5
## 124 23 5 3 20
## 125 12 3 2 7
## 126 5 3 2 5
## 127 40 3 2 40
## 128 0 2 2 0
## 129 3 2 3 2
## 130 16 2 2 15
## 131 18 2 3 3
## 132 16 3 3 2
## 133 4 2 3 2
## 134 12 2 3 9
## 135 8 3 3 8
## 136 7 0 3 3
## 137 18 2 3 4
## 138 17 1 4 15
## 139 6 3 3 2
## 140 12 2 1 11
## 141 10 2 4 5
## 142 9 2 2 6
## 143 19 2 3 3
## 144 5 4 2 5
## 145 9 2 4 8
## 146 8 3 3 3
## 147 6 3 3 5
## 148 21 2 2 7
## 149 7 2 3 5
## 150 1 3 3 1
## 151 20 2 3 20
## 152 10 3 3 10
## 153 13 3 1 7
## 154 20 2 1 17
## 155 9 2 3 9
## 156 10 1 3 10
## 157 10 2 3 1
## 158 9 3 2 1
## 159 15 2 2 12
## 160 6 3 3 4
## 161 2 6 3 2
## 162 6 1 1 2
## 163 5 2 1 5
## 164 12 2 1 5
## 165 1 2 2 1
## 166 21 5 3 5
## 167 13 2 3 9
## 168 12 2 3 10
## 169 12 2 4 8
## 170 8 2 3 5
## 171 6 3 2 5
## 172 1 5 4 0
## 173 13 3 2 8
## 174 12 4 3 12
## 175 9 3 4 5
## 176 19 3 3 2
## 177 4 2 4 3
## 178 1 3 2 1
## 179 24 2 3 24
## 180 2 3 3 2
## 181 7 0 3 4
## 182 9 3 3 3
## 183 4 5 2 4
## 184 5 2 2 3
## 185 5 3 3 4
## 186 8 5 3 8
## 187 21 2 3 20
## 188 36 3 3 24
## 189 10 2 2 10
## 190 21 6 3 7
## 191 34 5 3 33
## 192 7 2 2 7
## 193 8 1 3 1
## 194 7 3 4 5
## 195 22 1 3 20
## 196 8 3 2 8
## 197 10 5 3 6
## 198 11 5 3 5
## 199 14 3 3 1
## 200 9 3 1 7
## 201 6 1 3 2
## 202 7 0 3 7
## 203 5 4 4 5
## 204 15 2 4 7
## 205 17 2 3 1
## 206 10 3 2 10
## 207 4 2 2 4
## 208 8 2 3 8
## 209 5 5 1 3
## 210 17 3 3 4
## 211 14 2 2 14
## 212 12 2 3 11
## 213 7 2 3 7
## 214 16 5 1 10
## 215 8 5 3 5
## 216 16 3 3 1
## 217 9 5 2 6
## 218 7 1 2 6
## 219 23 2 3 19
## 220 16 4 3 6
## 221 16 3 4 13
## 222 7 3 3 3
## 223 10 1 3 10
## 224 17 6 2 16
## 225 6 2 3 5
## 226 7 6 3 1
## 227 13 2 3 9
## 228 11 5 3 11
## 229 10 3 4 10
## 230 4 3 2 4
## 231 6 3 2 2
## 232 22 3 3 22
## 233 7 2 2 2
## 234 32 3 2 7
## 235 8 2 1 5
## 236 22 3 3 17
## 237 13 3 4 9
## 238 33 2 4 33
## 239 6 5 3 4
## 240 4 2 1 3
## 241 7 1 3 3
## 242 4 2 3 3
## 243 17 2 2 1
## 244 9 3 3 9
## 245 25 2 3 24
## 246 9 3 3 2
## 247 2 2 2 2
## 248 13 3 3 11
## 249 17 2 2 3
## 250 9 1 3 3
## 251 17 5 3 1
## 252 20 1 3 19
## 253 6 1 3 6
## 254 10 1 3 3
## 255 10 2 3 3
## 256 5 3 3 4
## 257 10 4 3 9
## 258 22 5 3 21
## 259 1 0 2 1
## 260 6 2 4 5
## 261 5 3 1 5
## 262 13 0 3 8
## 263 9 5 3 6
## 264 28 2 2 7
## 265 5 5 1 0
## 266 10 2 3 0
## 267 10 2 3 9
## 268 6 2 3 6
## 269 21 2 3 20
## 270 16 4 3 15
## 271 37 2 3 36
## 272 10 2 2 10
## 273 5 3 2 5
## 274 7 5 4 5
## 275 3 4 4 3
## 276 15 2 3 5
## 277 10 6 2 7
## 278 8 3 3 8
## 279 6 6 1 6
## 280 28 4 2 10
## 281 21 5 2 5
## 282 20 3 3 20
## 283 10 3 2 10
## 284 12 4 3 10
## 285 5 3 3 5
## 286 17 3 3 17
## 287 19 0 1 1
## 288 10 2 3 2
## 289 5 2 3 3
## 290 5 3 3 5
## 291 22 4 3 3
## 292 10 2 3 8
## 293 2 5 2 2
## 294 8 0 3 8
## 295 4 3 2 4
## 296 23 2 4 20
## 297 0 2 3 0
## 298 12 3 2 11
## 299 4 2 3 1
## 300 13 3 3 4
## 301 22 2 3 22
## 302 0 2 3 0
## 303 9 2 3 8
## 304 10 3 2 8
## 305 19 5 2 18
## 306 11 3 3 9
## 307 13 3 2 13
## 308 19 2 3 10
## 309 12 2 3 5
## 310 6 4 3 5
## 311 9 1 3 2
## 312 24 2 3 24
## 313 3 2 1 2
## 314 12 2 3 10
## 315 21 3 3 21
## 316 9 5 3 8
## 317 25 2 3 7
## 318 11 3 2 8
## 319 4 2 2 4
## 320 13 2 3 13
## 321 5 3 3 5
## 322 13 3 3 7
## 323 10 0 2 7
## 324 5 4 2 3
## 325 11 2 3 10
## 326 10 2 3 10
## 327 21 2 3 21
## 328 12 3 2 1
## 329 9 6 2 7
## 330 21 2 3 3
## 331 9 3 4 9
## 332 6 0 3 5
## 333 20 4 2 4
## 334 10 1 2 1
## 335 12 3 3 10
## 336 6 2 2 4
## 337 7 4 2 7
## 338 4 2 3 3
## 339 10 2 3 10
## 340 8 3 3 8
## 341 8 2 2 6
## 342 12 3 3 11
## 343 11 2 2 11
## 344 7 2 3 7
## 345 17 5 3 16
## 346 4 2 2 4
## 347 8 3 3 5
## 348 5 3 3 0
## 349 16 2 3 1
## 350 4 3 2 4
## 351 4 5 3 3
## 352 8 4 3 8
## 353 15 3 1 0
## 354 13 2 3 7
## 355 4 2 4 2
## 356 8 3 3 8
## 357 14 6 3 1
## 358 3 3 3 3
## 359 7 6 3 1
## 360 16 2 3 13
## 361 15 2 3 3
## 362 10 3 3 7
## 363 3 3 2 3
## 364 1 2 3 1
## 365 17 3 3 5
## 366 7 2 4 1
## 367 8 5 3 8
## 368 20 2 3 4
## 369 8 6 3 6
## 370 3 5 3 2
## 371 1 0 3 1
## 372 6 4 3 3
## 373 10 5 3 1
## 374 5 2 3 5
## 375 7 1 3 7
## 376 26 2 3 5
## 377 18 2 2 7
## 378 6 3 3 2
## 379 9 3 2 5
## 380 30 2 3 5
## 381 5 2 4 5
## 382 1 3 3 1
## 383 7 2 3 6
## 384 2 1 3 2
## 385 10 2 3 10
## 386 3 4 3 1
## 387 18 2 2 18
## 388 8 5 3 5
## 389 8 5 3 5
## 390 18 2 2 1
## 391 25 3 3 25
## 392 20 3 3 1
## 393 24 4 2 4
## 394 6 3 3 3
## 395 13 5 1 13
## 396 8 1 3 3
## 397 8 3 3 5
## 398 5 3 3 5
## 399 15 2 3 13
## 400 4 3 3 4
## 401 21 3 3 21
## 402 36 0 2 7
## 403 6 6 3 5
## 404 10 2 3 10
## 405 10 2 3 10
## 406 6 5 3 2
## 407 28 4 3 5
## 408 8 3 2 2
## 409 31 2 1 5
## 410 19 3 3 5
## 411 11 2 3 5
## 412 33 5 1 29
## 413 19 5 2 10
## 414 7 4 4 6
## 415 6 4 3 5
## 416 3 3 2 2
## 417 1 3 3 1
## 418 21 2 3 20
## 419 3 3 1 3
## 420 9 3 1 5
## 421 10 2 3 10
## 422 6 2 4 2
## 423 1 3 4 1
## 424 10 3 3 9
## 425 32 3 2 1
## 426 28 2 3 27
## 427 12 2 2 11
## 428 22 5 4 18
## 429 20 2 3 5
## 430 26 2 1 3
## 431 6 2 3 5
## 432 19 3 3 1
## 433 14 3 3 7
## 434 15 3 3 3
## 435 13 6 4 13
## 436 15 2 4 7
## 437 8 2 3 4
## 438 4 3 3 3
## 439 10 2 3 8
## 440 12 2 3 1
## 441 11 2 3 3
## 442 8 4 3 2
## 443 10 3 2 10
## 444 4 3 3 2
## 445 14 2 3 9
## 446 37 2 3 10
## 447 16 3 3 14
## 448 15 2 3 11
## 449 22 3 3 20
## 450 8 3 3 8
## 451 10 2 4 6
## 452 10 5 2 10
## 453 9 3 4 8
## 454 8 2 2 7
## 455 8 2 4 3
## 456 10 2 3 6
## 457 10 3 2 5
## 458 0 3 3 0
## 459 20 2 3 1
## 460 10 3 3 8
## 461 8 5 3 0
## 462 5 3 3 5
## 463 10 3 3 10
## 464 1 3 1 1
## 465 12 3 4 6
## 466 28 3 2 2
## 467 22 2 3 18
## 468 9 6 3 9
## 469 18 3 3 10
## 470 6 2 3 4
## 471 3 3 3 2
## 472 18 4 3 1
## 473 8 2 2 6
## 474 31 5 3 31
## 475 6 3 3 6
## 476 6 5 4 5
## 477 1 2 3 1
## 478 32 2 3 32
## 479 7 1 3 7
## 480 6 4 3 6
## 481 1 2 4 1
## 482 6 3 3 6
## 483 9 2 1 3
## 484 9 3 3 3
## 485 13 4 4 7
## 486 6 5 2 5
## 487 17 3 2 3
## 488 1 0 4 1
## 489 10 4 3 10
## 490 21 3 2 1
## 491 8 3 2 0
## 492 10 3 3 8
## 493 21 3 1 3
## 494 10 1 4 2
## 495 8 3 3 8
## 496 5 3 3 4
## 497 3 2 3 3
## 498 26 2 4 2
## 499 3 3 3 2
## 500 6 3 3 5
## 501 6 2 2 6
## 502 1 2 3 1
## 503 18 2 4 14
## 504 10 4 2 10
## 505 5 4 3 1
## 506 3 2 3 3
## 507 10 2 3 10
## 508 6 5 2 6
## 509 17 3 3 17
## 510 15 2 1 13
## 511 16 1 4 9
## 512 13 2 3 3
## 513 5 0 3 4
## 514 1 5 3 1
## 515 10 3 3 10
## 516 1 3 3 1
## 517 5 3 4 3
## 518 4 4 3 3
## 519 8 2 3 7
## 520 10 5 3 10
## 521 12 3 3 2
## 522 6 3 3 6
## 523 4 2 3 1
## 524 20 3 3 20
## 525 9 3 4 8
## 526 4 3 3 2
## 527 20 4 3 20
## 528 10 2 2 10
## 529 18 4 3 4
## 530 10 4 2 9
## 531 9 3 3 9
## 532 10 6 3 7
## 533 20 2 3 7
## 534 20 2 3 18
## 535 32 3 3 5
## 536 23 2 2 21
## 537 10 1 3 2
## 538 9 4 2 9
## 539 22 3 3 22
## 540 4 2 3 2
## 541 10 4 3 7
## 542 10 2 3 8
## 543 10 4 4 1
## 544 9 5 3 5
## 545 28 2 2 22
## 546 10 2 2 8
## 547 1 6 3 1
## 548 7 2 3 2
## 549 7 5 3 2
## 550 10 2 3 5
## 551 5 2 4 4
## 552 12 3 1 8
## 553 30 1 2 10
## 554 5 2 2 4
## 555 9 2 1 7
## 556 2 2 3 2
## 557 19 4 3 2
## 558 16 2 4 1
## 559 10 2 3 10
## 560 6 0 1 1
## 561 7 3 3 0
## 562 34 3 4 34
## 563 10 2 2 10
## 564 6 5 2 6
## 565 9 3 3 8
## 566 2 3 3 2
## 567 8 2 3 5
## 568 6 5 3 6
## 569 24 2 3 5
## 570 10 1 3 10
## 571 5 3 3 4
## 572 5 2 3 2
## 573 11 3 2 8
## 574 6 2 2 4
## 575 10 2 3 4
## 576 9 4 3 5
## 577 5 3 3 4
## 578 6 3 2 5
## 579 17 2 3 15
## 580 6 3 3 6
## 581 3 1 2 3
## 582 7 2 3 2
## 583 8 2 3 8
## 584 6 1 3 3
## 585 24 4 2 24
## 586 1 2 3 0
## 587 1 4 3 1
## 588 9 3 3 5
## 589 30 3 3 4
## 590 1 1 3 1
## 591 14 3 4 13
## 592 6 3 3 3
## 593 26 3 2 26
## 594 10 2 3 9
## 595 10 3 3 10
## 596 40 2 3 31
## 597 7 0 3 2
## 598 8 4 3 4
## 599 5 3 2 2
## 600 8 2 3 5
## 601 14 3 3 14
## 602 10 6 3 1
## 603 12 4 2 0
## 604 1 3 3 1
## 605 10 3 2 9
## 606 13 3 2 4
## 607 6 3 3 5
## 608 9 3 4 9
## 609 12 3 2 9
## 610 22 3 3 4
## 611 9 3 3 9
## 612 17 3 4 8
## 613 8 2 3 8
## 614 4 1 1 3
## 615 8 2 3 8
## 616 0 6 2 0
## 617 29 2 2 20
## 618 10 2 2 5
## 619 6 3 2 4
## 620 9 5 3 9
## 621 6 3 2 6
## 622 18 1 2 18
## 623 8 3 2 5
## 624 10 3 2 5
## 625 35 3 3 5
## 626 18 2 3 5
## 627 9 3 2 5
## 628 31 3 3 9
## 629 9 2 3 1
## 630 6 6 3 5
## 631 4 2 1 2
## 632 10 2 2 3
## 633 8 2 3 2
## 634 6 3 3 5
## 635 5 3 3 5
## 636 17 2 3 17
## 637 10 3 2 10
## 638 4 2 3 3
## 639 5 1 4 5
## 640 7 2 3 4
## 641 6 2 2 5
## 642 10 3 3 10
## 643 3 3 3 2
## 644 17 1 2 5
## 645 8 2 3 6
## 646 5 3 3 3
## 647 28 3 3 2
## 648 16 2 3 13
## 649 10 3 3 5
## 650 33 0 3 12
## 651 12 2 2 5
## 652 8 2 3 7
## 653 10 2 3 10
## 654 31 3 3 31
## 655 13 2 3 5
## 656 7 4 4 4
## 657 1 2 1 1
## 658 8 5 3 4
## 659 8 0 3 8
## 660 4 3 3 4
## 661 3 3 2 1
## 662 4 2 4 1
## 663 2 3 2 2
## 664 1 3 2 1
## 665 17 2 2 16
## 666 3 3 2 3
## 667 4 3 4 3
## 668 10 1 2 7
## 669 6 2 3 2
## 670 8 2 1 2
## 671 1 2 3 1
## 672 1 3 3 1
## 673 10 6 3 5
## 674 6 1 3 3
## 675 24 3 3 6
## 676 13 2 4 7
## 677 10 2 1 10
## 678 29 3 2 26
## 679 13 2 2 0
## 680 9 2 2 9
## 681 8 6 3 6
## 682 15 3 3 15
## 683 5 3 3 3
## 684 1 2 3 1
## 685 11 2 2 1
## 686 7 2 3 7
## 687 20 3 3 18
## 688 16 6 3 11
## 689 1 3 4 1
## 690 1 2 3 1
## 691 10 2 1 9
## 692 3 2 3 1
## 693 8 2 4 8
## 694 16 6 3 16
## 695 6 3 3 5
## 696 17 2 1 14
## 697 9 5 2 9
## 698 3 5 3 3
## 699 5 3 3 5
## 700 26 2 2 9
## 701 7 4 3 1
## 702 22 3 4 17
## 703 10 2 4 8
## 704 6 1 3 5
## 705 12 2 3 10
## 706 9 3 3 8
## 707 22 2 2 1
## 708 20 3 4 19
## 709 12 3 3 7
## 710 4 0 3 3
## 711 10 2 