Final Report on Employee Attrition Analysis
Siddhant Mishra
January 27, 2018
Project Title: Final Report on Employee Attrition Analysis
NAME: Siddhant Mishra
EMAIL: mishra.siddhant18@gmail.com
COLLEGE: IIT (BHU), Varanasi
Introduction:
Employee Attrition refres to when a company employee choose to leave when company to work for other company. Employee attrition is a very common process across organizations. Vraious employee leave one company for other company just becuase of some benifits. The benefits depend on employee to employee like some may be better compansation, distance from home, better growth Opportunity, better skillset training, betterrole, environmant factors, bad relationship with managers etc.
Overview Of The Study:
The analysis has been carried to find out the most important factor that affect employee’s attrition. The opposite of attrition is retention. As employee’s attrition very negatively affect companies growth, every year companies come with different retention schemes for employee so employees dont leave the company. Those method include retention bonuses, better training, promotion and stock option which mature after som definite period. So finding the important factor for attrition will help organization to plan their retention schemes more targeted.
An Empirical Study on Dataset Created by IBM Data Scientist
Overview
In this study we will study Data created by IBM data scientist involving various factors of employee. Though the data is fictious but since the data is used by data scientist of a reputed company it can be considered for a explorartory study.
Data:
The dataset consist information of 1470 employees with their 35 Characteristics including their Attrition/Retention status
The factors that are included in this Dataset are:
- Age
- Attrition Yes/No
- Business Travel - NonTravel/Travel-Rarely/Travel-Frequently
- DailyRate - DailyRate paid to Employee
- Deaprtment- There are 3 department as follow Human Resources, Research & Development & Sales
- DistanceFromHome
- Education: The level of Education coded as follow: 1 ‘Below College’ 2 ‘College’ 3. ‘Bachelor’ 4 ‘Master’ 5 ‘Doctor’
- Education Field: Eduaction Field of employee among Human Resources, Life Sciences Marketing, Medical,Other & Technical Degree
- EmployeeCount- Number of Employees in record 10.EmployeeNumber- Unique Code of Employees
- EnvironmentSatisfaction: The Environment satisfaction rate given by employee as 1 ‘Low’ 2 ‘Medium’ 3 ‘High’ 4 ‘Very High’
- Gender: Male/Female
- HourlyRate: The Hourly Rate paid to Employee
- JobInvolvement: The degree to which employee his involved in job, given as 1 ‘Low’ 2’Medium’ 3 ‘High’ 4 ‘Very High’
- JobLevel: The Job heirarchy of Employee, rated as 1 ‘Low’ 2 ‘Medium’ 3 ‘High’ 4 ‘Very High’
- JobRole: The Job Role of Employee among Healthcare Representative, Human Resources Laboratory Technician, Manager, Manufacturing Director, Research Director, Research Scientist, Sales Executive & Sales Representative
- JobSatisfaction: The job satisfcation rate of employee given as 1 ‘Low’ 2 ‘Medium’ 3 ‘High’ 4 ‘Very High’
- MaritalStatus: Employee is Married/Divorced/Single
- MonthyIncome: The monthly income of employee in Rs
- MonthlyRate: The monthly rate of employee paid by company in Rs
- NumCompaniesWorked: The total number of comapnies employee has worked.
- Over18: Whether Employee is above 18 age or not.
- OverTime: Whether employee do OverTime or not.
- PercentSalaryHike: The percent incraese in salary given to employee.
- Perfrormance Rating: The performance rating given to employee given as 1 ‘Low’ 2 ‘Good’ 3 ‘Excellent’ 4 ‘Outstanding’
- RelationshipSatisfaction: The relastionship satisfaction rating by employee given as 1 ‘Low’ 2 ‘Medium’ 3 ‘High’ 4 ‘Very High’
- StandardHours: StandardHours for an employe in week.
- StockOptionLevel:
- TotalWorkingYears: The total working years of employee.
- TrainingTimeLastYear: The total times for which employee was given training last year.
- WorkLifeBalance: The work life balance rating given by employee as 1 ‘Bad’ 2 ‘Good’ 3 ‘Better’ 4 ‘Best’
- YearsAtComany: Total yaers employee have been in comapny.
- YearsInCurrentRole: The total year employee has been in current role.
- YearsSinceLastPromotion: The total years after last promotion.
- YearWithCurrManager: The total year employee is with current manager.
Since some variable have values in character form so they were converted into numeric form as follow
- Attrition-> Attrition1, Yes=1, No=0
- BusinessTravel -> BusinessTravel1, “Non-Travel” = 1,“Travel_Rarely” = 2,
Travel_Frequently" =3 - Department -> Department1, “Human Resources” = 1,“Research & Development”] = 2,
“Travel_Frequently”] = 3 EducationField -> EducationField1,“Human Resources” = 1, “Life Sciences”=2, “Marketing”= 3, “Medical” = 4, “Other” = 5, “Technical Degree” = 6
Gender -> Gender1,“Female” = 1, “Male” = 2
JobRole -> JobRole1,“Human Resources”= 1, “Healthcare Representative”= 2, “Laboratory Technician” = 3, “Manager”= 4, “Manufacturing Director” = 5, “Research Director” = 6, “Research Scientist” = 7, “Sales Executive” =8, “Sales Representative”= 9
MaritalStatus-> MaritalStatus1“Divorced”= 1,“Married”=2“Single”= 3
OverTime -> OverTime1, “Yes”=1, “No”= 0
Setting Working Directory
setwd("E:/Internship/Tasks/Project")
Employeedata <-read.csv(paste("HR-Employee-Attrition.csv",sep=""))
View(Employeedata)Converting Character Variable into Numeric Vraiables
Employeedata$Attrition1[Employeedata$Attrition== "Yes"] <-1
Employeedata$Attrition1[Employeedata$Attrition== "No"] <-0
Employeedata$BusinessTravel1[Employeedata$BusinessTravel== "Non-Travel"] <-1
Employeedata$BusinessTravel1[Employeedata$BusinessTravel== "Travel_Rarely"] <-2
Employeedata$BusinessTravel1[Employeedata$BusinessTravel== "Travel_Frequently"] <-3
Employeedata$Department1[Employeedata$Department== "Human Resources"] <-1
Employeedata$Department1[Employeedata$Department== "Research & Development"] <-2
Employeedata$Department1[Employeedata$Department== "Sales"] <-3
Employeedata$EducationField1[Employeedata$EducationField== "Human Resources"] <-1
Employeedata$EducationField1[Employeedata$EducationField== "Life Sciences"] <-2
Employeedata$EducationField1[Employeedata$EducationField== "Marketing"] <-3
Employeedata$EducationField1[Employeedata$EducationField== "Medical"] <-4
Employeedata$EducationField1[Employeedata$EducationField== "Other"] <-5
Employeedata$EducationField1[Employeedata$EducationField== "Technical Degree"] <-6
Employeedata$Gender1[Employeedata$Gender== "Female"] <-1
Employeedata$Gender1[Employeedata$Gender== "Male"] <-2
Employeedata$JobRole1[Employeedata$JobRole== "Human Resources"] <-1
Employeedata$JobRole1[Employeedata$JobRole== "Healthcare Representative"] <-2
Employeedata$JobRole1[Employeedata$JobRole== "Laboratory Technician"] <-3
Employeedata$JobRole1[Employeedata$JobRole== "Manager"] <-4
Employeedata$JobRole1[Employeedata$JobRole== "Manufacturing Director"] <-5
Employeedata$JobRole1[Employeedata$JobRole== "Research Director"] <-6
Employeedata$JobRole1[Employeedata$JobRole== "Research Scientist"] <-7
Employeedata$JobRole1[Employeedata$JobRole== "Sales Executive"] <-8
Employeedata$JobRole1[Employeedata$JobRole== "Sales Representative"] <-9
Employeedata$MaritalStatus1[Employeedata$MaritalStatus== "Divorced"] <-1
Employeedata$MaritalStatus1[Employeedata$MaritalStatus== "Married"] <-2
Employeedata$MaritalStatus1[Employeedata$MaritalStatus== "Single"] <-3
Employeedata$OverTime1[Employeedata$OverTime== "Yes"] <-1
Employeedata$OverTime1[Employeedata$OverTime== "No"] <-0
dim(Employeedata)## [1] 1470 43Creating a Data Frame “Num” containing only numeric relevant variable
Num <- subset(Employeedata, select = c(1,4,6,7,11,13,14,15,17,19,20,21,24,25,26,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43))
View(Num)Regression Analysis
Modelling Attrtion as a function of ï..Age, BusinessTravel1, DailyRate, Department1, DistanceFromHome, Education, EducationField1, EnvironmentSatisfaction, Gender1, HourlyRate, JobInvolvement, JobLevel, JobRole1, JobSatisfaction, MaritalStatus1, MonthlyIncome, MonthlyRate, NumCompaniesWorked, OverTime1, PercentSalaryHike, PerformanceRating, RelationshipSatisfaction, StockOptionLevel, TotalWorkingYears, TrainingTimesLastYear, WorkLifeBalance, YearsAtCompany, YearsInCurrentRole, YearsSinceLastPromotion and YearsWithCurrManager