3 10
## 712 3 5 3 0
## 713 5 4 3 3
## 714 8 2 3 5
## 715 32 1 2 5
## 716 6 2 3 6
## 717 21 2 4 18
## 718 4 2 3 2
## 719 9 2 3 9
## 720 9 0 3 9
## 721 7 2 3 5
## 722 22 2 3 12
## 723 3 0 2 2
## 724 13 3 3 8
## 725 5 6 3 4
## 726 5 2 1 4
## 727 4 1 3 4
## 728 0 2 3 0
## 729 22 2 3 10
## 730 16 3 2 16
## 731 9 4 2 8
## 732 1 2 3 1
## 733 4 3 3 3
## 734 8 2 3 8
## 735 4 3 2 4
## 736 19 0 3 2
## 737 27 3 3 15
## 738 8 6 3 2
## 739 21 3 3 21
## 740 4 2 3 3
## 741 3 4 2 3
## 742 21 3 4 1
## 743 8 5 3 1
## 744 30 4 3 5
## 745 15 2 1 1
## 746 17 3 3 8
## 747 21 3 3 21
## 748 19 1 3 1
## 749 7 6 3 2
## 750 33 3 3 32
## 751 23 2 3 12
## 752 19 3 3 18
## 753 18 1 3 17
## 754 21 2 3 21
## 755 3 2 2 2
## 756 26 4 4 9
## 757 10 2 3 8
## 758 16 2 2 15
## 759 14 1 1 6
## 760 6 3 3 6
## 761 30 2 3 15
## 762 9 3 2 1
## 763 6 2 3 3
## 764 1 2 3 1
## 765 1 5 3 1
## 766 8 2 3 2
## 767 29 2 2 8
## 768 8 3 2 4
## 769 8 3 2 7
## 770 5 5 3 5
## 771 23 0 3 2
## 772 13 4 3 9
## 773 18 4 3 5
## 774 15 2 2 14
## 775 31 3 4 9
## 776 18 5 3 1
## 777 2 3 3 2
## 778 1 6 2 1
## 779 19 2 3 16
## 780 18 2 4 10
## 781 10 2 2 10
## 782 6 2 3 5
## 783 7 1 2 6
## 784 10 3 3 10
## 785 20 4 2 19
## 786 14 6 3 11
## 787 3 4 3 3
## 788 23 4 3 3
## 789 10 4 4 8
## 790 24 1 3 20
## 791 9 2 3 3
## 792 9 2 3 8
## 793 14 4 3 13
## 794 4 5 2 4
## 795 7 1 2 6
## 796 8 5 3 1
## 797 7 3 4 7
## 798 1 0 2 1
## 799 5 0 3 2
## 800 23 3 3 22
## 801 1 2 3 1
## 802 5 4 3 0
## 803 4 3 3 3
## 804 6 2 3 0
## 805 27 3 2 5
## 806 15 2 3 15
## 807 18 4 3 8
## 808 9 2 3 8
## 809 11 1 3 7
## 810 10 3 2 9
## 811 23 3 3 12
## 812 10 2 3 1
## 813 18 1 3 8
## 814 21 4 3 18
## 815 21 2 4 20
## 816 2 6 4 2
## 817 9 2 2 5
## 818 18 0 3 11
## 819 3 4 3 2
## 820 6 2 1 5
## 821 5 3 3 5
## 822 22 3 3 9
## 823 5 3 2 3
## 824 8 2 2 7
## 825 16 3 3 1
## 826 10 2 2 10
## 827 7 2 4 7
## 828 3 2 3 3
## 829 0 0 3 0
## 830 6 3 3 5
## 831 6 4 3 1
## 832 2 5 2 2
## 833 9 2 3 6
## 834 4 0 3 4
## 835 6 3 2 6
## 836 6 2 1 5
## 837 11 3 1 11
## 838 20 3 2 18
## 839 22 2 2 21
## 840 9 3 4 6
## 841 10 2 3 8
## 842 6 2 3 4
## 843 1 4 2 1
## 844 8 2 3 8
## 845 10 3 3 10
## 846 16 3 1 1
## 847 15 2 4 7
## 848 14 3 3 13
## 849 2 2 4 2
## 850 7 2 2 4
## 851 1 3 3 1
## 852 28 2 3 5
## 853 10 5 3 10
## 854 1 4 3 1
## 855 7 2 2 3
## 856 14 2 2 14
## 857 2 2 2 2
## 858 6 4 3 6
## 859 26 6 3 7
## 860 6 2 2 5
## 861 1 5 3 0
## 862 28 2 3 26
## 863 6 3 3 0
## 864 5 2 3 5
## 865 5 2 1 1
## 866 8 3 3 4
## 867 5 2 3 0
## 868 32 3 3 2
## 869 6 2 3 6
## 870 25 2 3 4
## 871 15 2 3 7
## 872 1 3 1 1
## 873 10 3 2 10
## 874 7 4 3 7
## 875 10 4 2 10
## 876 20 3 3 20
## 877 2 2 3 2
## 878 12 3 2 6
## 879 10 3 4 4
## 880 12 3 3 11
## 881 2 2 3 2
## 882 10 2 3 8
## 883 17 2 3 7
## 884 15 5 3 14
## 885 7 2 4 5
## 886 5 4 3 4
## 887 12 2 3 11
## 888 20 6 3 1
## 889 16 6 2 13
## 890 9 3 2 9
## 891 33 2 1 5
## 892 10 5 3 10
## 893 1 2 4 1
## 894 3 5 3 3
## 895 36 2 3 10
## 896 6 2 2 6
## 897 10 2 3 10
## 898 13 2 3 6
## 899 25 2 3 8
## 900 23 2 4 1
## 901 12 2 2 11
## 902 7 4 2 1
## 903 5 2 3 5
## 904 6 2 3 6
## 905 25 3 4 1
## 906 9 2 2 7
## 907 2 5 2 1
## 908 26 5 3 22
## 909 10 3 3 9
## 910 1 2 3 1
## 911 1 2 3 1
## 912 1 4 3 1
## 913 8 2 2 8
## 914 26 2 3 24
## 915 34 3 3 33
## 916 2 2 1 2
## 917 26 2 3 11
## 918 4 3 3 3
## 919 31 5 2 29
## 920 25 6 2 9
## 921 15 2 4 11
## 922 5 2 2 4
## 923 26 4 2 25
## 924 14 5 4 10
## 925 4 3 3 3
## 926 18 2 3 1
## 927 23 3 4 21
## 928 18 2 3 16
## 929 10 2 3 10
## 930 2 2 3 2
## 931 8 6 2 7
## 932 10 3 3 8
## 933 10 2 3 7
## 934 5 2 2 3
## 935 2 3 2 2
## 936 10 4 4 10
## 937 22 4 3 0
## 938 21 4 3 19
## 939 2 3 3 2
## 940 10 3 3 10
## 941 6 2 3 5
## 942 10 6 3 9
## 943 10 0 3 7
## 944 10 3 4 7
## 945 10 6 3 9
## 946 25 2 3 3
## 947 9 2 3 5
## 948 10 2 2 8
## 949 9 2 3 9
## 950 9 6 3 8
## 951 10 5 2 10
## 952 19 4 3 19
## 953 3 2 4 3
## 954 10 4 4 3
## 955 21 3 2 20
## 956 23 5 3 19
## 957 36 4 3 7
## 958 6 0 3 4
## 959 10 0 2 9
## 960 9 4 3 9
## 961 10 2 3 9
## 962 9 3 3 9
## 963 33 2 3 33
## 964 11 3 3 7
## 965 10 6 4 10
## 966 7 6 2 3
## 967 31 0 2 10
## 968 7 3 3 4
## 969 17 2 2 15
## 970 11 2 3 10
## 971 5 4 3 1
## 972 29 1 2 5
## 973 0 5 4 0
## 974 10 0 3 9
## 975 8 3 3 6
## 976 24 2 2 19
## 977 33 0 3 19
## 978 5 2 3 5
## 979 15 0 3 12
## 980 10 1 3 8
## 981 3 3 4 1
## 982 5 0 2 4
## 983 4 2 3 4
## 984 14 2 4 14
## 985 5 0 3 5
## 986 10 3 2 10
## 987 8 2 4 5
## 988 14 5 3 4
## 989 12 4 2 6
## 990 8 2 3 6
## 991 8 2 4 5
## 992 4 3 2 3
## 993 13 2 3 6
## 994 6 3 2 3
## 995 24 3 2 5
## 996 20 3 1 20
## 997 6 3 3 6
## 998 8 2 3 8
## 999 5 2 3 4
## 1000 21 5 3 20
## 1001 12 2 1 5
## 1002 8 6 3 3
## 1003 10 2 3 2
## 1004 7 2 2 3
## 1005 8 2 3 7
## 1006 10 3 2 10
## 1007 20 2 3 4
## 1008 9 1 3 8
## 1009 29 3 2 20
## 1010 32 3 3 9
## 1011 31 4 4 7
## 1012 15 3 3 5
## 1013 1 3 3 1
## 1014 8 3 3 3
## 1015 9 3 4 3
## 1016 10 2 3 4
## 1017 1 3 4 1
## 1018 6 3 4 5
## 1019 10 2 2 10
## 1020 11 3 2 3
## 1021 17 2 2 6
## 1022 6 2 3 3
## 1023 7 2 1 6
## 1024 5 3 4 3
## 1025 26 2 4 20
## 1026 5 3 3 5
## 1027 7 3 2 4
## 1028 7 2 2 5
## 1029 7 5 2 4
## 1030 11 2 4 8
## 1031 13 5 3 13
## 1032 28 1 4 7
## 1033 11 2 4 1
## 1034 10 2 3 10
## 1035 24 2 3 7
## 1036 8 3 3 3
## 1037 7 2 1 2
## 1038 10 4 4 3
## 1039 15 3 3 2
## 1040 2 0 3 2
## 1041 16 5 3 9
## 1042 6 4 3 5
## 1043 7 5 3 5
## 1044 35 2 2 9
## 1045 20 0 2 3
## 1046 8 3 4 3
## 1047 6 3 2 5
## 1048 5 2 3 4
## 1049 15 2 3 13
## 1050 4 2 2 2
## 1051 12 3 3 12
## 1052 11 4 2 1
## 1053 1 2 2 1
## 1054 13 2 2 12
## 1055 29 3 3 8
## 1056 16 3 2 14
## 1057 5 3 4 3
## 1058 7 4 1 5
## 1059 16 2 4 15
## 1060 1 3 3 1
## 1061 4 2 2 0
## 1062 1 2 3 1
## 1063 16 2 2 2
## 1064 10 3 3 10
## 1065 6 3 4 5
## 1066 4 2 3 3
## 1067 8 5 3 5
## 1068 11 4 2 5
## 1069 8 2 2 0
## 1070 1 2 1 1
## 1071 5 5 3 4
## 1072 10 3 3 3
## 1073 4 3 3 3
## 1074 8 2 2 8
## 1075 14 6 3 0
## 1076 10 5 4 5
## 1077 26 2 3 14
## 1078 11 2 1 9
## 1079 24 1 4 20
## 1080 9 3 3 2
## 1081 23 2 4 13
## 1082 11 3 1 11
## 1083 5 2 3 4
## 1084 15 3 1 5
## 1085 10 3 3 10
## 1086 7 2 1 7
## 1087 32 2 3 32
## 1088 12 4 3 11
## 1089 4 3 3 1
## 1090 10 2 3 10
## 1091 9 3 3 7
## 1092 5 2 3 5
## 1093 8 3 3 5
## 1094 24 3 1 20
## 1095 9 5 4 8
## 1096 15 3 3 15
## 1097 21 2 3 21
## 1098 2 3 3 1
## 1099 8 2 3 2
## 1100 10 4 3 10
## 1101 6 5 3 5
## 1102 12 2 3 7
## 1103 7 3 2 3
## 1104 18 2 3 8
## 1105 5 2 2 1
## 1106 8 6 1 2
## 1107 10 4 3 10
## 1108 10 2 3 6
## 1109 3 3 3 3
## 1110 9 3 3 4
## 1111 1 2 3 1
## 1112 34 4 3 33
## 1113 7 2 3 5
## 1114 9 3 2 6
## 1115 10 3 2 8
## 1116 1 4 1 1
## 1117 36 3 3 36
## 1118 9 3 2 2
## 1119 1 5 3 1
## 1120 10 3 3 9
## 1121 8 5 4 3
## 1122 15 5 3 1
## 1123 10 3 3 10
## 1124 10 4 3 5
## 1125 11 3 3 7
## 1126 6 0 4 6
## 1127 27 2 3 1
## 1128 4 2 3 2
## 1129 9 3 3 3
## 1130 24 2 3 1
## 1131 10 3 2 10
## 1132 8 3 2 8
## 1133 5 2 3 5
## 1134 8 0 3 6
## 1135 1 5 2 1
## 1136 27 5 1 26
## 1137 1 3 3 1
## 1138 4 2 2 4
## 1139 32 3 3 30
## 1140 6 3 3 3
## 1141 23 4 2 22
## 1142 6 3 2 6
## 1143 10 3 3 10
## 1144 10 2 3 10
## 1145 10 3 3 5
## 1146 7 2 3 3
## 1147 9 3 3 9
## 1148 10 3 2 9
## 1149 10 3 3 7
## 1150 7 3 3 7
## 1151 16 2 3 16
## 1152 6 5 2 5
## 1153 3 2 3 2
## 1154 0 2 4 0
## 1155 27 2 3 5
## 1156 11 3 3 10
## 1157 18 2 3 18
## 1158 15 5 3 14
## 1159 9 2 2 4
## 1160 10 2 1 9
## 1161 10 3 3 10
## 1162 10 6 4 5
## 1163 15 3 3 13
## 1164 8 2 1 8
## 1165 18 2 2 4
## 1166 14 3 3 10
## 1167 23 3 3 2
## 1168 7 2 2 2
## 1169 6 2 3 6
## 1170 5 0 3 3
## 1171 6 0 2 4
## 1172 10 3 1 4
## 1173 8 2 3 5
## 1174 9 6 3 3
## 1175 7 6 3 7
## 1176 7 3 3 5
## 1177 27 2 3 4
## 1178 19 3 3 14
## 1179 2 3 3 2
## 1180 11 2 3 11
## 1181 15 4 3 4
## 1182 30 3 3 15
## 1183 4 6 3 3
## 1184 13 3 3 5
## 1185 36 6 3 10
## 1186 14 3 3 14
## 1187 13 2 4 11
## 1188 19 4 4 13
## 1189 10 3 2 10
## 1190 6 5 3 6
## 1191 10 4 2 9
## 1192 10 2 3 10
## 1193 17 2 2 2
## 1194 4 3 3 4
## 1195 29 2 3 3
## 1196 23 2 3 8
## 1197 21 2 3 2
## 1198 2 3 3 2
## 1199 7 2 3 6
## 1200 10 2 2 3
## 1201 8 2 3 2
## 1202 5 2 3 5
## 1203 7 5 2 0
## 1204 11 2 3 4
## 1205 19 3 3 9
## 1206 1 2 3 1
## 1207 7 5 3 7
## 1208 8 1 3 4
## 1209 15 3 4 15
## 1210 19 2 4 1
## 1211 14 6 3 14
## 1212 6 2 2 6
## 1213 10 3 3 10
## 1214 3 2 3 3
## 1215 9 2 3 8
## 1216 6 2 4 5
## 1217 10 3 3 10
## 1218 5 1 3 5
## 1219 10 3 3 10
## 1220 9 2 3 5
## 1221 17 2 2 13
## 1222 25 3 2 23
## 1223 1 2 3 1
## 1224 25 3 1 23
## 1225 3 3 4 3
## 1226 21 2 3 21
## 1227 10 3 2 5
## 1228 6 2 4 5
## 1229 10 4 3 3
## 1230 18 3 3 1
## 1231 6 2 4 6
## 1232 19 3 3 10
## 1233 17 3 3 7
## 1234 10 2 2 10
## 1235 4 3 1 1
## 1236 13 5 2 10
## 1237 16 3 3 2
## 1238 10 2 3 0
## 1239 3 3 1 3
## 1240 9 3 2 5
## 1241 9 2 3 9
## 1242 10 2 1 4
## 1243 21 3 2 21
## 1244 9 2 4 5
## 1245 10 2 3 9
## 1246 3 2 3 2
## 1247 6 0 2 4
## 1248 6 3 3 5
## 1249 6 5 2 6
## 1250 2 3 3 2
## 1251 10 5 4 3
## 1252 12 2 3 7
## 1253 6 3 3 5
## 1254 13 2 2 11
## 1255 8 1 4 5
## 1256 11 2 2 0
## 1257 9 4 2 6
## 1258 10 2 3 1
## 1259 1 2 3 1
## 1260 10 3 3 7
## 1261 12 3 3 7
## 1262 15 2 3 1
## 1263 6 4 3 1
## 1264 7 6 2 5
## 1265 34 2 3 1
## 1266 11 2 2 9
## 1267 5 2 3 5
## 1268 15 3 3 15
## 1269 27 2 2 3
## 1270 10 5 3 9
## 1271 6 3 3 2
## 1272 1 3 3 1
## 1273 5 3 3 5
## 1274 1 6 3 1
## 1275 13 3 3 12
## 1276 15 2 3 2
## 1277 5 2 3 5
## 1278 24 3 3 2
## 1279 15 1 3 12
## 