Model <-lm(Attrition1 ~ ï..Age + BusinessTravel1 + DailyRate + Department1 + DistanceFromHome + Education + EducationField1 + EnvironmentSatisfaction + Gender1 + HourlyRate + JobInvolvement + JobLevel + JobRole1 + JobSatisfaction + MaritalStatus1 + MonthlyIncome + MonthlyRate + NumCompaniesWorked + OverTime1 + PercentSalaryHike + PerformanceRating + RelationshipSatisfaction + StockOptionLevel + TotalWorkingYears + TrainingTimesLastYear + WorkLifeBalance + YearsAtCompany + YearsInCurrentRole + YearsSinceLastPromotion + YearsWithCurrManager, data=Num)
summary(Model)##
## Call:
## lm(formula = Attrition1 ~ ï..Age + BusinessTravel1 + DailyRate +
## Department1 + DistanceFromHome + Education + EducationField1 +
## EnvironmentSatisfaction + Gender1 + HourlyRate + JobInvolvement +
## JobLevel + JobRole1 + JobSatisfaction + MaritalStatus1 +
## MonthlyIncome + MonthlyRate + NumCompaniesWorked + OverTime1 +
## PercentSalaryHike + PerformanceRating + RelationshipSatisfaction +
## StockOptionLevel + TotalWorkingYears + TrainingTimesLastYear +
## WorkLifeBalance + YearsAtCompany + YearsInCurrentRole + YearsSinceLastPromotion +
## YearsWithCurrManager, data = Num)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.58773 -0.21194 -0.08635 0.07390 1.13505
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.886e-01 1.425e-01 2.728 0.006455 **
## ï..Age -3.783e-03 1.336e-03 -2.832 0.004697 **
## BusinessTravel1 8.250e-02 1.610e-02 5.125 3.37e-07 ***
## DailyRate -3.107e-05 2.135e-05 -1.455 0.145770
## Department1 6.934e-02 2.365e-02 2.932 0.003423 **
## DistanceFromHome 3.525e-03 1.059e-03 3.330 0.000892 ***
## Education 2.293e-04 8.573e-03 0.027 0.978663
## EducationField1 6.534e-03 6.447e-03 1.014 0.310992
## EnvironmentSatisfaction -4.044e-02 7.856e-03 -5.148 3.00e-07 ***
## Gender1 3.898e-02 1.755e-02 2.221 0.026496 *
## HourlyRate -2.528e-04 4.230e-04 -0.598 0.550236
## JobInvolvement -6.148e-02 1.209e-02 -5.087 4.12e-07 ***
## JobLevel -3.477e-02 2.638e-02 -1.318 0.187610
## JobRole1 -6.889e-03 5.197e-03 -1.326 0.185181
## JobSatisfaction -3.861e-02 7.795e-03 -4.953 8.16e-07 ***
## MaritalStatus1 5.329e-02 1.582e-02 3.369 0.000774 ***
## MonthlyIncome 1.108e-06 5.999e-06 0.185 0.853543
## MonthlyRate 5.144e-07 1.205e-06 0.427 0.669557
## NumCompaniesWorked 1.694e-02 3.830e-03 4.423 1.04e-05 ***
## OverTime1 2.073e-01 1.918e-02 10.811 < 2e-16 ***
## PercentSalaryHike -3.448e-03 3.697e-03 -0.933 0.351193
## PerformanceRating 2.318e-02 3.747e-02 0.619 0.536343
## RelationshipSatisfaction -2.193e-02 7.949e-03 -2.759 0.005865 **
## StockOptionLevel -2.389e-02 1.348e-02 -1.773 0.076463 .
## TotalWorkingYears -3.476e-03 2.404e-03 -1.446 0.148328
## TrainingTimesLastYear -1.264e-02 6.681e-03 -1.892 0.058715 .
## WorkLifeBalance -2.774e-02 1.216e-02 -2.282 0.022647 *
## YearsAtCompany 5.993e-03 2.981e-03 2.011 0.044550 *
## YearsInCurrentRole -1.027e-02 3.881e-03 -2.647 0.008213 **
## YearsSinceLastPromotion 1.111e-02 3.421e-03 3.247 0.001193 **
## YearsWithCurrManager -1.046e-02 3.979e-03 -2.628 0.008681 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3263 on 1439 degrees of freedom
## Multiple R-squared: 0.2294, Adjusted R-squared: 0.2133
## F-statistic: 14.28 on 30 and 1439 DF, p-value: < 2.2e-16Results:
We found that some factors affect the attrition of employee very much while some factors totall not. The main factors which cause attrition of employee are age of employee, how much business tarvel he make, in which business department he is involved,office distance from home, enviromant satisfaction,gender,JobInvolvement, his Job Satisfaction rate ,his MaritalStatus,Number of Companies he worked, wherther he does overtime or not, his RelationshipSatisfaction, WorklifeBalance, YearsAtCompany, YearsInCurrentRole,Years Since he lost promoted and Years with current manager
Conclusion:
The factor which affect attrition rates are given above.
The factors which increases attrition rate in employees are:
(because of their positive coefficient in Model)
- More Business Travel
- Office Distance From Home
- Worked in more number of companies
- Need of ovetime to complete work
- More number in present company
- If he is not promoted from long time.
The factors which decreases attrition rate in employees are:
(because of their negative coefficient in Model)
- Higher Age of Employee
- Good Environment Satisfaction
- Good Job Involvement
- Good Job Satisfaction
- Good Relationship Satisfaction
- Good Work Life Balance
- More number of years in Current Role
- More number of years with Current Manager.
The factors which adversly affect Attrition Rate means to very high extent are:
- More Business Travel
- Office Distance From Home
- Worked in more number of companies
- Need of ovetime to complete work
- Good Environment Satisfaction
- Good Job Involvement
- Good Job Satisfaction
Summary Statistics
summary(Employeedata)## ï..Age Attrition BusinessTravel DailyRate
## Min. :18.00 No :1233 Non-Travel : 150 Min. : 102.0
## 1st Qu.:30.00 Yes: 237 Travel_Frequently: 277 1st Qu.: 465.0
## Median :36.00 Travel_Rarely :1043 Median : 802.0
## Mean :36.92 Mean : 802.5
## 3rd Qu.:43.00 3rd Qu.:1157.0
## Max. :60.00 Max. :1499.0
##
## Department DistanceFromHome Education
## Human Resources : 63 Min. : 1.000 Min. :1.000
## Research & Development:961 1st Qu.: 2.000 1st Qu.:2.000
## Sales :446 Median : 7.000 Median :3.000
## Mean : 9.193 Mean :2.913
## 3rd Qu.:14.000 3rd Qu.:4.000
## Max. :29.000 Max. :5.000
##
## EducationField EmployeeCount EmployeeNumber
## Human Resources : 27 Min. :1 Min. : 1.0
## Life Sciences :606 1st Qu.:1 1st Qu.: 491.2
## Marketing :159 Median :1 Median :1020.5
## Medical :464 Mean :1 Mean :1024.9
## Other : 82 3rd Qu.:1 3rd Qu.:1555.8
## Technical Degree:132 Max. :1 Max. :2068.0
##
## EnvironmentSatisfaction Gender HourlyRate JobInvolvement
## Min. :1.000 Female:588 Min. : 30.00 Min. :1.00
## 1st Qu.:2.000 Male :882 1st Qu.: 48.00 1st Qu.:2.00
## Median :3.000 Median : 66.00 Median :3.00
## Mean :2.722 Mean : 65.89 Mean :2.73
## 3rd Qu.:4.000 3rd Qu.: 83.75 3rd Qu.:3.00
## Max. :4.000 Max. :100.00 Max. :4.00
##
## JobLevel JobRole JobSatisfaction
## Min. :1.000 Sales Executive :326 Min. :1.000
## 1st Qu.:1.000 Research Scientist :292 1st Qu.:2.000
## Median :2.000 Laboratory Technician :259 Median :3.000
## Mean :2.064 Manufacturing Director :145 Mean :2.729
## 3rd Qu.:3.000 Healthcare Representative:131 3rd Qu.:4.000
## Max. :5.000 Manager :102 Max. :4.000
## (Other) :215
## MaritalStatus MonthlyIncome MonthlyRate NumCompaniesWorked
## Divorced:327 Min. : 1009 Min. : 2094 Min. :0.000
## Married :673 1st Qu.: 2911 1st Qu.: 8047 1st Qu.:1.000
## Single :470 Median : 4919 Median :14236 Median :2.000
## Mean : 6503 Mean :14313 Mean :2.693
## 3rd Qu.: 8379 3rd Qu.:20462 3rd Qu.:4.000
## Max. :19999 Max. :26999 Max. :9.000
##
## Over18 OverTime PercentSalaryHike PerformanceRating
## Y:1470 No :1054 Min. :11.00 Min. :3.000
## Yes: 416 1st Qu.:12.00 1st Qu.:3.000
## Median :14.00 Median :3.000
## Mean :15.21 Mean :3.154
## 3rd Qu.:18.00 3rd Qu.:3.000
## Max. :25.00 Max. :4.000
##
## RelationshipSatisfaction StandardHours StockOptionLevel TotalWorkingYears
## Min. :1.000 Min. :80 Min. :0.0000 Min. : 0.00
## 1st Qu.:2.000 1st Qu.:80 1st Qu.:0.0000 1st Qu.: 6.00
## Median :3.000 Median :80 Median :1.0000 Median :10.00
## Mean :2.712 Mean :80 Mean :0.7939 Mean :11.28
## 3rd Qu.:4.000 3rd Qu.:80 3rd Qu.:1.0000 3rd Qu.:15.00
## Max. :4.000 Max. :80 Max. :3.0000 Max. :40.00
##
## TrainingTimesLastYear WorkLifeBalance YearsAtCompany YearsInCurrentRole
## Min. :0.000 Min. :1.000 Min. : 0.000 Min. : 0.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.: 3.000 1st Qu.: 2.000
## Median :3.000 Median :3.000 Median : 5.000 Median : 3.000
## Mean :2.799 Mean :2.761 Mean : 7.008 Mean : 4.229
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.: 9.000 3rd Qu.: 7.000
## Max. :6.000 Max. :4.000 Max. :40.000 Max. :18.000
##
## YearsSinceLastPromotion YearsWithCurrManager Attrition1
## Min. : 0.000 Min. : 0.000 Min. :0.0000
## 1st Qu.: 0.000 1st Qu.: 2.000 1st Qu.:0.0000
## Median : 1.000 Median : 3.000 Median :0.0000
## Mean : 2.188 Mean : 4.123 Mean :0.1612
## 3rd Qu.: 3.000 3rd Qu.: 7.000 3rd Qu.:0.0000
## Max. :15.000 Max. :17.000 Max. :1.0000
##
## BusinessTravel1 Department1 EducationField1 Gender1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.0
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.0