1280 6 2 2 5
## 1281 19 4 2 10
## 1282 10 2 3 10
## 1283 14 1 3 10
## 1284 5 6 4 5
## 1285 9 3 3 4
## 1286 6 3 3 2
## 1287 7 5 2 4
## 1288 10 2 2 0
## 1289 15 6 3 7
## 1290 13 3 3 11
## 1291 11 3 2 7
## 1292 10 4 1 10
## 1293 7 6 3 2
## 1294 7 2 2 3
## 1295 11 3 1 3
## 1296 23 3 4 22
## 1297 9 3 3 7
## 1298 6 3 3 5
## 1299 13 2 4 9
## 1300 12 3 3 5
## 1301 10 5 3 10
## 1302 37 0 2 16
## 1303 6 2 4 5
## 1304 28 4 3 22
## 1305 15 3 3 7
## 1306 14 2 2 7
## 1307 9 3 3 3
## 1308 3 3 3 3
## 1309 20 4 2 4
## 1310 5 4 3 5
## 1311 23 3 3 2
## 1312 0 4 1 0
## 1313 2 4 3 1
## 1314 4 3 3 2
## 1315 10 3 4 8
## 1316 15 2 3 1
## 1317 7 5 3 7
## 1318 6 3 2 5
## 1319 11 3 4 11
## 1320 4 2 4 0
## 1321 10 1 2 6
## 1322 7 2 3 2
## 1323 12 4 2 9
## 1324 3 2 3 3
## 1325 11 3 3 7
## 1326 8 3 3 0
## 1327 7 3 2 2
## 1328 25 5 3 19
## 1329 9 5 3 9
## 1330 1 3 3 1
## 1331 21 2 3 16
## 1332 29 3 3 22
## 1333 1 3 2 1
## 1334 14 2 3 8
## 1335 8 2 2 7
## 1336 7 2 3 2
## 1337 19 2 4 5
## 1338 1 3 3 1
## 1339 1 3 2 1
## 1340 1 2 3 1
## 1341 10 4 3 10
## 1342 10 2 3 10
## 1343 9 3 3 5
## 1344 11 2 3 3
## 1345 16 2 3 5
## 1346 4 2 3 3
## 1347 10 2 2 9
## 1348 10 2 2 10
## 1349 22 3 3 19
## 1350 1 3 2 1
## 1351 9 5 2 8
## 1352 24 3 3 22
## 1353 10 5 3 2
## 1354 5 2 3 5
## 1355 5 3 3 4
## 1356 12 1 1 4
## 1357 14 3 3 5
## 1358 18 3 4 13
## 1359 8 2 3 5
## 1360 9 3 2 4
## 1361 4 0 2 2
## 1362 8 3 3 7
## 1363 12 3 3 4
## 1364 10 2 2 10
## 1365 7 2 3 7
## 1366 1 3 3 1
## 1367 10 1 3 3
## 1368 6 3 2 5
## 1369 16 3 3 15
## 1370 6 0 3 2
## 1371 16 4 4 8
## 1372 6 3 3 0
## 1373 10 3 3 10
## 1374 20 3 3 20
## 1375 29 2 2 1
## 1376 8 2 3 4
## 1377 10 0 4 5
## 1378 28 3 3 5
## 1379 14 4 3 0
## 1380 1 2 3 1
## 1381 6 2 1 5
## 1382 5 3 2 5
## 1383 4 3 4 4
## 1384 5 3 3 5
## 1385 14 3 2 14
## 1386 9 2 2 8
## 1387 8 6 2 7
## 1388 6 5 2 5
## 1389 9 6 3 5
## 1390 10 6 3 10
## 1391 6 2 2 4
## 1392 20 3 2 1
## 1393 10 2 3 10
## 1394 7 5 3 7
## 1395 8 1 3 1
## 1396 10 4 3 10
## 1397 15 2 2 2
## 1398 9 6 2 4
## 1399 9 2 3 9
## 1400 10 1 3 10
## 1401 7 2 3 6
## 1402 35 0 3 10
## 1403 1 4 3 1
## 1404 21 3 3 20
## 1405 20 2 3 20
## 1406 13 2 4 11
## 1407 9 3 3 5
## 1408 4 2 2 4
## 1409 5 6 4 5
## 1410 10 2 4 10
## 1411 15 4 3 11
## 1412 6 3 3 2
## 1413 12 6 2 12
## 1414 7 2 2 2
## 1415 25 3 3 17
## 1416 1 2 2 1
## 1417 16 3 3 15
## 1418 3 1 3 2
## 1419 10 1 3 8
## 1420 9 3 2 4
## 1421 12 3 3 5
## 1422 14 3 1 13
## 1423 5 3 3 2
## 1424 4 2 4 3
## 1425 10 5 3 10
## 1426 10 6 3 9
## 1427 6 3 3 6
## 1428 8 3 2 1
## 1429 3 2 3 2
## 1430 18 2 3 7
## 1431 20 3 3 18
## 1432 14 3 3 14
## 1433 16 2 3 16
## 1434 6 3 2 5
## 1435 16 3 2 9
## 1436 6 2 3 4
## 1437 2 6 3 2
## 1438 21 3 2 6
## 1439 1 3 2 1
## 1440 10 2 3 9
## 1441 18 3 3 4
## 1442 13 2 2 13
## 1443 4 3 4 2
## 1444 24 2 2 22
## 1445 14 4 1 10
## 1446 21 3 3 20
## 1447 8 2 3 8
## 1448 15 4 2 15
## 1449 14 5 3 5
## 1450 4 4 3 4
## 1451 9 2 3 9
## 1452 10 1 3 10
## 1453 12 3 3 6
## 1454 8 2 2 6
## 1455 8 3 3 5
## 1456 8 2 3 2
## 1457 10 2 4 10
## 1458 20 2 3 5
## 1459 4 5 3 4
## 1460 10 2 3 4
## 1461 5 3 1 5
## 1462 20 3 3 3
## 1463 21 2 2 20
## 1464 10 2 3 9
## 1465 5 2 3 4
## 1466 17 3 3 5
## 1467 9 5 3 7
## 1468 6 0 3 6
## 1469 17 3 2 9
## 1470 6 3 4 4
## YearsInCurrentRole YearsSinceLastPromotion YearsWithCurrManager
## 1 4 0 5
## 2 7 1 7
## 3 0 0 0
## 4 7 3 0
## 5 2 2 2
## 6 7 3 6
## 7 0 0 0
## 8 0 0 0
## 9 7 1 8
## 10 7 7 7
## 11 4 0 3
## 12 5 0 8
## 13 2 4 3
## 14 2 1 2
## 15 2 0 3
## 16 9 8 8
## 17 2 0 5
## 18 0 0 0
## 19 8 3 7
## 20 2 1 2
## 21 2 1 3
## 22 3 0 3
## 23 6 2 11
## 24 0 0 0
## 25 2 1 3
## 26 13 4 8
## 27 2 6 7
## 28 7 4 2
## 29 6 5 17
## 30 2 2 1
## 31 1 0 0
## 32 2 1 3
## 33 0 1 8
## 34 0 0 0
## 35 0 2 0
## 36 3 1 4
## 37 2 0 2
## 38 2 2 2
## 39 1 0 0
## 40 3 1 3
## 41 0 0 0
## 42 0 0 0
## 43 0 0 1
## 44 8 1 7
## 45 8 3 7
## 46 15 15 8
## 47 5 8 7
## 48 0 0 0
## 49 6 0 8
## 50 0 0 1
## 51 0 0 0
## 52 2 2 2
## 53 3 1 3
## 54 0 2 3
## 55 2 0 0
## 56 14 8 12
## 57 8 1 8
## 58 2 2 2
## 59 7 1 7
## 60 5 0 7
## 61 8 7 8
## 62 3 9 9
## 63 3 13 8
## 64 16 7 9
## 65 14 12 8
## 66 0 0 2
## 67 5 0 3
## 68 0 0 0
## 69 2 1 2
## 70 0 0 0
## 71 3 1 3
## 72 4 0 4
## 73 1 1 0
## 74 4 0 9
## 75 0 0 0
## 76 7 1 8
## 77 13 2 8
## 78 0 0 0
## 79 2 0 2
## 80 2 0 2
## 81 8 3 0
## 82 4 0 4
## 83 0 1 0
## 84 11 1 9
## 85 2 1 3
## 86 4 0 2
## 87 0 0 0
## 88 2 0 3
## 89 10 10 8
## 90 8 4 7
## 91 3 11 11
## 92 7 1 0
## 93 8 2 7
## 94 9 9 5
## 95 8 5 7
## 96 3 0 3
## 97 2 0 2
## 98 4 0 4
## 99 10 1 8
## 100 2 1 2
## 101 2 0 2
## 102 0 0 0
## 103 0 1 0
## 104 13 2 10
## 105 0 0 0
## 106 2 2 2
## 107 3 0 7
## 108 4 0 3
## 109 2 2 1
## 110 0 0 0
## 111 12 12 8
## 112 7 0 6
## 113 3 4 4
## 114 3 1 2
## 115 0 0 0
## 116 3 1 3
## 117 10 5 8
## 118 7 2 8
## 119 0 0 0
## 120 12 4 12
## 121 7 0 9
## 122 7 0 7
## 123 4 4 3
## 124 18 15 15
## 125 7 7 7
## 126 4 4 3
## 127 10 15 6
## 128 0 0 0
## 129 1 2 1
## 130 11 5 11
## 131 2 1 2
## 132 2 2 1
## 133 2 2 2
## 134 7 0 7
## 135 7 5 7
## 136 2 0 1
## 137 2 0 3
## 138 11 5 9
## 139 2 2 2
## 140 9 4 7
## 141 4 0 4
## 142 5 0 3
## 143 2 2 2
## 144 4 0 4
## 145 7 0 7
## 146 2 2 2
## 147 3 1 2
## 148 6 7 7
## 149 0 1 4
## 150 0 0 0
## 151 7 2 13
## 152 8 0 6
## 153 7 4 5
## 154 9 0 15
## 155 8 3 7
## 156 7 0 9
## 157 0 0 0
## 158 0 0 0
## 159 11 2 11
## 160 3 1 2
## 161 2 2 2
## 162 2 2 2
## 163 2 0 2
## 164 3 1 4
## 165 1 0 1
## 166 4 4 4
## 167 8 1 8
## 168 9 7 4
## 169 3 0 7
## 170 4 1 4
## 171 2 1 1
## 172 0 0 0
## 173 7 7 2
## 174 9 6 10
## 175 4 0 3
## 176 2 2 2
## 177 1 0 2
## 178 0 1 0
## 179 13 15 7
## 180 2 2 1
## 181 2 0 2
## 182 2 1 2
## 183 3 0 2
## 184 2 0 2
## 185 2 1 3
## 186 7 1 6
## 187 8 9 9
## 188 15 2 15
## 189 9 1 9
## 190 7 1 0
## 191 18 11 9
## 192 7 0 3
## 193 0 0 1
## 194 4 2 2
## 195 8 11 8
## 196 7 1 7
## 197 2 1 2
## 198 3 0 2
## 199 0 0 0
## 200 7 1 7
## 201 2 2 0
## 202 7 1 7
## 203 3 2 0
## 204 2 3 7
## 205 0 0 0
## 206 4 1 9
## 207 2 2 2
## 208 1 1 7
## 209 2 0 2
## 210 2 0 3
## 211 8 9 8
## 212 8 5 8
## 213 7 0 7
## 214 9 4 7
## 215 2 0 4
## 216 0 0 0
## 217 3 0 1
## 218 2 1 5
## 219 7 12 8
## 220 2 0 5
## 221 11 3 7
## 222 2 1 1
## 223 8 0 7
## 224 10 5 13
## 225 4 1 4
## 226 0 0 0
## 227 8 5 8
## 228 10 4 1
## 229 7 0 8
## 230 3 0 1
## 231 2 2 2
## 232 17 11 15
## 233 2 2 2
## 234 0 0 6
## 235 4 0 4
## 236 13 1 9
## 237 7 1 7
## 238 7 15 12
## 239 3 1 2
## 240 2 1 2
## 241 2 1 2
## 242 2 2 2
## 243 0 0 0
## 244 8 4 7
## 245 0 1 7
## 246 2 2 2
## 247 2 0 2
## 248 9 5 9
## 249 1 0 2
## 250 2 0 2
## 251 0 0 0
## 252 6 11 8
## 253 5 1 5
## 254 2 0 2
## 255 2 0 2
## 256 2 1 2
## 257 6 7 8
## 258 7 3 9
## 259 0 0 0
## 260 4 1 4
## 261 1 0 3
## 262 7 7 5
## 263 2 0 4
## 264 7 7 7
## 265 0 0 0
## 266 0 0 0
## 267 0 7 8
## 268 3 1 5
## 269 7 4 10
## 270 13 10 11
## 271 10 4 13
## 272 7 9 9
## 273 2 0 4
## 274 4 0 1
## 275 2 1 0
## 276 2 0 2
## 277 7 6 2
## 278 0 7 7
## 279 5 1 4
## 280 4 1 6
## 281 3 1 3
## 282 16 11 6
## 283 7 0 9
## 284 7 0 8
## 285 3 3 3
## 286 12 5 7
## 287 0 0 0
## 288 2 1 2
## 289 2 0 2
## 290 3 0 2
## 291 2 1 2
## 292 0 7 7
## 293 2 2 2
## 294 7 7 4
## 295 2 1 2
## 296 4 4 8
## 297 0 0 0
## 298 9 6 9
## 299 0 0 0
## 300 1 1 2
## 301 10 0 4
## 302 0 0 0
## 303 3 0 7
## 304 7 7 7
## 305 10 3 7
## 306 8 0 8
## 307 8 4 8
## 308 8 0 1
## 309 3 1 2
## 310 2 0 3
## 311 2 1 0
## 312 9 9 11
## 313 2 2 2
## 314 6 8 8
## 315 9 11 10
## 316 7 1 7
## 317 1 0 7
## 318 2 7 7
## 319 3 1 2
## 320 12 11 9
## 321 4 0 4
## 322 7 1 7
## 323 7 0 7
## 324 2 2 2
## 325 8 1 9
## 326 9 8 9
## 327 9 13 3
## 328 0 0 0
## 329 7 0 1
## 330 2 1 1
## 331 7 0 0
## 332 4 1 4
## 333 3 0 3
## 334 0 0 0
## 335 9 9 8
## 336 3 0 2
## 337 7 0 7
## 338 2 2 2
## 339 9 1 2
## 340 7 0 7
## 341 2 0 4
## 342 10 2 9
## 343 9 4 10
## 344 7 1 7
## 345 6 0 13
## 346 2 0 2
## 347 4 1 2
## 348 0 0 0
## 349 0 0 0
## 350 3 0 2
## 351 2 1 0
## 352 0 0 7
## 353 0 0 0
## 354 7 6 7
## 355 2 2 2
## 356 7 7 7
## 357 0 0 0
## 358 2 1 2
## 359 0 0 0
## 360 9 1 12
## 361 2 1 2
## 362 7 1 7
## 363 2 2 2
## 364 0 0 0
## 365 4 0 3
## 366 0 0 0
## 367 7 7 7
## 368 3 1 3
## 369 4 1 0
## 370 2 2 2
## 371 0 0 0
## 372 2 1 2
## 373 1 0 0
## 374 2 0 4
## 375 5 0 7
## 376 2 0 0
## 377 7 0 7
## 378 2 2 2
## 379 2 0 4
## 380 4 1 2
## 381 2 0 3
## 382 0 0 0
## 383 4 0 4
## 384 1 1 2
## 385 9 9 0
## 386 0 0 0
## 387 7 12 17
## 388 4 1 3
## 389 2 1 2
## 390 0 0 1
## 391 10 3 9
## 392 0 0 0
## 393 2 1 2
## 394 2 1 2
## 395 10 3 12
## 396 2 1 2
## 397 2 0 2
## 398 4 1 3
## 399 11 10 7
## 400 2 3 2
## 401 8 1 6
## 402 7 7 7
## 403 4 4 4
## 404 7 0 7
## 405 0 1 8
## 406 2 0 2
## 407 4 0 4
## 408 2 0 2
## 409 2 1 4
## 410 4 0 2
## 411 4 0 2
## 412 8 11 10
## 413 7 0 8
## 414 5 0 4
## 415 3 1 4
## 416 2 1 0
## 417 0 0 0
## 418 15 1 12
## 419 2 0 2
## 420 3 1 4
## 421 2 0 7
## 422 2 1 1
## 423 0 0 0
## 424 8 7 8
## 425 0 0 0
## 426 10 15 7
## 427 7 6 7
## 428 13 13 11
## 429 0 0 4
## 430 2 0 1
## 431 4 4 3
## 432 0 0 0
## 433 3 5 7
## 434 2 1 2
## 435 8 0 8
## 436 6 7 7
## 437 3 1 3
## 438 2 1 2
## 439 7 0 0
## 440 0 0 0
## 441 2 0 2
## 442 2 2 0
## 443 3 9 7
## 444 2 1 2
## 445 7 6 7
## 446 9 7 7
## 447 3 1 10
## 448 9 6 9
## 449 6 5 13
## 450 3 0 7
## 451 5 0 5
## 452 9 5 8
## 453 7 3 7
## 454 7 1 0
## 455 2 1 2
## 456 1 0 5
## 457 4 0 1
## 458 0 0 0
## 459 0 0 1
## 460 7 0 7
## 461 0 0 0