## Median :2.000 Median :2.000 Median :3.000 Median :2.0
## Mean :2.086 Mean :2.261 Mean :3.248 Mean :1.6
## 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:2.0
## Max. :3.000 Max. :3.000 Max. :6.000 Max. :2.0
##
## JobRole1 MaritalStatus1 OverTime1
## Min. :1.000 Min. :1.000 Min. :0.000
## 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:0.000
## Median :6.000 Median :2.000 Median :0.000
## Mean :5.512 Mean :2.097 Mean :0.283
## 3rd Qu.:8.000 3rd Qu.:3.000 3rd Qu.:1.000
## Max. :9.000 Max. :3.000 Max. :1.000
## Summary Statstics using Describe()
library("psych", lib.loc="~/R/win-library/3.4")
describe(Employeedata)## vars n mean sd median trimmed
## ï..Age 1 1470 36.92 9.14 36.0 36.47
## Attrition* 2 1470 1.16 0.37 1.0 1.08
## BusinessTravel* 3 1470 2.61 0.67 3.0 2.76
## DailyRate 4 1470 802.49 403.51 802.0 803.83
## Department* 5 1470 2.26 0.53 2.0 2.25
## DistanceFromHome 6 1470 9.19 8.11 7.0 8.08
## Education 7 1470 2.91 1.02 3.0 2.98
## EducationField* 8 1470 3.25 1.33 3.0 3.10
## EmployeeCount 9 1470 1.00 0.00 1.0 1.00
## EmployeeNumber 10 1470 1024.87 602.02 1020.5 1023.40
## EnvironmentSatisfaction 11 1470 2.72 1.09 3.0 2.78
## Gender* 12 1470 1.60 0.49 2.0 1.62
## HourlyRate 13 1470 65.89 20.33 66.0 66.02
## JobInvolvement 14 1470 2.73 0.71 3.0 2.74
## JobLevel 15 1470 2.06 1.11 2.0 1.90
## JobRole* 16 1470 5.46 2.46 6.0 5.61
## JobSatisfaction 17 1470 2.73 1.10 3.0 2.79
## MaritalStatus* 18 1470 2.10 0.73 2.0 2.12
## MonthlyIncome 19 1470 6502.93 4707.96 4919.0 5667.24
## MonthlyRate 20 1470 14313.10 7117.79 14235.5 14286.48
## NumCompaniesWorked 21 1470 2.69 2.50 2.0 2.36
## Over18* 22 1470 1.00 0.00 1.0 1.00
## OverTime* 23 1470 1.28 0.45 1.0 1.23
## PercentSalaryHike 24 1470 15.21 3.66 14.0 14.80
## PerformanceRating 25 1470 3.15 0.36 3.0 3.07
## RelationshipSatisfaction 26 1470 2.71 1.08 3.0 2.77
## StandardHours 27 1470 80.00 0.00 80.0 80.00
## StockOptionLevel 28 1470 0.79 0.85 1.0 0.67
## TotalWorkingYears 29 1470 11.28 7.78 10.0 10.37
## TrainingTimesLastYear 30 1470 2.80 1.29 3.0 2.72
## WorkLifeBalance 31 1470 2.76 0.71 3.0 2.77
## YearsAtCompany 32 1470 7.01 6.13 5.0 5.99
## YearsInCurrentRole 33 1470 4.23 3.62 3.0 3.85
## YearsSinceLastPromotion 34 1470 2.19 3.22 1.0 1.48
## YearsWithCurrManager 35 1470 4.12 3.57 3.0 3.77
## Attrition1 36 1470 0.16 0.37 0.0 0.08
## BusinessTravel1 37 1470 2.09 0.53 2.0 2.11
## Department1 38 1470 2.26 0.53 2.0 2.25
## EducationField1 39 1470 3.25 1.33 3.0 3.10
## Gender1 40 1470 1.60 0.49 2.0 1.62
## JobRole1 41 1470 5.51 2.37 6.0 5.61
## MaritalStatus1 42 1470 2.10 0.73 2.0 2.12
## OverTime1 43 1470 0.28 0.45 0.0 0.23
## mad min max range skew kurtosis se
## ï..Age 8.90 18 60 42 0.41 -0.41 0.24
## Attrition* 0.00 1 2 1 1.84 1.39 0.01
## BusinessTravel* 0.00 1 3 2 -1.44 0.69 0.02
## DailyRate 510.01 102 1499 1397 0.00 -1.21 10.52
## Department* 0.00 1 3 2 0.17 -0.40 0.01
## DistanceFromHome 7.41 1 29 28 0.96 -0.23 0.21
## Education 1.48 1 5 4 -0.29 -0.56 0.03
## EducationField* 1.48 1 6 5 0.55 -0.69 0.03
## EmployeeCount 0.00 1 1 0 NaN NaN 0.00
## EmployeeNumber 790.97 1 2068 2067 0.02 -1.23 15.70
## EnvironmentSatisfaction 1.48 1 4 3 -0.32 -1.20 0.03
## Gender* 0.00 1 2 1 -0.41 -1.83 0.01
## HourlyRate 26.69 30 100 70 -0.03 -1.20 0.53
## JobInvolvement 0.00 1 4 3 -0.50 0.26 0.02
## JobLevel 1.48 1 5 4 1.02 0.39 0.03
## JobRole* 2.97 1 9 8 -0.36 -1.20 0.06
## JobSatisfaction 1.48 1 4 3 -0.33 -1.22 0.03
## MaritalStatus* 1.48 1 3 2 -0.15 -1.12 0.02
## MonthlyIncome 3260.24 1009 19999 18990 1.37 0.99 122.79
## MonthlyRate 9201.76 2094 26999 24905 0.02 -1.22 185.65
## NumCompaniesWorked 1.48 0 9 9 1.02 0.00 0.07
## Over18* 0.00 1 1 0 NaN NaN 0.00
## OverTime* 0.00 1 2 1 0.96 -1.07 0.01
## PercentSalaryHike 2.97 11 25 14 0.82 -0.31 0.10
## PerformanceRating 0.00 3 4 1 1.92 1.68 0.01
## RelationshipSatisfaction 1.48 1 4 3 -0.30 -1.19 0.03
## StandardHours 0.00 80 80 0 NaN NaN 0.00
## StockOptionLevel 1.48 0 3 3 0.97 0.35 0.02
## TotalWorkingYears 5.93 0 40 40 1.11 0.91 0.20
## TrainingTimesLastYear 1.48 0 6 6 0.55 0.48 0.03
## WorkLifeBalance 0.00 1 4 3 -0.55 0.41 0.02
## YearsAtCompany 4.45 0 40 40 1.76 3.91 0.16
## YearsInCurrentRole 4.45 0 18 18 0.92 0.47 0.09
## YearsSinceLastPromotion 1.48 0 15 15 1.98 3.59 0.08
## YearsWithCurrManager 4.45 0 17 17 0.83 0.16 0.09
## Attrition1 0.00 0 1 1 1.84 1.39 0.01
## BusinessTravel1 0.00 1 3 2 0.08 0.41 0.01
## Department1 0.00 1 3 2 0.17 -0.40 0.01
## EducationField1 1.48 1 6 5 0.55 -0.69 0.03
## Gender1 0.00 1 2 1 -0.41 -1.83 0.01
## JobRole1 2.97 1 9 8 -0.28 -1.32 0.06
## MaritalStatus1 1.48 1 3 2 -0.15 -1.12 0.02
## OverTime1 0.00 0 1 1 0.96 -1.07 0.01Reading more details about Data
One-Way Contingency tables
Attrition Count
mytable <-xtabs(~Attrition, data =Employeedata)
mytable## Attrition
## No Yes
## 1233 237Type of Travelling Involved
mytable <-xtabs(~BusinessTravel, data =Employeedata)
mytable## BusinessTravel
## Non-Travel Travel_Frequently Travel_Rarely
## 150 277 1043Number of Employees in Different Department
mytable <-xtabs(~Department, data =Employeedata)
mytable## Department
## Human Resources Research & Development Sales
## 63 961 446Type of Education of Employee
mytable <-xtabs(~Education, data =Employeedata)
mytable## Education
## 1 2 3 4 5
## 170 282 572 398 48Different EducationField of Employees
mytable <-xtabs(~EducationField, data =Employeedata)
mytable## EducationField
## Human Resources Life Sciences Marketing Medical
## 27 606 159 464
## Other Technical Degree
## 82 132Types of EnvironmentSatisfaction of Employees
mytable <-xtabs(~EnvironmentSatisfaction, data =Employeedata)
mytable## EnvironmentSatisfaction
## 1 2 3 4
## 284 287 453 446Number of Males and Females in Company
mytable <-xtabs(~Gender, data =Employeedata)
mytable## Gender
## Female Male
## 588 882Kinds of JobInvolvement of Employee in Company
mytable <-xtabs(~JobInvolvement, data =Employeedata)
mytable## JobInvolvement
## 1 2 3 4
## 83 375 868 144Types of Job Level
mytable <-xtabs(~JobLevel,data=Employeedata)
mytable## JobLevel
## 1 2 3 4 5
## 543 534 218 106 69Types of Job Role
mytable <-xtabs(~JobRole,data=Employeedata)
mytable## JobRole
## Healthcare Representative Human Resources
## 131 52
## Laboratory Technician Manager
## 259 102
## Manufacturing Director Research Director
## 145 80
## Research Scientist Sales Executive
## 292 326
## Sales Representative
## 83Types of JobSatisfaction
mytable <-xtabs(~JobSatisfaction,data=Employeedata)
mytable## JobSatisfaction
## 1 2 3 4
## 289 280 442 459Employees Maried or Not
mytable <-xtabs(~MaritalStatus,data=Employeedata)
mytable## MaritalStatus
## Divorced Married Single
## 327 673 470Over 18 or not
mytable <-xtabs(~Over18,data=Employeedata)
mytable## Over18
## Y
## 1470How many Employees do Overtime
mytable <-xtabs(~OverTime,data=Employeedata)
mytable## OverTime
## No Yes
## 1054 416Distribution of Perforamce Rating
mytable <-xtabs(~PerformanceRating,data=Employeedata)
mytable## PerformanceRating
## 3 4
## 1244 226Distribution of RealtionshipSatisfaction
mytable <-xtabs(~RelationshipSatisfaction, data=Employeedata)
mytable## RelationshipSatisfaction
## 1 2 3 4
## 276 303 459 432Standard Hours of Employees
mytable <-xtabs(~StandardHours, data=Employeedata)
mytable## StandardHours
## 80
## 1470Type of StockOptionsGiven
mytable <-xtabs(~StockOptionLevel, data=Employeedata)
mytable## StockOptionLevel
## 0 1 2 3
## 631 596 158 85Work Life Balance
mytable <-xtabs(~WorkLifeBalance, data = Employeedata)
mytable## WorkLifeBalance
## 1 2 3 4
## 80 344 893 153Two Way Contingency Tables
Effect of Travel on Attrition
mytable <-xtabs(~BusinessTravel + Attrition, data =Employeedata)
prop.table(mytable,1)## Attrition
## BusinessTravel No Yes
## Non-Travel 0.9200000 0.0800000
## Travel_Frequently 0.7509025 0.2490975
## Travel_Rarely 0.8504314 0.1495686Finding: Seems like Employee Facing heavy travel has more attrition rate
Departmentwise Attrition Rate
mytable <-xtabs(~Department + Attrition, data =Employeedata)
prop.table(mytable,1)## Attrition
## Department No Yes
## Human Resources 0.8095238 0.1904762
## Research & Development 0.8616025 0.1383975
## Sales 0.7937220 0.2062780Finding: Attrition rate does not very much across different departments
Educationwise Attrition Rate
mytable <-xtabs(~Education + Attrition, data =Employeedata)