## 462 4 0 3
## 463 7 5 7
## 464 0 0 0
## 465 5 1 2
## 466 2 1 2
## 467 16 11 8
## 468 5 7 7
## 469 9 6 9
## 470 2 1 2
## 471 2 2 1
## 472 0 0 0
## 473 5 4 3
## 474 9 0 9
## 475 5 1 4
## 476 3 1 4
## 477 0 0 0
## 478 5 10 7
## 479 4 0 6
## 480 3 1 2
## 481 0 0 0
## 482 5 1 3
## 483 1 1 2
## 484 2 0 2
## 485 7 5 7
## 486 3 0 3
## 487 0 1 0
## 488 0 0 0
## 489 2 2 2
## 490 0 0 0
## 491 0 0 0
## 492 7 4 7
## 493 2 0 2
## 494 2 0 2
## 495 2 0 6
## 496 3 0 2
## 497 2 1 2
## 498 2 0 1
## 499 2 2 2
## 500 0 1 2
## 501 4 0 5
## 502 0 0 0
## 503 7 8 10
## 504 9 8 8
## 505 1 0 0
## 506 2 0 2
## 507 7 7 8
## 508 0 1 2
## 509 11 11 8
## 510 11 4 7
## 511 7 7 1
## 512 2 0 2
## 513 2 1 1
## 514 0 1 1
## 515 8 9 7
## 516 0 0 0
## 517 2 0 2
## 518 2 1 2
## 519 7 0 5
## 520 7 2 8
## 521 2 2 2
## 522 5 0 4
## 523 0 0 0
## 524 8 3 8
## 525 7 7 7
## 526 2 2 0
## 527 7 11 10
## 528 7 0 8
## 529 3 1 3
## 530 7 1 8
## 531 7 0 7
## 532 7 7 7
## 533 7 1 7
## 534 13 1 12
## 535 1 1 3
## 536 6 12 6
## 537 2 2 2
## 538 7 1 7
## 539 7 2 10
## 540 2 2 2
## 541 7 3 7
## 542 7 0 5
## 543 0 0 0
## 544 2 1 4
## 545 2 11 13
## 546 7 7 7
## 547 0 0 0
## 548 2 2 2
## 549 2 2 2
## 550 1 4 3
## 551 3 0 2
## 552 3 3 6
## 553 7 1 1
## 554 2 2 3
## 555 6 0 7
## 556 2 2 2
## 557 2 2 2
## 558 0 0 0
## 559 8 4 7
## 560 0 0 1
## 561 0 0 0
## 562 6 1 16
## 563 9 7 8
## 564 5 1 4
## 565 7 3 1
## 566 2 0 2
## 567 4 1 3
## 568 5 1 4
## 569 2 1 4
## 570 7 0 9
## 571 3 1 2
## 572 2 2 0
## 573 7 1 1
## 574 3 1 2
## 575 2 1 3
## 576 3 1 4
## 577 2 1 1
## 578 3 4 3
## 579 7 4 12
## 580 5 1 3
## 581 2 0 2
## 582 2 0 2
## 583 7 3 7
## 584 2 1 2
## 585 7 14 9
## 586 0 0 0
## 587 0 0 0
## 588 2 1 4
## 589 3 0 3
## 590 0 0 0
## 591 9 3 7
## 592 2 0 2
## 593 14 3 0
## 594 7 0 5
## 595 7 0 7
## 596 15 13 8
## 597 2 2 2
## 598 3 0 2
## 599 2 2 1
## 600 2 0 4
## 601 13 6 8
## 602 0 0 0
## 603 0 0 0
## 604 0 0 0
## 605 8 7 8
## 606 3 1 2
## 607 2 1 3
## 608 8 7 7
## 609 7 7 3
## 610 1 1 0
## 611 8 0 8
## 612 5 1 6
## 613 7 7 5
## 614 2 0 2
## 615 7 1 7
## 616 0 0 0
## 617 6 4 17
## 618 2 2 3
## 619 3 0 1
## 620 8 0 8
## 621 5 1 1
## 622 14 4 11
## 623 2 0 3
## 624 4 0 3
## 625 2 0 4
## 626 4 0 2
## 627 3 1 3
## 628 8 0 0
## 629 0 0 0
## 630 1 0 4
## 631 2 2 2
## 632 2 0 2
## 633 1 2 2
## 634 4 0 3
## 635 3 0 3
## 636 14 5 15
## 637 2 7 8
## 638 0 0 2
## 639 2 0 3
## 640 2 0 2
## 641 2 3 4
## 642 8 5 3
## 643 2 1 2
## 644 3 1 3
## 645 4 0 2
## 646 2 0 2
## 647 0 2 2
## 648 10 4 8
## 649 4 0 0
## 650 9 3 8
## 651 2 2 2
## 652 6 7 7
## 653 0 0 9
## 654 6 14 7
## 655 4 0 4
## 656 3 0 3
## 657 0 0 1
## 658 3 0 3
## 659 7 7 1
## 660 2 0 2
## 661 0 0 0
## 662 0 0 0
## 663 2 0 2
## 664 0 0 0
## 665 8 4 11
## 666 2 1 2
## 667 2 0 2
## 668 7 1 0
## 669 2 2 2
## 670 2 2 2
## 671 1 0 0
## 672 0 1 0
## 673 4 0 3
## 674 2 0 2
## 675 0 0 4
## 676 7 5 2
## 677 6 0 7
## 678 9 1 7
## 679 0 0 0
## 680 8 0 0
## 681 2 0 1
## 682 12 5 11
## 683 0 0 2
## 684 0 0 0
## 685 0 0 0
## 686 7 0 7
## 687 13 2 17
## 688 8 3 9
## 689 0 0 0
## 690 0 0 0
## 691 7 8 5
## 692 1 0 0
## 693 7 6 3
## 694 7 3 7
## 695 0 1 4
## 696 1 11 7
## 697 7 0 8
## 698 1 0 2
## 699 2 1 0
## 700 8 7 8
## 701 0 0 0
## 702 13 15 2
## 703 4 7 7
## 704 3 1 3
## 705 9 0 8
## 706 7 0 7
## 707 0 0 0
## 708 10 2 7
## 709 7 0 7
## 710 2 1 2
## 711 8 6 0
## 712 0 0 0
## 713 2 0 2
## 714 3 0 2
## 715 4 1 3
## 716 5 1 2
## 717 16 0 11
## 718 2 2 2
## 719 8 0 8
## 720 0 0 7
## 721 2 0 1
## 722 11 1 5
## 723 1 0 2
## 724 7 0 7
## 725 2 3 2
## 726 2 0 2
## 727 3 0 3
## 728 0 0 0
## 729 7 0 8
## 730 10 10 1
## 731 7 1 7
## 732 0 0 0
## 733 2 1 2
## 734 7 1 3
## 735 3 1 1
## 736 2 2 2
## 737 11 4 8
## 738 2 2 1
## 739 6 11 8
## 740 2 2 2
## 741 2 1 2
## 742 0 0 0
## 743 0 0 0
## 744 3 4 3
## 745 0 0 0
## 746 7 6 7
## 747 16 5 10
## 748 0 0 0
## 749 2 2 2
## 750 14 6 9
## 751 11 11 11
## 752 7 0 13
## 753 13 15 14
## 754 6 2 8
## 755 2 2 2
## 756 3 1 1
## 757 7 7 7
## 758 1 0 9
## 759 4 0 4
## 760 3 0 4
## 761 7 6 12
## 762 0 0 0
## 763 2 1 2
## 764 1 0 0
## 765 0 0 0
## 766 2 2 2
## 767 1 7 7
## 768 3 0 1
## 769 7 7 5
## 770 3 1 3
## 771 2 2 2
## 772 4 7 0
## 773 4 0 3
## 774 8 7 8
## 775 7 6 2
## 776 0 0 0
## 777 2 0 2
## 778 0 1 0
## 779 13 1 7
## 780 0 2 7
## 781 7 1 9
## 782 3 1 3
## 783 2 0 2
## 784 6 0 8
## 785 9 1 9
## 786 10 11 1
## 787 2 1 2
## 788 2 1 2
## 789 7 1 7
## 790 6 3 6
## 791 2 1 2
## 792 7 4 7
## 793 7 3 8
## 794 2 2 2
## 795 2 0 4
## 796 0 0 0
## 797 7 5 6
## 798 1 0 0
## 799 2 2 2
## 800 6 13 7
## 801 0 0 0
## 802 0 0 0
## 803 2 0 2
## 804 0 0 0
## 805 4 2 1
## 806 10 4 12
## 807 6 4 0
## 808 7 2 7
## 809 5 1 7
## 810 7 1 7
## 811 9 4 9
## 812 0 0 0
## 813 7 0 1
## 814 7 11 5
## 815 7 4 9
## 816 2 2 2
## 817 4 0 3
## 818 9 0 9
## 819 2 2 2
## 820 3 0 4
## 821 2 0 2
## 822 8 2 3
## 823 2 0 2
## 824 5 1 1
## 825 0 0 0
## 826 7 1 2
## 827 6 5 0
## 828 1 0 2
## 829 0 0 0
## 830 2 0 3
## 831 0 0 0
## 832 2 2 2
## 833 2 1 3
## 834 2 2 2
## 835 5 1 3
## 836 4 1 4
## 837 8 3 10
## 838 7 2 13
## 839 9 13 14
## 840 4 1 5
## 841 0 1 7
## 842 3 1 2
## 843 1 0 0
## 844 7 0 7
## 845 3 1 4
## 846 1 0 0
## 847 7 6 4
## 848 9 4 9
## 849 2 2 2
## 850 3 1 3
## 851 0 0 0
## 852 2 4 2
## 853 8 0 8
## 854 1 0 0
## 855 2 0 2
## 856 8 3 11
## 857 2 1 2
## 858 4 0 2
## 859 7 4 7
## 860 4 1 3
## 861 0 0 0
## 862 15 15 9
## 863 0 0 0
## 864 4 1 4
## 865 0 0 0
## 866 3 0 3
## 867 0 0 0
## 868 2 2 2
## 869 5 3 3
## 870 2 0 3
## 871 7 1 7
## 872 0 0 0
## 873 7 0 1
## 874 7 1 7
## 875 7 8 9
## 876 11 13 17
## 877 1 2 2
## 878 3 1 4
## 879 3 0 3
## 880 7 1 9
## 881 2 2 2
## 882 7 0 7
## 883 7 7 7
## 884 10 4 10
## 885 1 1 3
## 886 3 1 1
## 887 9 5 7
## 888 0 0 0
## 889 2 4 12
## 890 7 6 8
## 891 4 1 4
## 892 5 7 7
## 893 1 0 0
## 894 2 1 2
## 895 9 0 9
## 896 5 0 1
## 897 9 1 8
## 898 1 0 5
## 899 7 0 7
## 900 0 0 0
## 901 10 0 7
## 902 0 0 0
## 903 3 0 3
## 904 0 1 0
## 905 0 0 0
## 906 7 1 7
## 907 0 0 0
## 908 9 3 10
## 909 8 0 8
## 910 0 0 1
## 911 0 0 1
## 912 0 1 0
## 913 5 2 2
## 914 10 1 11
## 915 9 15 0
## 916 2 2 2
## 917 4 0 8
## 918 2 0 2
## 919 10 11 10
## 920 7 5 4
## 921 8 5 10
## 922 2 0 2
## 923 9 14 13
## 924 9 1 8
## 925 2 2 2
## 926 0 0 0
## 927 7 15 17
## 928 14 5 12
## 929 7 0 5
## 930 2 2 2
## 931 0 7 7
## 932 4 1 7
## 933 7 7 7
## 934 2 1 2
## 935 2 2 1
## 936 7 0 8
## 937 0 0 0
## 938 9 15 2
## 939 2 2 2
## 940 4 1 1
## 941 2 0 3
## 942 2 6 7
## 943 7 1 7
## 944 6 5 7
## 945 8 7 5
## 946 2 1 2
## 947 4 1 0
## 948 7 7 7
## 949 1 0 8
## 950 7 1 7
## 951 8 9 6
## 952 2 11 9
## 953 2 2 2
## 954 2 1 2
## 955 8 2 10
## 956 9 9 11
## 957 3 7 7
## 958 2 0 0
## 959 7 1 6
## 960 8 8 8
## 961 3 1 7
## 962 6 1 1
## 963 9 0 10
## 964 7 1 7
## 965 8 9 6
## 966 2 1 2
## 967 9 5 9
## 968 2 0 3
## 969 7 6 13
## 970 9 0 8
## 971 0 0 0
## 972 2 0 3
## 973 0 0 0
## 974 7 0 0
## 975 2 0 0
## 976 7 3 8
## 977 16 15 9
## 978 4 0 0
## 979 11 11 8
## 980 7 7 7
## 981 0 0 0
## 982 2 3 2
## 983 2 0 3
## 984 11 4 11
## 985 3 0 4
## 986 2 6 7
## 987 4 1 4
## 988 2 3 2
## 989 2 3 3
## 990 4 1 3
## 991 4 1 2
## 992 2 1 2
## 993 4 0 5
## 994 2 1 2
## 995 3 0 2
## 996 7 1 8
## 997 2 4 4
## 998 2 7 7
## 999 3 1 1
## 1000 7 0 9
## 1001 4 0 4
## 1002 2 0 2
## 1003 2 2 2
## 1004 2 0 2
## 1005 7 0 7
## 1006 9 0 9
## 1007 3 1 3
## 1008 7 7 7
## 1009 7 12 7
## 1010 8 1 5
## 1011 7 0 0
## 1012 4 0 1
## 1013 0 0 0
## 1014 2 0 2
## 1015 2 1 0
## 1016 3 1 3
## 1017 0 0 0
## 1018 0 1 4
## 1019 4 1 8
## 1020 2 0 2
## 1021 5 1 2
## 1022 2 2 2
## 1023 5 1 3
## 1024 2 1 0
## 1025 17 5 6
## 1026 4 0 3
## 1027 3 0 3
## 1028 4 0 2
## 1029 2 0 3
## 1030 7 0 7
## 1031 7 9 9
## 1032 7 4 3
## 1033 0 0 0
## 1034 8 4 7
## 1035 7 0 7
## 1036 2 0 2
## 1037 2 2 2
## 1038 1 1 2
## 1039 2 2 2
## 1040 2 2 2
## 1041 8 4 8
## 1042 4 1 3
## 1043 2 0 3
## 1044 8 8 8
## 1045 2 1 2
## 1046 1 1 2
## 1047 2 1 3
## 1048 3 0 3
## 1049 9 3 12
## 1050 1 2 2
## 1051 9 5 8
## 1052 0 0 0
## 1053 0 0 0
## 1054 9 2 8
## 1055 7 0 7
## 1056 8 6 9
## 1057 2 1 2
## 1058 3 0 0
## 1059 9 10 10
## 1060 0 0 0
## 1061 0 0 0
## 1062 0 0 0
## 1063 2 2 2
## 1064 7 0 4
## 1065 3 1 3
## 1066 2 1 2
## 1067 4 0 2
## 1068 4 1 2
## 1069 0 0 0
## 1070 0 0 0
## 1071 2 1 3
## 1072 2 1 2
## 1073 2 0 2
## 1074 6 1 7
## 1075 0 0 0
## 1076 2 0 0
## 1077 9 1 12
## 1078 7 0 7
## 1079 6 14 17
## 1080 0 2 2
## 1081 12 5 1
## 1082 8 3 3
## 1083 3 1 2
## 1084 4 1 0
## 1085 9 8 6
## 1086 2 7 7
## 1087 6 13 9
## 1088 10 5 7
## 1089 0 0 0
## 1090 8 3 7
## 1091 7 0 7
## 1092 3 0 2
## 1093 4 0 3
## 1094 8 13 9
## 1095 4 7 1
## 1096 14 5 7
## 1097 7 7 7
## 1098 1 0 0
## 1099 2 2 2
## 1100 8 7 7
## 1101 3 4 2
## 1102 1 2 5
## 1103 2 1 2
## 1104 7 7 7
## 1105 0 0 0
## 1106 2 2 2
## 1107 8 6 7
## 1108 3 1 2
## 1109 0 1 2
## 1110 2 1 3
## 1111 0 0 0
## 1112 7 1 9
## 1113 2 1 4
## 1114 5 1 2
## 1115 2 7 6
## 1116 0 0 0
## 1117 6 2 13
## 1118 2 2 1
## 1119 0 0 0
## 1120 8 7 7
## 1121 2 1 2
## 1122 0 0 0
## 1123 9 1 5
## 1124 2 0 4
## 1125 6 7 6
## 1126 4 1 3
## 1127 0 0 0
## 1128 2 2 2
## 1129 2 0 2
## 1130 0 0 1
## 1131 9 6 8
## 1132 2 7 7
## 1133 4 1 2
## 1134 4 0 2
## 1135 0 0 1
## 1136 0 0 12
## 1137 1 0 0
## 1138 2 1 3
## 1139 8 12 13
## 1140 2 0 2
## 1141 7 1 10
## 1142 4 1 1
## 1143 7 1 4
## 1144 7 4 5
## 1145 2 0 3
## 1146 2 1 1
## 1147 7 7 2
## 1148 6 1 4
## 1149 7 7 7
## 1150 7 0 7
## 1151 15 1 10
## 