prop.table(mytable,1)## Attrition
## Education No Yes
## 1 0.8176471 0.1823529
## 2 0.8439716 0.1560284
## 3 0.8269231 0.1730769
## 4 0.8542714 0.1457286
## 5 0.8958333 0.1041667Finding: Attrition rate does not very much across different Education
EducationField wise Attrition Rate
mytable <-xtabs(~EducationField + Attrition, data =Employeedata)
prop.table(mytable,1)## Attrition
## EducationField No Yes
## Human Resources 0.7407407 0.2592593
## Life Sciences 0.8531353 0.1468647
## Marketing 0.7798742 0.2201258
## Medical 0.8642241 0.1357759
## Other 0.8658537 0.1341463
## Technical Degree 0.7575758 0.2424242Finding: Attrition rate donot vary much between different EducationField
Effect of EnvironmentSatisfaction on Attrition
mytable <-xtabs(~EnvironmentSatisfaction + Attrition, data =Employeedata)
prop.table(mytable,1)## Attrition
## EnvironmentSatisfaction No Yes
## 1 0.7464789 0.2535211
## 2 0.8501742 0.1498258
## 3 0.8631347 0.1368653
## 4 0.8654709 0.1345291Finding: Lower Environment Satisfaction rate increases Attrition Rate
Effect of Gender on Attrition Rate
mytable <-xtabs(~Gender + Attrition, data =Employeedata)
prop.table(mytable,1)## Attrition
## Gender No Yes
## Female 0.8520408 0.1479592
## Male 0.8299320 0.1700680Finding: So Gender doesnot effect Attrition Rate much
Effect of JobInvolvement on Attrition Rate
mytable <-xtabs(~JobInvolvement + Attrition, data =Employeedata)
prop.table(mytable,1)## Attrition
## JobInvolvement No Yes
## 1 0.66265060 0.33734940
## 2 0.81066667 0.18933333
## 3 0.85599078 0.14400922
## 4 0.90972222 0.09027778Finding: Level of JobInvolvement also plays a good role in attrition rate. High Job Involvemnet reduces attrition rate
Effect of JobLevel on Attrition Rate
mytable <-xtabs(~JobLevel + Attrition, data =Employeedata)
prop.table(mytable,1)## Attrition
## JobLevel No Yes
## 1 0.73664825 0.26335175
## 2 0.90262172 0.09737828
## 3 0.85321101 0.14678899
## 4 0.95283019 0.04716981
## 5 0.92753623 0.07246377Finding: Level of JobLevel also plays a role in attrition rate as percentage of attrition is maximum for lowest JobLevl Employees but it is not that strict for further levels
Effect of JobRole on Attrition Rate
mytable <-xtabs(~JobRole + Attrition, data =Employeedata)
prop.table(mytable,1)## Attrition
## JobRole No Yes
## Healthcare Representative 0.93129771 0.06870229
## Human Resources 0.76923077 0.23076923
## Laboratory Technician 0.76061776 0.23938224
## Manager 0.95098039 0.04901961
## Manufacturing Director 0.93103448 0.06896552
## Research Director 0.97500000 0.02500000
## Research Scientist 0.83904110 0.16095890
## Sales Executive 0.82515337 0.17484663
## Sales Representative 0.60240964 0.39759036Finding: Attrition Rate depend on JobRole, some JobRole have 39% Attrition Rate while some JobRole have 4% Attrition Rate
Effect of JobSatisfaction on Attrition Rate
mytable <-xtabs(~JobSatisfaction + Attrition, data =Employeedata)
prop.table(mytable,1)## Attrition
## JobSatisfaction No Yes
## 1 0.7716263 0.2283737
## 2 0.8357143 0.1642857
## 3 0.8348416 0.1651584
## 4 0.8867102 0.1132898Finding: Higher Job Satisfaction Rate decreases attrition rate
Effect of MaritalStatus on Attrition Rate
mytable <-xtabs(~MaritalStatus + Attrition, data =Employeedata)
prop.table(mytable,1)## Attrition
## MaritalStatus No Yes
## Divorced 0.8990826 0.1009174
## Married 0.8751857 0.1248143
## Single 0.7446809 0.2553191Finding: Divorced People have lowest Attrition Rate
Effect of OverTime on Attrition Rate
mytable <-xtabs(~OverTime + Attrition, data =Employeedata)
prop.table(mytable,1)## Attrition
## OverTime No Yes
## No 0.8956357 0.1043643
## Yes 0.6947115 0.3052885Finding: People doing more OverTime have higher Attrition Rate
Effect of PerformanceRating on Attrition Rate
mytable <-xtabs(~PerformanceRating + Attrition, data =Employeedata)
prop.table(mytable,1)## Attrition
## PerformanceRating No Yes
## 3 0.8392283 0.1607717
## 4 0.8362832 0.1637168Finding: PerformanceRating does not effect Attrition Rate much
Effect of RelationshipSatisfaction on Attrition Rate