1152 3 0 0
## 1153 2 2 2
## 1154 0 0 0
## 1155 2 1 0
## 1156 8 0 7
## 1157 15 14 12
## 1158 11 2 9
## 1159 3 0 2
## 1160 2 3 8
## 1161 7 3 9
## 1162 4 0 2
## 1163 12 6 0
## 1164 4 7 7
## 1165 2 3 3
## 1166 7 0 2
## 1167 2 2 2
## 1168 2 2 2
## 1169 3 1 3
## 1170 2 0 2
## 1171 2 1 2
## 1172 2 0 3
## 1173 2 1 4
## 1174 2 0 2
## 1175 7 0 7
## 1176 4 1 0
## 1177 2 1 2
## 1178 11 1 11
## 1179 2 2 2
## 1180 8 7 9
## 1181 3 1 3
## 1182 11 2 12
## 1183 2 1 2
## 1184 4 0 4
## 1185 8 4 7
## 1186 10 6 11
## 1187 9 6 7
## 1188 11 2 9
## 1189 0 0 9
## 1190 2 0 4
## 1191 5 1 6
## 1192 0 0 2
## 1193 2 2 2
## 1194 3 3 3
## 1195 2 1 2
## 1196 7 0 0
## 1197 0 0 2
## 1198 2 0 2
## 1199 5 1 2
## 1200 2 0 2
## 1201 2 2 2
## 1202 4 1 2
## 1203 0 0 0
## 1204 3 1 2
## 1205 7 7 7
## 1206 0 0 0
## 1207 7 5 7
## 1208 2 1 2
## 1209 10 4 13
## 1210 0 0 0
## 1211 11 2 13
## 1212 3 1 3
## 1213 9 8 7
## 1214 2 0 2
## 1215 7 6 7
## 1216 4 1 4
## 1217 9 8 8
## 1218 3 0 4
## 1219 8 8 7
## 1220 3 1 2
## 1221 7 6 7
## 1222 15 14 4
## 1223 0 0 0
## 1224 5 14 10
## 1225 2 0 2
## 1226 6 8 6
## 1227 2 1 3
## 1228 2 0 3
## 1229 2 1 2
## 1230 0 0 0
## 1231 2 1 2
## 1232 7 0 9
## 1233 7 0 7
## 1234 0 0 8
## 1235 0 0 0
## 1236 6 0 3
## 1237 2 2 2
## 1238 0 0 0
## 1239 2 1 2
## 1240 4 1 4
## 1241 8 5 8
## 1242 3 0 2
## 1243 8 12 8
## 1244 0 0 3
## 1245 7 0 7
## 1246 2 2 1
## 1247 2 1 2
## 1248 2 0 2
## 1249 2 1 4
## 1250 2 2 2
## 1251 2 0 2
## 1252 7 1 7
## 1253 0 1 2
## 1254 7 1 7
## 1255 1 0 4
## 1256 0 0 0
## 1257 1 0 5
## 1258 0 0 0
## 1259 0 0 0
## 1260 0 1 7
## 1261 7 0 7
## 1262 0 1 0
## 1263 0 0 0
## 1264 3 0 4
## 1265 0 0 0
## 1266 8 1 7
## 1267 3 0 4
## 1268 14 0 7
## 1269 2 0 2
## 1270 7 1 8
## 1271 2 2 2
## 1272 0 1 0
## 1273 4 0 4
## 1274 0 0 0
## 1275 7 5 7
## 1276 2 2 2
## 1277 2 0 3
## 1278 1 2 2
## 1279 8 5 7
## 1280 3 2 3
## 1281 0 4 7
## 1282 7 7 7
## 1283 8 7 6
## 1284 3 0 3
## 1285 3 2 2
## 1286 2 2 2
## 1287 3 0 2
## 1288 0 0 0
## 1289 7 1 7
## 1290 10 3 8
## 1291 1 0 7
## 1292 3 0 8
## 1293 1 2 2
## 1294 2 1 2
## 1295 2 1 2
## 1296 14 13 5
## 1297 7 0 2
## 1298 1 1 4
## 1299 7 3 7
## 1300 3 0 3
## 1301 8 4 8
## 1302 9 14 14
## 1303 4 0 3
## 1304 11 14 10
## 1305 4 7 7
## 1306 1 1 7
## 1307 2 2 2
## 1308 2 1 2
## 1309 2 0 3
## 1310 3 1 2
## 1311 2 2 2
## 1312 0 0 0
## 1313 0 0 0
## 1314 2 2 0
## 1315 7 5 7
## 1316 0 0 0
## 1317 7 7 7
## 1318 4 0 2
## 1319 8 3 10
## 1320 0 0 0
## 1321 3 3 3
## 1322 2 2 0
## 1323 8 4 7
## 1324 2 2 2
## 1325 0 1 6
## 1326 0 0 0
## 1327 2 2 2
## 1328 17 2 8
## 1329 8 5 8
## 1330 0 0 0
## 1331 12 6 14
## 1332 10 12 9
## 1333 0 1 0
## 1334 7 0 7
## 1335 6 7 3
## 1336 2 2 2
## 1337 2 0 4
## 1338 0 0 0
## 1339 0 0 0
## 1340 0 0 0
## 1341 9 1 7
## 1342 8 0 2
## 1343 3 1 0
## 1344 2 1 2
## 1345 3 0 4
## 1346 2 0 2
## 1347 8 3 8
## 1348 1 0 8
## 1349 7 11 16
## 1350 0 1 0
## 1351 7 0 7
## 1352 17 4 7
## 1353 0 2 2
## 1354 2 3 0
## 1355 2 1 0
## 1356 2 1 3
## 1357 4 1 4
## 1358 7 5 7
## 1359 2 1 4
## 1360 2 0 1
## 1361 2 2 2
## 1362 7 7 7
## 1363 2 0 3
## 1364 4 0 9
## 1365 7 0 7
## 1366 0 0 0
## 1367 2 1 2
## 1368 3 1 2
## 1369 10 6 11
## 1370 0 2 2
## 1371 7 1 7
## 1372 0 0 0
## 1373 0 7 9
## 1374 11 0 7
## 1375 0 0 0
## 1376 1 0 3
## 1377 2 0 3
## 1378 4 4 3
## 1379 0 0 0
## 1380 0 0 0
## 1381 3 0 4
## 1382 3 1 4
## 1383 2 2 3
## 1384 4 0 2
## 1385 8 2 1
## 1386 7 1 1
## 1387 7 7 6
## 1388 3 0 2
## 1389 1 1 2
## 1390 8 8 7
## 1391 1 0 3
## 1392 0 0 0
## 1393 8 0 9
## 1394 7 0 7
## 1395 0 0 0
## 1396 7 0 8
## 1397 2 2 2
## 1398 3 2 3
## 1399 7 2 8
## 1400 9 0 9
## 1401 2 1 2
## 1402 9 1 4
## 1403 0 0 0
## 1404 8 11 10
## 1405 9 3 7
## 1406 7 4 8
## 1407 2 1 4
## 1408 3 1 2
## 1409 2 1 4
## 1410 9 9 4
## 1411 8 5 10
## 1412 0 1 2
## 1413 8 1 7
## 1414 2 0 2
## 1415 14 12 11
## 1416 1 0 0
## 1417 13 5 8
## 1418 2 1 2
## 1419 3 7 7
## 1420 3 1 2
## 1421 3 1 0
## 1422 8 5 12
## 1423 2 2 2
## 1424 2 1 2
## 1425 7 0 8
## 1426 7 8 1
## 1427 2 4 1
## 1428 0 0 0
## 1429 2 2 2
## 1430 7 1 7
## 1431 16 1 11
## 1432 10 5 7
## 1433 11 6 8
## 1434 3 0 4
## 1435 8 0 0
## 1436 3 1 2
## 1437 2 1 2
## 1438 0 1 3
## 1439 0 1 0
## 1440 7 3 4
## 1441 2 0 2
## 1442 12 1 9
## 1443 2 2 2
## 1444 6 4 14
## 1445 9 9 8
## 1446 7 0 10
## 1447 7 1 7
## 1448 12 11 11
## 1449 4 0 4
## 1450 2 1 2
## 1451 0 1 7
## 1452 7 1 9
## 1453 3 0 1
## 1454 3 0 0
## 1455 3 0 1
## 1456 2 2 2
## 1457 2 0 2
## 1458 3 0 2
## 1459 3 1 1
## 1460 3 0 3
## 1461 4 0 4
## 1462 2 2 0
## 1463 9 9 6
## 1464 4 1 7
## 1465 2 0 0
## 1466 2 0 3
## 1467 7 1 7
## 1468 2 0 3
## 1469 6 0 8
## 1470 3 1 2• Is any type of gender more dissatisfied than the other?
# Create Box plots for each attrition status
# Create a dataframe with only attrition, JobSatisfaction and Gender
gender.js <- empAttrn %>% select(Attrition, JobSatisfaction, Gender)
# Convert JobSatisfaction to numeric
gender.js$JobSatisfaction <- as.numeric(gender.js$JobSatisfaction)
#Create a boxplot of overall Job Satisfaction by Gender
gender.js.overall <- gender.js %>% ggplot(aes(x=Gender, y=JobSatisfaction, fill=Gender)) + geom_boxplot(color="black") + theme_minimal() + theme(plot.title=element_text(hjust=0.5)) + labs(title="Overall Job Satisfaction by Gender") + scale_fill_manual(values=c("#F781F3", "#819FF7"))
#Create a boxplot of Job Satisfaction by Gender
gender.js.Attrition <- gender.js %>%
ggplot(aes(x=Attrition, y=JobSatisfaction, fill=Attrition)) + geom_boxplot(color="black") + theme_minimal() + facet_wrap(~Gender) + theme(plot.title=element_text(hjust=0.5)) + labs(title="Job Satisfaction by Gender and Attrition Status") + scale_fill_manual(values=c("#9FF781","#FA5858"))
plot_grid(gender.js.overall, gender.js.Attrition, ncol=2)
It is evident that for employees who didn’t leave the organization, job satisfaction levels are the same. However, for employees who left the organization, females had a lower satisfaction level as opposed to males.
So, we could conclude that there is no gender bias with age and income levels. Even the attrition percentages are fairly same. However, the job satisfaction levels are low in the female employees who left the organization.
3. Can we validate the saying “people don’t leave jobs; they leave bad managers”?
• How is the Relationship Satisfaction for employees who quit the organization?
• What’s the average satisfaction rate by the status of the manager?
# Create a Categorical Value for Years with Current Manager
# Create a Categorical Value for RelationShip Satisfaction
# Then we will use income as our Y-Axis
yearsWithMgr <- ifelse(empAttrn$YearsWithCurrManager <= 1, "Recently Hired",
ifelse(empAttrn$YearsWithCurrManager > 1 & empAttrn$YearsWithCurrManager <= 4,
"2-4 Years hired", "Long Established Manager"))
mgrAnal <- cbind (empAttrn,yearsWithMgr)
# Determine what is the Average Relationship Satisfaction with the Recently Hired Managers
relSat.MgrYrs <- mgrAnal %>% select(yearsWithMgr, RelationshipSatisfaction, Attrition) %>% group_by(yearsWithMgr, Attrition) %>%
summarize(avg.sat=mean(RelationshipSatisfaction)) %>%
ggplot(aes(x=fct_reorder(yearsWithMgr,-avg.sat), y=avg.sat, fill=Attrition)) + geom_bar(stat="identity", position="dodge") + facet_wrap(~Attrition) +
geom_text(aes(x=yearsWithMgr, y=0, label= paste0(round(avg.sat,2))),
hjust=-0.5, vjust=0.5, size=4,
colour="black", fontface="italic",
angle=360) + coord_flip() + theme_bw() +
theme(legend.position="bottom", strip.background = element_blank(), strip.text.x = element_blank(), plot.title=element_text(hjust=0.5),axis.text.y = element_text(angle = 55)) +
labs(x="Years with Current Manager",y="Average Satisfaction Score", title="Dealing with Current Managers") +
scale_fill_manual(values=c("#58FA58", "#FA5858"))## `summarise()` regrouping output by 'yearsWithMgr' (override with `.groups` argument)# Relationship Satisfaction by attrition status
relSat.dist <- mgrAnal %>% select(RelationshipSatisfaction, Attrition) %>% group_by(Attrition) %>%
ggplot(aes(y=Attrition, x=factor(RelationshipSatisfaction), fill=Attrition)) + geom_bar(stat="identity") + facet_wrap(~Attrition) + theme_bw() + theme(legend.position="none", strip.background = element_blank(), strip.text.x = element_blank(), plot.title=element_text(hjust=0.5),axis.text.y = element_blank()) + labs(title="Relationship Satisfaction by Attrition",x="Relationship Satisfaction") +
scale_fill_manual(values=c("#58FA58", "#FA5858"))
plot_grid(relSat.dist, relSat.MgrYrs, nrow=2)
Overall Relationship Satisfaction are not bad. However, Employees that are dealing with recently hired managers have a lower satisfaction score than managers that have been there for a longer time.
• How is work life balance of employees who quit the organization?
# Overall work life balance of employees
wlb.dist <- empAttrn %>% select(WorkLifeBalance, EmployeeCount, Attrition) %>% group_by(Attrition) %>%
ggplot(aes(y=Attrition, x=factor(WorkLifeBalance), fill=Attrition)) + geom_bar(stat="identity") + facet_wrap(~Attrition) + theme_bw() + theme(legend.position="none", strip.background = element_blank(), strip.text.x = element_blank(), plot.title=element_text(hjust=0.5),axis.text.y = element_blank()) + labs(title="Work life balance by Attrition",x="WorkLifeBalance") +
scale_fill_manual(values=c("#58FA58", "#FA5858"))
wlb.dist
The worklife balance is better overall.
Correlation, Bi-Variate and Multi-Variate Analysis
# Lets first convert all categoric variables into factors
cat_cols <- c("Education", "EnvironmentSatisfaction", "JobInvolvement", "JobLevel",
"JobSatisfaction", "PerformanceRating", "RelationshipSatisfaction",
"StockOptionLevel", "TrainingTimesLastYear", "WorkLifeBalance")
df_attrn[cat_cols] <- lapply(df_attrn[cat_cols], factor)
# Lets create a dataframe with numeric columns only
df_attrn_num <- select_if(df_attrn, is.numeric)str(df_attrn)## 'data.frame': 1470 obs. of 35 variables:
## $ Age : int 41 49 37 33 27 32 59 30 38 36 ...
## $ Attrition : Factor w/ 2 levels "No","Yes": 2 1 2 1 1 1 1 1 1 1 ...
## $ BusinessTravel : Factor w/ 3 levels "Non-Travel","Travel_Frequently",..: 3 2 3 2 3 2 3 3 2 3 ...
## $ DailyRate : int 1102 279 1373 1392 591 1005 1324 1358 216 1299 ...
## $ Department : Factor w/ 3 levels "Human Resources",..: 3 2 2 2 2 2 2 2 2 2 ...
## $ DistanceFromHome : int 1 8 2 3 2 2 3 24 23 27 ...
## $ Education : Factor w/ 5 levels "1","2","3","4",..: 2 1 2 4 1 2 3 1 3 3 ...
## $ EducationField : Factor w/ 6 levels "Human Resources",..: 2 2 5 2 4 2 4 2 2 4 ...
## $ EmployeeCount : int 1 1 1 1 1 1 1 1 1 1 ...
## $ EmployeeNumber : int 1 2 4 5 7 8 10 11 12 13 ...
## $ EnvironmentSatisfaction : Factor w/ 4 levels "1","2","3","4": 2 3 4 4 1 4 3 4 4 3 ...
## $ Gender : Factor w/ 2 levels "Female","Male": 1 2 2 1 2 2 1 2 2 2 ...
## $ HourlyRate : int 94 61 92 56 40 79 81 67 44 94 ...
## $ JobInvolvement : Factor w/ 4 levels "1","2","3","4": 3 2 2 3 3 3 4 3 2 3 ...
## $ JobLevel : Factor w/ 5 levels "1","2","3","4",..: 2 2 1 1 1 1 1 1 3 2 ...
## $ JobRole : Factor w/ 9 levels "Healthcare Representative",..: 8 7 3 7 3 3 3 3 5 1 ...
## $ JobSatisfaction : Factor w/ 4 levels "1","2","3","4": 4 2 3 3 2 4 1 3 3 3 ...
## $ MaritalStatus : Factor w/ 3 levels "Divorced","Married",..: 3 2 3 2 2 3 2 1 3 2 ...
## $ MonthlyIncome : int 5993 5130 2090 2909 3468 3068 2670 2693 9526 5237 ...
## $ MonthlyRate : int 19479 24907 2396 23159 16632 11864 9964 13335 8787 16577 ...
## $ NumCompaniesWorked : int 8 1 6 1 9 0 4 1 0 6 ...
## $ Over18 : Factor w/ 1 level "Y": 1 1 1 1 1 1 1 1 1 1 ...
## $ OverTime : Factor w/ 2 levels "No","Yes": 2 1 2 2 1 1 2 1 1 1 ...
## $ PercentSalaryHike : int 11 23 15 11 12 13 20 22 21 13 ...
## $ PerformanceRating : Factor w/ 2 levels "3","4": 1 2 1 1 1 1 2 2 2 1 ...
## $ RelationshipSatisfaction: Factor w/ 4 levels "1","2","3","4": 1 4 2 3 4 3 1 2 2 2 ...
## $ StandardHours : int 80 80 80 80 80 80 80 80 80 80 ...
## $ StockOptionLevel : Factor w/ 4 levels "0","1","2","3": 1 2 1 1 2 1 4 2 1 3 ...
## $ TotalWorkingYears : int 8 10 7 8 6 8 12 1 10 17 ...
## $ TrainingTimesLastYear : Factor w/ 7 levels "0","1","2","3",..: 1 4 4 4 4 3 4 3 3 4 ...
## $ WorkLifeBalance : Factor w/ 4 levels "1","2","3","4": 1 3 3 3 3 2 2 3 3 2 ...
## $ YearsAtCompany : int 6 10 0 8 2 7 1 1 9 7 ...
## $ YearsInCurrentRole : int 4 7 0 7 2 7 0 0 7 7 ...
## $ YearsSinceLastPromotion : int 0 1 0 3 2 3 0 0 1 7 ...
## $ YearsWithCurrManager : int 5 7 0 0 2 6 0 0 8 7 ...# Lets build the correlation chart
chart.Correlation(df_attrn_num, histogram=TRUE, pch="+")
There exists correlation between few of the predictor pairs.Few of the most noticeable ones are below:
The higher the total working years, the higher the monthly income of an employee.
The higher the Years with current manager, the higher the Years at the company and the higher the Years in the current role.
Also, the higher the age, the higher the Total working years.
# Create box plots to understand variable correlation with the response variable Acquistion
x1.1 <- ggplot(df_attrn, aes(y=Age)) + geom_boxplot()
x1.2 <- ggplot(df_attrn, aes(x=Attrition, y=Age, fill=Attrition)) + geom_boxplot() + theme(axis.title.x=element_blank()) +
scale_fill_manual(values=c("#58FA58", "#FA5858"))
x1 <- plot_grid(x1.1,x1.2,align="v",axis="t",ncol=2)
x2.1 <- ggplot(df_attrn, aes(y=DailyRate)) + geom_boxplot()
x2.2 <- ggplot(df_attrn, aes(x=Attrition, y=DailyRate, fill=Attrition)) + geom_boxplot() + theme(axis.title.x=element_blank()) +
scale_fill_manual(values=c("#58FA58", "#FA5858"))
x2 <- plot_grid(x2.1,x2.2,align="v",axis="t",ncol=2)
x3.1 <- ggplot(df_attrn, aes(y=DistanceFromHome)) + geom_boxplot()
x3.2 <- ggplot(df_attrn, aes(x=Attrition, y=DistanceFromHome, fill=Attrition)) + geom_boxplot() + theme(axis.title.x=element_blank()) +
scale_fill_manual(values=c("#58FA58", "#FA5858"))
x3 <- plot_grid(x3.1,x3.2,align="v",axis="t",ncol=2)
x4.1 <- ggplot(df_attrn, aes(y=EmployeeCount)) + geom_boxplot()
x4.2 <- ggplot(df_attrn, aes(x=Attrition, y=EmployeeCount, fill=Attrition)) + geom_boxplot() + theme(axis.title.x=element_blank()) +
scale_fill_manual(values=c("#58FA58", "#FA5858"))
x4 <- plot_grid(x4.1,x4.2,align="v",axis="t",ncol=2)
x5.1 <- ggplot(df_attrn, aes(y=EmployeeNumber)) + geom_boxplot()
x5.2 <- ggplot(df_attrn, aes(x=Attrition, y=EmployeeNumber, fill=Attrition)) + geom_boxplot() + theme(axis.title.x=element_blank()) +
scale_fill_manual(values=c("#58FA58", "#FA5858"))
x5 <- plot_grid(x5.1,x5.2,align="v",axis="t",ncol=2)
x6.1 <- ggplot(df_attrn, aes(y=HourlyRate)) + geom_boxplot()
x6.2 <- ggplot(df_attrn, aes(x=Attrition, y=HourlyRate, fill=Attrition)) + geom_boxplot() + theme(axis.title.x=element_blank()) +
scale_fill_manual(values=c("#58FA58", "#FA5858"))
x6 <- plot_grid(x6.1,x6.2,align="v",axis="t",ncol=2)
x7.1 <- ggplot(df_attrn, aes(y=MonthlyIncome)) + geom_boxplot()
x7.2 <- ggplot(df_attrn, aes(x=Attrition, y=MonthlyIncome, fill=Attrition)) + geom_boxplot() + theme(axis.title.x=element_blank()) +
scale_fill_manual(values=c("#58FA58", "#FA5858"))
x7 <- plot_grid(x7.1,x7.2,align="v",axis="t",ncol=2)
x8.1 <- ggplot(df_attrn, aes(y=MonthlyRate)) + geom_boxplot()
x8.2 <- ggplot(df_attrn, aes(x=Attrition, y=MonthlyRate, fill=Attrition)) + geom_boxplot() + theme(axis.title.x=element_blank()) +
scale_fill_manual(values=c("#58FA58", "#FA5858"))
x8 <- plot_grid(x8.1,x8.2,align="v",axis="t",ncol=2)
x9.1 <- ggplot(df_attrn, aes(y=NumCompaniesWorked)) + geom_boxplot()
x9.2 <- ggplot(df_attrn, aes(x=Attrition, y=NumCompaniesWorked, fill=Attrition)) + geom_boxplot() + theme(axis.title.x=element_blank()) +
scale_fill_manual(values=c("#58FA58", "#FA5858"))
x9 <- plot_grid(x9.1,x9.2,align="v",axis="t",ncol=2)
x10.1 <- ggplot(df_attrn, aes(y=PercentSalaryHike)) + geom_boxplot()
x10.2 <- ggplot(df_attrn, aes(x=Attrition, y=PercentSalaryHike, fill=Attrition)) + geom_boxplot() + theme(axis.title.x=element_blank()) +
scale_fill_manual(values=c("#58FA58", "#FA5858"))
x10 <- plot_grid(x10.1,x10.2,align="v",axis="t",ncol=2)
x11.1 <- ggplot(df_attrn, aes(y=StandardHours)) + geom_boxplot()
x11.2 <- ggplot(df_attrn, aes(x=Attrition, y=StandardHours, fill=Attrition)) + geom_boxplot() + theme(axis.title.x=element_blank()) +
scale_fill_manual(values=c("#58FA58", "#FA5858"))
x11 <- plot_grid(x11.1,x11.2,align="v",axis="t",ncol=2)
x12.1 <- ggplot(df_attrn, aes(y=TotalWorkingYears)) + geom_boxplot()
x12.2 <- ggplot(df_attrn, aes(x=Attrition, y=TotalWorkingYears, fill=Attrition)) + geom_boxplot() + theme(axis.title.x=element_blank()) +
scale_fill_manual(values=c("#58FA58", "#FA5858"))
x12 <- plot_grid(x12.1,x12.2,align="v",axis="t",ncol=2)
x13.1 <- ggplot(df_attrn, aes(y=YearsAtCompany)) + geom_boxplot()
x13.2 <- ggplot(df_attrn, aes(x=Attrition, y=YearsAtCompany, fill=Attrition)) + geom_boxplot() + theme(axis.title.x=element_blank()) +
scale_fill_manual(values=c("#58FA58", "#FA5858"))
x13 <- plot_grid(x13.1,x13.2,align="v",axis="t",ncol=2)
x14.1 <- ggplot(df_attrn, aes(y=YearsInCurrentRole)) + geom_boxplot()
x14.2 <- ggplot(df_attrn, aes(x=Attrition, y=YearsInCurrentRole, fill=Attrition)) + geom_boxplot() + theme(axis.title.x=element_blank()) +
scale_fill_manual(values=c("#58FA58", "#FA5858"))
x14 <- plot_grid(x14.1,x14.2,align="v",axis="t",ncol=2)
x15.1 <- ggplot(df_attrn, aes(y=YearsSinceLastPromotion)) + geom_boxplot()
x15.2 <- ggplot(df_attrn, aes(x=Attrition, y=YearsSinceLastPromotion, fill=Attrition)) + geom_boxplot() + theme(axis.title.x=element_blank()) +
scale_fill_manual(values=c("#58FA58", "#FA5858"))
x15 <- plot_grid(x15.1,x15.2,align="v",axis="t",ncol=2)
x16.1 <- ggplot(df_attrn, aes(y=YearsWithCurrManager)) + geom_boxplot()
x16.2 <- ggplot(df_attrn, aes(x=Attrition, y=YearsWithCurrManager, fill=Attrition)) + geom_boxplot() + theme(axis.title.x=element_blank()) +
scale_fill_manual(values=c("#58FA58", "#FA5858"))
x16 <- plot_grid(x16.1,x16.2,align="v",axis="t",ncol=2)plot_grid(x1,x2,x3,x4,x5,x6,x7,x8,ncol=2,nrow=4)
plot_grid(x9,x10,x11,x12,x13,x14,x15,x16,ncol=2,nrow=4)
It is clearly evident from the above charts that EmployeeCount and StandardHours should be removed from the final predictor set for modeling.
Similarly the EmployeeNumber is also not required.
# Bar Plots to see distribution of each of the categoric variables
b1.1 <- df_attrn %>% group_by(Gender) %>% summarise(Count=n()) %>% arrange(-Count) %>% ggplot(aes(x=reorder(Gender,-Count),y=Count)) + geom_bar(stat="identity",fill="Orange") + labs(x="Gender") + geom_text(aes(label=Count),vjust=1.5)
b1.2 <- df_attrn %>% group_by(Gender,Attrition) %>% summarise(Count=n()) %>% arrange(-Count) %>% ggplot(aes(x=reorder(Gender,-Count),y=Count, fill=Attrition)) + geom_bar(stat="identity") + labs(x="Gender") + facet_wrap(~Attrition) + scale_fill_manual(values=c("#2EF688", "#F63A2E")) + geom_text(aes(label=Count),vjust=1.5)
b1 <- plot_grid(b1.1,b1.2,ncol=2)
b2.1 <- df_attrn %>% group_by(MaritalStatus) %>% summarise(Count=n()) %>% ggplot(aes(x=reorder(MaritalStatus,-Count),y=Count)) + geom_bar(stat="identity",fill="Orange") + labs(x="MaritalStatus") + geom_text(aes(label=Count),vjust=1.5)
b2.2 <- df_attrn %>% group_by(MaritalStatus,Attrition) %>% summarise(Count=n()) %>% arrange(-Count) %>% ggplot(aes(x=reorder(MaritalStatus,-Count),y=Count, fill=Attrition)) + geom_bar(stat="identity") + labs(x="MaritalStatus") + facet_wrap(~Attrition) + scale_fill_manual(values=c("#2EF688", "#F63A2E")) + geom_text(aes(label=Count),vjust=1.5)
b2 <- plot_grid(b2.1,b2.2,ncol=2)
b3.1 <- df_attrn %>% group_by(Over18) %>% summarise(Count=n()) %>% ggplot(aes(x=reorder(Over18,-Count),y=Count)) + geom_bar(stat="identity",fill="Orange") + labs(x="Over18") + geom_text(aes(label=Count),vjust=1.5)
b3.2 <- df_attrn %>% group_by(Over18,Attrition) %>% summarise(Count=n()) %>% arrange(-Count) %>% ggplot(aes(x=reorder(Over18,-Count),y=Count, fill=Attrition)) + geom_bar(stat="identity") + labs(x="Over18") + facet_wrap(~Attrition) + scale_fill_manual(values=c("#2EF688", "#F63A2E")) + geom_text(aes(label=Count),vjust=1.5)
b3 <- plot_grid(b3.1,b3.2,ncol=2)
b4.1 <- df_attrn %>% group_by(Department) %>% summarise(Count=n()) %>% ggplot(aes(x=reorder(Department,-Count),y=Count)) + geom_bar(stat="identity",fill="Orange") + labs(x="Department") + geom_text(aes(label=Count),vjust=1.5)
b4.2 <- df_attrn %>% group_by(Department,Attrition) %>% summarise(Count=n()) %>% arrange(-Count) %>% ggplot(aes(x=reorder(Department,-Count),y=Count, fill=Attrition)) + geom_bar(stat="identity") + labs(x="Department") + facet_wrap(~Attrition) + scale_fill_manual(values=c("#2EF688", "#F63A2E")) + geom_text(aes(label=Count),vjust=1.5)
b4 <- plot_grid(b4.1,b4.2,ncol=2)
b5.1 <- df_attrn %>% group_by(Education) %>% summarise(Count=n()) %>% ggplot(aes(x=reorder(Education,-Count),y=Count)) + geom_bar(stat="identity",fill="Orange") + labs(x="Education") + geom_text(aes(label=Count),vjust=1.5)
b5.2 <- df_attrn %>% group_by(Education,Attrition) %>% summarise(Count=n()) %>% arrange(-Count) %>% ggplot(aes(x=reorder(Education,-Count),y=Count, fill=Attrition)) + geom_bar(stat="identity") + labs(x="Education") + facet_wrap(~Attrition) + scale_fill_manual(values=c("#2EF688", "#F63A2E")) + geom_text(aes(label=Count),vjust=1.5)
b5 <- plot_grid(b5.1,b5.2,ncol=2)
b6.1 <- df_attrn %>% group_by(EducationField) %>% summarise(Count=n()) %>% ggplot(aes(x=reorder(EducationField,-Count),y=Count)) + geom_bar(stat="identity",fill="Orange") + labs(x="EducationField") + geom_text(aes(label=Count),vjust=1.5)
b6.2 <- df_attrn %>% group_by(EducationField,Attrition) %>% summarise(Count=n()) %>% arrange(-Count) %>% ggplot(aes(x=reorder(EducationField,-Count),y=Count, fill=Attrition)) + geom_bar(stat="identity") + labs(x="EducationField") + facet_wrap(~Attrition) + scale_fill_manual(values=c("#2EF688", "#F63A2E")) + geom_text(aes(label=Count),vjust=1.5)
b6 <- plot_grid(b6.1,b6.2,ncol=2)
b7.1 <- df_attrn %>% group_by(BusinessTravel) %>% summarise(Count=n()) %>% ggplot(aes(x=reorder(BusinessTravel,-Count),y=Count)) + geom_bar(stat="identity",fill="Orange") + labs(x="BusinessTravel") + geom_text(aes(label=Count),vjust=1.5)
b7.2 <- df_attrn %>% group_by(BusinessTravel,Attrition) %>% summarise(Count=n()) %>% arrange(-Count) %>% ggplot(aes(x=reorder(BusinessTravel,-Count),y=Count, fill=Attrition)) + geom_bar(stat="identity") + labs(x="BusinessTravel") + facet_wrap(~Attrition) + scale_fill_manual(values=c("#2EF688", "#F63A2E")) + geom_text(aes(label=Count),vjust=1.5)
b7 <- plot_grid(b7.1,b7.2,ncol=2)
b8.1 <- df_attrn %>% group_by(StockOptionLevel) %>% summarise(Count=n()) %>% ggplot(aes(x=reorder(StockOptionLevel,-Count),y=Count)) + geom_bar(stat="identity",fill="Orange") + labs(x="StockOptionLevel") + geom_text(aes(label=Count),vjust=1.5)
b8.2 <- df_attrn %>% group_by(StockOptionLevel,Attrition) %>% summarise(Count=n()) %>% arrange(-Count) %>% ggplot(aes(x=reorder(StockOptionLevel,-Count),y=Count, fill=Attrition)) + geom_bar(stat="identity") + labs(x="StockOptionLevel") + facet_wrap(~Attrition) + scale_fill_manual(values=c("#2EF688", "#F63A2E")) + geom_text(aes(label=Count),vjust=1.5)
b8 <- plot_grid(b8.1,b8.2,ncol=2)
b9.1 <- df_attrn %>% group_by(OverTime) %>% summarise(Count=n()) %>% ggplot(aes(x=reorder(OverTime,-Count),y=Count)) + geom_bar(stat="identity",fill="Orange") + labs(x="OverTime") + geom_text(aes(label=Count),vjust=1.5)
b9.2 <- df_attrn %>% group_by(OverTime,Attrition) %>% summarise(Count=n()) %>% arrange(-Count) %>% ggplot(aes(x=reorder(OverTime,-Count),y=Count, fill=Attrition)) + geom_bar(stat="identity") + labs(x="OverTime") + facet_wrap(~Attrition) + scale_fill_manual(values=c("#2EF688", "#F63A2E")) + geom_text(aes(label=Count),vjust=1.5)
b9 <- plot_grid(b9.1,b9.2,ncol=2)
b10.1 <- df_attrn %>% group_by(JobRole) %>% summarise(Count=n()) %>% ggplot(aes(x=reorder(JobRole,-Count),y=Count)) + geom_bar(stat="identity",fill="Orange") + labs(x="JobRole") + geom_text(aes(label=Count),vjust=1.5)
b10.2 <- df_attrn %>% group_by(JobRole,Attrition) %>% summarise(Count=n()) %>% arrange(-Count) %>% ggplot(aes(x=reorder(JobRole,-Count),y=Count, fill=Attrition)) + geom_bar(stat="identity") + labs(x="JobRole") + facet_wrap(~Attrition) + scale_fill_manual(values=c("#2EF688", "#F63A2E")) + geom_text(aes(label=Count),vjust=1.5)
b10 <- plot_grid(b10.1,b10.2,ncol=2)
b11.1 <- df_attrn %>% group_by(JobLevel) %>% summarise(Count=n()) %>% ggplot(aes(x=reorder(JobLevel,-Count),y=Count)) + geom_bar(stat="identity",fill="Orange") + labs(x="JobLevel") + geom_text(aes(label=Count),vjust=1.5)
b11.2 <- df_attrn %>% group_by(JobLevel,Attrition) %>% summarise(Count=n()) %>% arrange(-Count) %>% ggplot(aes(x=reorder(JobLevel,-Count),y=Count, fill=Attrition)) + geom_bar(stat="identity") + labs(x="JobLevel") + facet_wrap(~Attrition) + scale_fill_manual(values=c("#2EF688", "#F63A2E")) + geom_text(aes(label=Count),vjust=1.5)
b11 <- plot_grid(b11.1,b11.2,ncol=2)
b12.1 <- df_attrn %>% group_by(JobInvolvement) %>% summarise(Count=n()) %>% ggplot(aes(x=reorder(JobInvolvement,-Count),y=Count)) + geom_bar(stat="identity",fill="Orange") + labs(x="JobInvolvement") + geom_text(aes(label=Count),vjust=1.5)
b12.2 <- df_attrn %>% group_by(JobInvolvement,Attrition) %>% summarise(Count=n()) %>% arrange(-Count) %>% ggplot(aes(x=reorder(JobInvolvement,-Count),y=Count, fill=Attrition)) + geom_bar(stat="identity") + labs(x="JobInvolvement") + facet_wrap(~Attrition) + scale_fill_manual(values=c("#2EF688", "#F63A2E")) + geom_text(aes(label=Count),vjust=1.5)
b12 <- plot_grid(b12.1,b12.2,ncol=2)
b13.1 <- df_attrn %>% group_by(JobSatisfaction) %>% summarise(Count=n()) %>% ggplot(aes(x=reorder(JobSatisfaction,-Count),y=Count)) + geom_bar(stat="identity",fill="Orange") + labs(x="JobSatisfaction") + geom_text(aes(label=Count),vjust=1.5)
b13.2 <- df_attrn %>% group_by(JobSatisfaction,Attrition) %>% summarise(Count=n()) %>% arrange(-Count) %>% ggplot(aes(x=reorder(JobSatisfaction,-Count),y=Count, fill=Attrition)) + geom_bar(stat="identity") + labs(x="JobSatisfaction") + facet_wrap(~Attrition) + scale_fill_manual(values=c("#2EF688", "#F63A2E")) + geom_text(aes(label=Count),vjust=1.5)
b13 <- plot_grid(b13.1,b13.2,ncol=2)
b14.1 <- df_attrn %>% group_by(WorkLifeBalance) %>% summarise(Count=n()) %>% ggplot(aes(x=reorder(WorkLifeBalance,-Count),y=Count)) + geom_bar(stat="identity",fill="Orange") + labs(x="WorkLifeBalance") + geom_text(aes(label=Count),vjust=1.5)
b14.2 <- df_attrn %>% group_by(WorkLifeBalance,Attrition) %>% summarise(Count=n()) %>% arrange(-Count) %>% ggplot(aes(x=reorder(WorkLifeBalance,-Count),y=Count, fill=Attrition)) + geom_bar(stat="identity") + labs(x="WorkLifeBalance") + facet_wrap(~Attrition) + scale_fill_manual(values=c("#2EF688", "#F63A2E")) + geom_text(aes(label=Count),vjust=1.5)
b14 <- plot_grid(b14.1,b14.2,ncol=2)
b15.1 <- df_attrn %>% group_by(EnvironmentSatisfaction) %>% summarise(Count=n()) %>% ggplot(aes(x=reorder(EnvironmentSatisfaction,-Count),y=Count)) + geom_bar(stat="identity",fill="Orange") + labs(x="EnvironmentSatisfaction") + geom_text(aes(label=Count),vjust=1.5)
b15.2 <- df_attrn %>% group_by(EnvironmentSatisfaction,Attrition) %>% summarise(Count=n()) %>% arrange(-Count) %>% ggplot(aes(x=reorder(EnvironmentSatisfaction,-Count),y=Count, fill=Attrition)) + geom_bar(stat="identity") + labs(x="EnvironmentSatisfaction") + facet_wrap(~Attrition) + scale_fill_manual(values=c("#2EF688", "#F63A2E")) + geom_text(aes(label=Count),vjust=1.5)
b15 <- plot_grid(b15.1,b15.2,ncol=2)
b16.1 <- df_attrn %>% group_by(RelationshipSatisfaction) %>% summarise(Count=n()) %>% ggplot(aes(x=reorder(RelationshipSatisfaction,-Count),y=Count)) + geom_bar(stat="identity",fill="Orange") + labs(x="RelationshipSatisfaction") + geom_text(aes(label=Count),vjust=1.5)
b16.2 <- df_attrn %>% group_by(RelationshipSatisfaction,Attrition) %>% summarise(Count=n()) %>% arrange(-Count) %>% ggplot(aes(x=reorder(RelationshipSatisfaction,-Count),y=Count, fill=Attrition)) + geom_bar(stat="identity") + labs(x="RelationshipSatisfaction") + facet_wrap(~Attrition) + scale_fill_manual(values=c("#2EF688", "#F63A2E")) + geom_text(aes(label=Count),vjust=1.5)
b16 <- plot_grid(b16.1,b16.2,ncol=2)
b17.1 <- df_attrn %>% group_by(PerformanceRating) %>% summarise(Count=n()) %>% ggplot(aes(x=reorder(PerformanceRating,-Count),y=Count)) + geom_bar(stat="identity",fill="Orange") + labs(x="PerformanceRating") + geom_text(aes(label=Count),vjust=1.5)
b17.2 <- df_attrn %>% group_by(PerformanceRating,Attrition) %>% summarise(Count=n()) %>% arrange(-Count) %>% ggplot(aes(x=reorder(PerformanceRating,-Count),y=Count, fill=Attrition)) + geom_bar(stat="identity") + labs(x="PerformanceRating") + facet_wrap(~Attrition) + scale_fill_manual(values=c("#2EF688", "#F63A2E")) + geom_text(aes(label=Count),vjust=1.5)
b17 <- plot_grid(b17.1,b17.2,ncol=2)
b18.1 <- df_attrn %>% group_by(TrainingTimesLastYear) %>% summarise(Count=n()) %>% ggplot(aes(x=reorder(TrainingTimesLastYear,-Count),y=Count)) + geom_bar(stat="identity",fill="Orange") + labs(x="TrainingTimesLastYear") + geom_text(aes(label=Count),vjust=1.5)
b18.2 <- df_attrn %>% group_by(TrainingTimesLastYear,Attrition) %>% summarise(Count=n()) %>% arrange(-Count) %>% ggplot(aes(x=reorder(TrainingTimesLastYear,-Count),y=Count, fill=Attrition)) + geom_bar(stat="identity") + labs(x="TrainingTimesLastYear") + facet_wrap(~Attrition) + scale_fill_manual(values=c("#2EF688", "#F63A2E")) + geom_text(aes(label=Count),vjust=1.5)
b18 <- plot_grid(b18.1,b18.2,ncol=2)plot_grid(b1,b2,b3,b4,b5,b6,nrow=3,ncol=2)
plot_grid(b7,b8,b9,b10,b11,b12,nrow=3,ncol=2)
plot_grid(b13,b14,b15,b16,b17,b18,nrow=3,ncol=2)
Predictor Elimination or Transformation
# Looking at the summary, bar charts and box plots, Over18, EmployeeCount and StandardHours could be removed as they have only one value and could potentially cause bias or clear separation issue.
df_attrn$Over18 <- NULL
df_attrn$EmployeeCount <- NULL
df_attrn$StandardHours <- NULL
# Also EmployeeNumber is not required to do Modeling or checking collinearity. Remove it
df_attrn$EmployeeNumber <- NULLSampling and Cross Validation
# Set the seed
set.seed(12345)
# Create a trainIndex with 80-20 split for train and test set
trainIndex <- createDataPartition(df_attrn$Attrition, p=0.8, list=FALSE, times=1)
train <- df_attrn[trainIndex,]
test <- df_attrn[-trainIndex,]
# Checking that both the training and testing sets have the same label proportions.
prop_train <- train %>% select(Attrition) %>% group_by(Attrition) %>% summarize(n=n()) %>%
mutate(pct=round(prop.table(n), 2))## `summarise()` ungrouping output (override with `.groups` argument)prop_test <- test %>% select(Attrition) %>% group_by(Attrition) %>% summarize(n=n()) %>%
mutate(pct=round(prop.table(n), 2))## `summarise()` ungrouping output (override with `.groups` argument)prop_train## # A tibble: 2 x 3
## Attrition n pct
## <fct> <int> <dbl>
## 1 No 987 0.84
## 2 Yes 190 0.16prop_test## # A tibble: 2 x 3
## Attrition n pct
## <fct> <int> <dbl>
## 1 No 246 0.84
## 2 Yes 47 0.16# Set the train control for Cross Validation
train.control <- trainControl(method = "cv")Collinearity
# Run a logistic regression model
set.seed(12345)
train.control <- trainControl(method = "cv")
glm.fit <- train(Attrition~.,
data=train,
method= 'glm',
family= 'binomial',
trControl = train.control)
summary(glm.fit)##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7956 -0.4487 -0.1865 -0.0511 3.7159
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.122e+01 5.839e+02 -0.019 0.984672
## Age -4.100e-02 1.608e-02 -2.549 0.010797 *
## BusinessTravelTravel_Frequently 1.991e+00 4.724e-01 4.214 2.51e-05 ***
## BusinessTravelTravel_Rarely 8.293e-01 4.348e-01 1.907 0.056482 .
## DailyRate -6.035e-04 2.643e-04 -2.284 0.022391 *
## `DepartmentResearch & Development` 1.452e+01 5.839e+02 0.025 0.980157
## DepartmentSales 1.251e+01 5.839e+02 0.021 0.982903
## DistanceFromHome 5.015e-02 1.297e-02 3.865 0.000111 ***
## Education2 4.312e-01 4.083e-01 1.056 0.290957
## Education3 3.368e-01 3.553e-01 0.948 0.343258
## Education4 4.050e-01 3.853e-01 1.051 0.293185
## Education5 1.192e-01 6.920e-01 0.172 0.863207
## `EducationFieldLife Sciences` -5.532e-01 9.372e-01 -0.590 0.555014
## EducationFieldMarketing -5.038e-02 9.982e-01 -0.050 0.959752
## EducationFieldMedical -5.643e-01 9.388e-01 -0.601 0.547736
## EducationFieldOther -5.332e-01 1.027e+00 -0.519 0.603441
## `EducationFieldTechnical Degree` 5.989e-01 9.579e-01 0.625 0.531821
## EnvironmentSatisfaction2 -9.816e-01 3.328e-01 -2.949 0.003186 **
## EnvironmentSatisfaction3 -1.076e+00 3.040e-01 -3.541 0.000399 ***
## EnvironmentSatisfaction4 -1.270e+00 3.091e-01 -4.111 3.94e-05 ***
## GenderMale 5.213e-01 2.251e-01 2.316 0.020583 *
## HourlyRate 5.903e-03 5.406e-03 1.092 0.274909
## JobInvolvement2 -8.577e-01 4.450e-01 -1.927 0.053928 .
## JobInvolvement3 -1.222e+00 4.213e-01 -2.900 0.003735 **
## JobInvolvement4 -1.809e+00 5.544e-01 -3.263 0.001102 **
## JobLevel2 -1.573e+00 5.095e-01 -3.086 0.002026 **
## JobLevel3 -2.560e-01 8.019e-01 -0.319 0.749587
## JobLevel4 -1.733e+00 1.434e+00 -1.208 0.226988
## JobLevel5 4.483e-01 1.826e+00 0.246 0.806039
## `JobRoleHuman Resources` 1.581e+01 5.839e+02 0.027 0.978401
## `JobRoleLaboratory Technician` 1.262e+00 6.785e-01 1.860 0.062855 .
## JobRoleManager 1.021e-01 1.193e+00 0.086 0.931829
## `JobRoleManufacturing Director` 7.762e-01 6.456e-01 1.202 0.229229
## `JobRoleResearch Director` -1.845e+00 1.259e+00 -1.465 0.142954
## `JobRoleResearch Scientist` 1.581e-01 6.998e-01 0.226 0.821235
## `JobRoleSales Executive` 3.657e+00 1.627e+00 2.248 0.024573 *
## `JobRoleSales Representative` 3.974e+00 1.700e+00 2.337 0.019451 *
## JobSatisfaction2 -8.523e-01 3.327e-01 -2.562 0.010417 *
## JobSatisfaction3 -7.132e-01 2.895e-01 -2.463 0.013767 *
## JobSatisfaction4 -1.539e+00 3.181e-01 -4.840 1.30e-06 ***
## MaritalStatusMarried 2.491e-01 3.183e-01 0.783 0.433734
## MaritalStatusSingle 3.356e-01 4.697e-01 0.715 0.474906
## MonthlyIncome -2.757e-05 1.052e-04 -0.262 0.793229
## MonthlyRate 1.313e-05 1.466e-05 0.895 0.370540
## NumCompaniesWorked 2.225e-01 4.709e-02 4.725 2.30e-06 ***
## OverTimeYes 2.188e+00 2.425e-01 9.023 < 2e-16 ***
## PercentSalaryHike -1.385e-02 4.638e-02 -0.299 0.765172
## PerformanceRating4 -1.752e-02 4.806e-01 -0.036 0.970919
## RelationshipSatisfaction2 -9.364e-01 3.435e-01 -2.726 0.006413 **
## RelationshipSatisfaction3 -1.081e+00 3.049e-01 -3.546 0.000392 ***
## RelationshipSatisfaction4 -1.004e+00 2.994e-01 -3.353 0.000799 ***
## StockOptionLevel1 -1.291e+00 3.800e-01 -3.396 0.000683 ***
## StockOptionLevel2 -1.015e+00 5.050e-01 -2.009 0.044486 *
## StockOptionLevel3 -8.411e-02 5.352e-01 -0.157 0.875127
## TotalWorkingYears -3.356e-02 3.503e-02 -0.958 0.338079
## TrainingTimesLastYear1 -1.430e+00 6.630e-01 -2.157 0.031037 *
## TrainingTimesLastYear2 -1.570e+00 5.019e-01 -3.129 0.001754 **
## TrainingTimesLastYear3 -1.809e+00 5.155e-01 -3.509 0.000450 ***
## TrainingTimesLastYear4 -1.307e+00 5.742e-01 -2.276 0.022867 *
## TrainingTimesLastYear5 -1.869e+00 6.036e-01 -3.097 0.001956 **
## TrainingTimesLastYear6 -2.016e+00 7.321e-01 -2.754 0.005893 **
## WorkLifeBalance2 -9.380e-01 4.721e-01 -1.987 0.046966 *
## WorkLifeBalance3 -1.432e+00 4.488e-01 -3.191 0.001417 **
## WorkLifeBalance4 -1.186e+00 5.297e-01 -2.238 0.025211 *
## YearsAtCompany 1.275e-01 4.816e-02 2.648 0.008105 **
## YearsInCurrentRole -1.561e-01 5.708e-02 -2.734 0.006252 **
## YearsSinceLastPromotion 1.794e-01 5.178e-02 3.464 0.000532 ***
## YearsWithCurrManager -1.722e-01 5.581e-02 -3.085 0.002032 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1040.54 on 1176 degrees of freedom
## Residual deviance: 622.89 on 1109 degrees of freedom
## AIC: 758.89
##
## Number of Fisher Scoring iterations: 15# Lets check the collinearity
car::vif(glm.fit$finalModel)## Age BusinessTravelTravel_Frequently
## 2.095188e+00 4.269574e+00
## BusinessTravelTravel_Rarely DailyRate
## 4.113060e+00 1.126862e+00
## `DepartmentResearch & Development` DepartmentSales
## 7.923032e+06 7.506990e+06
## DistanceFromHome Education2
## 1.187195e+00 2.352277e+00
## Education3 Education4
## 2.891553e+00 2.822589e+00
## Education5 `EducationFieldLife Sciences`
## 1.421845e+00 1.968302e+01
## EducationFieldMarketing EducationFieldMedical
## 1.115905e+01 1.792578e+01
## EducationFieldOther `EducationFieldTechnical Degree`
## 4.461469e+00 9.066842e+00
## EnvironmentSatisfaction2 EnvironmentSatisfaction3
## 1.660558e+00 1.830561e+00
## EnvironmentSatisfaction4 GenderMale
## 1.805022e+00 1.131452e+00
## HourlyRate JobInvolvement2
## 1.131057e+00 3.783890e+00
## JobInvolvement3 JobInvolvement4
## 4.171393e+00 2.099790e+00
## JobLevel2 JobLevel3
## 4.945620e+00 7.993151e+00
## JobLevel4 JobLevel5
## 3.651971e+00 8.790860e+00
## `JobRoleHuman Resources` `JobRoleLaboratory Technician`
## 1.711730e+06 7.963321e+00
## JobRoleManager `JobRoleManufacturing Director`
## 3.793770e+00 2.387207e+00
## `JobRoleResearch Director` `JobRoleResearch Scientist`
## 2.282053e+00 7.574043e+00
## `JobRoleSales Executive` `JobRoleSales Representative`
## 4.714601e+01 2.573392e+01
## JobSatisfaction2 JobSatisfaction3
## 1.596312e+00 1.800767e+00
## JobSatisfaction4 MaritalStatusMarried
## 1.730714e+00 2.327886e+00
## MaritalStatusSingle MonthlyIncome
## 5.148447e+00 1.468342e+01
## MonthlyRate NumCompaniesWorked
## 1.093827e+00 1.502205e+00
## OverTimeYes PercentSalaryHike
## 1.392850e+00 2.690390e+00
## PerformanceRating4 RelationshipSatisfaction2
## 2.680372e+00 1.637960e+00
## RelationshipSatisfaction3 RelationshipSatisfaction4
## 1.798088e+00 1.811451e+00
## StockOptionLevel1 StockOptionLevel2
## 2.918063e+00 1.808554e+00
## StockOptionLevel3 TotalWorkingYears
## 1.740500e+00 5.418750e+00
## TrainingTimesLastYear1 TrainingTimesLastYear2
## 2.045440e+00 5.683255e+00
## TrainingTimesLastYear3 TrainingTimesLastYear4
## 5.355411e+00 2.958248e+00
## TrainingTimesLastYear5 TrainingTimesLastYear6
## 2.397398e+00 1.829408e+00
## WorkLifeBalance2 WorkLifeBalance3
## 4.030278e+00 4.728657e+00
## WorkLifeBalance4 YearsAtCompany
## 2.673441e+00 7.699738e+00
## YearsInCurrentRole YearsSinceLastPromotion
## 3.190738e+00 2.824242e+00
## YearsWithCurrManager
## 3.135020e+00# Lets check the Accuracy of this Logistic Regression Model
pred.glm.fit<-predict(glm.fit,test)
confusionMatrix(pred.glm.fit,test$Attrition)## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 240 24
## Yes 6 23
##
## Accuracy : 0.8976
## 95% CI : (0.8571, 0.9298)
## No Information Rate : 0.8396
## P-Value [Acc > NIR] : 0.002927
##
## Kappa : 0.5502
##
## Mcnemar's Test P-Value : 0.001911
##
## Sensitivity : 0.9756
## Specificity : 0.4894
## Pos Pred Value : 0.9091
## Neg Pred Value : 0.7931
## Prevalence : 0.8396
## Detection Rate : 0.8191
## Detection Prevalence : 0.9010
## Balanced Accuracy : 0.7325
##
## 'Positive' Class : No
## set.seed(12345)
glm.fit2 <- train(Attrition~Age+DailyRate+DistanceFromHome+HourlyRate+MonthlyIncome+MonthlyRate+NumCompaniesWorked+PercentSalaryHike+TotalWorkingYears+YearsAtCompany+YearsInCurrentRole+YearsSinceLastPromotion+YearsWithCurrManager,data=train,family='binomial',method='glm',trControl=train.control)
car::vif(glm.fit2$finalModel)## Age DailyRate DistanceFromHome
## 1.839552 1.013556 1.013984
## HourlyRate MonthlyIncome MonthlyRate
## 1.010555 2.112725 1.008356
## NumCompaniesWorked PercentSalaryHike TotalWorkingYears
## 1.278963 1.008551 4.142205
## YearsAtCompany YearsInCurrentRole YearsSinceLastPromotion
## 5.111289 2.503467 2.353307
## YearsWithCurrManager
## 2.484731# Run the decision tree after applying Cross Validation
set.seed(12345)
dtree.model <- train(Attrition~.,
data = train,
trControl = train.control
,method = 'rpart')
dtree.model## CART
##
## 1177 samples
## 30 predictor
## 2 classes: 'No', 'Yes'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 1059, 1059, 1059, 1060, 1059, 1059, ...
## Resampling results across tuning parameters:
##
## cp Accuracy Kappa
## 0.01578947 0.8428654 0.2137445
## 0.03684211 0.8428509 0.1665119
## 0.04035088 0.8428509 0.1665119
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was cp = 0.01578947.pred.dTree.model <- predict(dtree.model, test)
confusionMatrix(pred.dTree.model,test$Attrition)## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 238 41
## Yes 8 6
##
## Accuracy : 0.8328
## 95% CI : (0.785, 0.8736)
## No Information Rate : 0.8396
## P-Value [Acc > NIR] : 0.6602
##
## Kappa : 0.1329
##
## Mcnemar's Test P-Value : 4.844e-06
##
## Sensitivity : 0.9675
## Specificity : 0.1277
## Pos Pred Value : 0.8530
## Neg Pred Value : 0.4286
## Prevalence : 0.8396
## Detection Rate : 0.8123
## Detection Prevalence : 0.9522
## Balanced Accuracy : 0.5476
##
## 'Positive' Class : No
## # KNN fit with all variables
set.seed(12345)
knn.fit <-
train(Attrition~.,
data=train,
method='knn',
trControl = train.control,
tuneLength=20)
pred.knn.fit <- predict(knn.fit, test)
confusionMatrix(pred.knn.fit,test$Attrition)## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 245 47
## Yes 1 0
##
## Accuracy : 0.8362
## 95% CI : (0.7887, 0.8767)
## No Information Rate : 0.8396
## P-Value [Acc > NIR] : 0.6009
##
## Kappa : -0.0067
##
## Mcnemar's Test P-Value : 8.293e-11
##
## Sensitivity : 0.9959
## Specificity : 0.0000
## Pos Pred Value : 0.8390
## Neg Pred Value : 0.0000
## Prevalence : 0.8396
## Detection Rate : 0.8362
## Detection Prevalence : 0.9966
## Balanced Accuracy : 0.4980
##
## 'Positive' Class : No
## knn.fit## k-Nearest Neighbors
##
## 1177 samples
## 30 predictor
## 2 classes: 'No', 'Yes'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 1059, 1059, 1059, 1060, 1059, 1059, ...
## Resampling results across tuning parameters:
##
## k Accuracy Kappa
## 5 0.8189555 0.083726005
## 7 0.8317471 0.091977534
## 9 0.8376793 0.082423113
## 11 0.8402361 0.075742306
## 13 0.8385412 0.046995298
## 15 0.8351369 0.027996463
## 17 0.8385485 0.039890078
## 19 0.8368608 0.016549542
## 21 0.8385774 0.006877815
## 23 0.8394249 0.008514335
## 25 0.8394249 0.008514335
## 27 0.8394249 0.008514335
## 29 0.8385702 0.000000000
## 31 0.8385702 0.000000000
## 33 0.8385702 0.000000000
## 35 0.8385702 0.000000000
## 37 0.8385702 0.000000000
## 39 0.8385702 0.000000000
## 41 0.8385702 0.000000000
## 43 0.8385702 0.000000000
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was k = 11.# Run random forests
set.seed(12345)
rf.fit = train(Attrition~.,
data=train,
method='rf',
trControl=train.control,
importance=TRUE)
rf.fit$besttune## NULLvarImp(rf.fit)## rf variable importance
##
## only 20 most important variables shown (out of 67)
##
## Importance
## OverTimeYes 100.00
## MonthlyIncome 60.28
## Age 50.51
## TotalWorkingYears 43.37
## BusinessTravelTravel_Frequently 34.79
## NumCompaniesWorked 32.39
## YearsWithCurrManager 31.51
## YearsInCurrentRole 31.01
## StockOptionLevel1 28.42
## MaritalStatusSingle 28.40
## YearsAtCompany 26.79
## DailyRate 26.78
## JobRoleSales Representative 25.90
## JobRoleLaboratory Technician 24.40
## YearsSinceLastPromotion 24.36
## JobSatisfaction4 23.90
## JobRoleResearch Scientist 23.15
## JobRoleSales Executive 22.06
## JobLevel2 20.78
## WorkLifeBalance2 20.37plot(varImp(rf.fit))
pred.rf<-predict(rf.fit,test)
confusionMatrix(pred.rf,test$Attrition)## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 242 33
## Yes 4 14
##
## Accuracy : 0.8737
## 95% CI : (0.8302, 0.9095)
## No Information Rate : 0.8396
## P-Value [Acc > NIR] : 0.06194
##
## Kappa : 0.3753
##
## Mcnemar's Test P-Value : 4.161e-06
##
## Sensitivity : 0.9837
## Specificity : 0.2979
## Pos Pred Value : 0.8800
## Neg Pred Value : 0.7778
## Prevalence : 0.8396
## Detection Rate : 0.8259
## Detection Prevalence : 0.9386
## Balanced Accuracy : 0.6408
##
## 'Positive' Class : No
## rf.fit## Random Forest
##
## 1177 samples
## 30 predictor
## 2 classes: 'No', 'Yes'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 1059, 1059, 1059, 1060, 1059, 1059, ...
## Resampling results across tuning parameters:
##
## mtry Accuracy Kappa
## 2 0.8419673 0.03352773
## 34 0.8522092 0.23567181
## 67 0.8488194 0.23740005
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 34.varImp(glm.fit)## glm variable importance
##
## only 20 most important variables shown (out of 67)
##
## Overall
## OverTimeYes 100.00
## JobSatisfaction4 53.53
## NumCompaniesWorked 52.25
## BusinessTravelTravel_Frequently 46.58
## EnvironmentSatisfaction4 45.43
## DistanceFromHome 42.70
## RelationshipSatisfaction3 39.15
## EnvironmentSatisfaction3 39.10
## TrainingTimesLastYear3 38.74
## YearsSinceLastPromotion 38.24
## StockOptionLevel1 37.49
## RelationshipSatisfaction4 37.01
## JobInvolvement4 36.01
## WorkLifeBalance3 35.21
## TrainingTimesLastYear2 34.52
## TrainingTimesLastYear5 34.17
## JobLevel2 34.05
## YearsWithCurrManager 34.04
## EnvironmentSatisfaction2 32.53
## JobInvolvement3 31.98varImp(knn.fit)## ROC curve variable importance
##
## only 20 most important variables shown (out of 30)
##
## Importance
## OverTime 100.00
## MonthlyIncome 98.11
## TotalWorkingYears 93.93
## JobLevel 93.54
## YearsAtCompany 90.99
## YearsInCurrentRole 89.62
## YearsWithCurrManager 87.17
## Age 86.59
## StockOptionLevel 70.58
## MaritalStatus 68.81
## JobSatisfaction 55.02
## JobInvolvement 50.42
## EnvironmentSatisfaction 45.10
## JobRole 43.83
## DailyRate 37.56
## DistanceFromHome 30.93
## Department 29.11
## RelationshipSatisfaction 24.50
## TrainingTimesLastYear 21.97
## BusinessTravel 19.28varImp(dtree.model)## rpart variable importance
##
## only 20 most important variables shown (out of 67)
##
## Overall
## MonthlyIncome 100.000
## TotalWorkingYears 68.824
## OverTimeYes 51.399
## StockOptionLevel1 44.139
## MaritalStatusSingle 43.489
## YearsWithCurrManager 38.137
## Age 37.285
## YearsAtCompany 30.636
## WorkLifeBalance2 16.983
## YearsInCurrentRole 11.944
## JobInvolvement4 8.042
## JobSatisfaction4 7.232
## TrainingTimesLastYear1 0.000
## JobInvolvement2 0.000
## `JobRoleSales Representative` 0.000
## WorkLifeBalance3 0.000
## YearsSinceLastPromotion 0.000
## `JobRoleResearch Scientist` 0.000
## Education4 0.000
## `JobRoleResearch Director` 0.000# Attrition by OverTime and within each OverTime category
options(repr.plot.width=8, repr.plot.height=4)
# Create a dataframe by filtering the data for OT and Attrition grouped by them. Then take percentage of it
options(repr.plot.width=15, repr.plot.height=5)
attr.OT <- df_attrn %>% select(OverTime, Attrition) %>% group_by(OverTime, Attrition) %>% summarize(amount=n()) %>% mutate(pct=round(prop.table(amount)*100,0)) %>% arrange(pct)## `summarise()` regrouping output by 'OverTime' (override with `.groups` argument)# Overall Attrition by OverTime
yes.Attr.OT <- attr.OT %>% filter(Attrition == "Yes")
yes.Attr.OT$pct <- NULL
yes.Attr.OT %>% mutate (perc = round((amount/sum(yes.Attr.OT$amount))*100,0)) -> yes.Attr.OT
attritionByOT <- yes.Attr.OT %>%
ggplot(aes(x=OverTime, y=perc, fill=OverTime, color=OverTime)) +
geom_bar(stat="identity") + coord_flip() +
geom_label(aes(label=paste0(perc, "%"), fill = OverTime), colour = "white", fontface = "italic") +
labs(x="OverTime", y="Attrition (%)", title="Attrition by OverTime", subtitle="Overall")+ theme_wsj() +
theme(legend.position="none", plot.title=element_text(hjust=0.5,vjust=0.5,size=14),plot.subtitle=element_text(hjust=0.5,vjust=0.5, size=11, face="italic"))
# Attrition within each OverTime
attritionWithinOTCtg <- attr.OT %>%
ggplot(aes(x=fct_reorder(OverTime,pct), y=pct, fill=Attrition, color=Attrition)) +
geom_bar(stat="identity") + facet_wrap(~Attrition) + coord_flip() +
geom_label(aes(label=paste0(pct, "%"), fill = Attrition), colour = "white", fontface = "italic") + scale_fill_manual(values=c("#2EF688", "#F63A2E")) + scale_color_manual(values=c("#09C873","#DD1509")) +
labs(x="", y="Attrition (%)", title="Attrition by Overtime ", subtitle="Percentage (%) with in each Category")+ theme_wsj() +
theme(legend.position="none", plot.title=element_text(hjust=1.0, vjust=0.5,size=14), plot.subtitle=element_text(hjust=1.0, vjust=0.5,size=11, face="italic"))
plot_grid(attritionByOT, attritionWithinOTCtg, align="h", axis = "b", ncol=2, rel_widths = c(1.5, 1.5))