mytable <-xtabs(~RelationshipSatisfaction + Attrition, data =Employeedata)
prop.table(mytable,1)## Attrition
## RelationshipSatisfaction No Yes
## 1 0.7934783 0.2065217
## 2 0.8514851 0.1485149
## 3 0.8453159 0.1546841
## 4 0.8518519 0.1481481Finding: Bad RelationshipSatisfaction increases Attrition Rate
Effect of StockOptionLevel on Attrition Rate
mytable <-xtabs(~StockOptionLevel + Attrition, data =Employeedata)
prop.table(mytable,1)## Attrition
## StockOptionLevel No Yes
## 0 0.75594295 0.24405705
## 1 0.90604027 0.09395973
## 2 0.92405063 0.07594937
## 3 0.82352941 0.17647059Finding: StockoptionLeveldont have any definite effect on Attrition rate
Effect of WorkLifeBalance on Attrition Rate
mytable <-xtabs(~WorkLifeBalance + Attrition, data =Employeedata)
prop.table(mytable,1)## Attrition
## WorkLifeBalance No Yes
## 1 0.6875000 0.3125000
## 2 0.8313953 0.1686047
## 3 0.8577828 0.1422172
## 4 0.8235294 0.1764706Finding: Poor Worklifebalance increases Attrition Rate
Boxplots
Boxplot to see distribution of Age
boxplot(Employeedata$ï..Age,main="Boxplot Showing Variation of Age")
mean(Employeedata$ï..Age)## [1] 36.92381Boxplot to see Variation of DailyRate
boxplot(Employeedata$DailyRate,main="Boxplot Showing Variation of DailyRate")
mean(Employeedata$DailyRate)## [1] 802.4857Boxplot to see Variation of DistanceFromHome
boxplot(Employeedata$DistanceFromHome,ylim=c(-5,20),main="Boxplot Showing Variation of DistanceFromHome")
mean(Employeedata$DistanceFromHome)## [1] 9.192517Boxplot to see Variation of HourlyRate
boxplot(Employeedata$HourlyRate,main="Boxplot Showing Variation of HourlyRate")
mean(Employeedata$HourlyRate)## [1] 65.89116Boxplot to see Variation of MonthlyIncome
boxplot(Employeedata$MonthlyIncome,ylim=c(0,17000),main="Boxplot Showing Variation of MontlyIncome")
mean(Employeedata$MonthlyIncome)## [1] 6502.931Boxplot to see Variation of MonthlyRate
boxplot(Employeedata$MonthlyRate,ylim=c(),main="Boxplot Showing Variation of MonthlyRate")
mean(Employeedata$MonthlyRate)## [1] 14313.1Boxplot to see Variation of Number Of Companies Worked
boxplot(Employeedata$NumCompaniesWorked,ylim=c(),main="Boxplot Showing Variation of Number of Companies Worked")
mean(Employeedata$NumCompaniesWorked)## [1] 2.693197Boxplot to see Distribution of PercentSalaryHike
boxplot(Employeedata$PercentSalaryHike,ylim=c(),main="Boxplot Showing Variation of Number of Companies Worked")
mean(Employeedata$PercentSalaryHike)## [1] 15.20952Boxplot to see Distribution of Total Working Years
boxplot(Employeedata$TotalWorkingYears,ylim=c(),main="Boxplot Showing Variation of Total Working Years of Employee")
mean(Employeedata$TotalWorkingYears)## [1] 11.27959Boxplot to see Distribution of Training Time Last Year
boxplot(Employeedata$TrainingTimesLastYear,ylim=c(),main="Boxplot Showing Variation of Training Time Last Year of Employee")
mean(Employeedata$TrainingTimesLastYear)## [1] 2.79932Boxplot to see Variation Of Years At Company
boxplot(Employeedata$YearsAtCompany,ylim=c(0,20),main="Boxplot Showing Variation of Years At Company for Employee")
mean(Employeedata$YearsAtCompany)## [1] 7.008163Boxplot to see Variation Of Years in Current Role
boxplot(Employeedata$YearsInCurrentRole,ylim=c(),main="Boxplot Showing Variation of Years In Current Role for Employee")
mean(Employeedata$YearsInCurrentRole)## [1] 4.229252Boxplot to see Variation Of Years Since Last Promotion
boxplot(Employeedata$YearsSinceLastPromotion,ylim=c(),main="Boxplot Showing Variation of Years Since Last Promotion")
mean(Employeedata$YearsSinceLastPromotion)## [1] 2.187755Boxplot to see Variation Of Years With Current Manager
boxplot(Employeedata$YearsWithCurrManager,ylim=c(),main="Boxplot Showing Variation of Years With Current Manager")
mean(Employeedata$YearsWithCurrManager)## [1] 4.123129Histograms
Histogram showing Attrition Rate
plot(Employeedata$Attrition,xlim=c(),ylim=c(0,1500),ylab="Number Of Employee",main="Attrition Rate Counts")
Histogram showing BusinessTravel Variance
plot(Employeedata$BusinessTravel,xlim=c(),ylim=c(),ylab="Count Of Employee",main="Variation Of Travel")
Variance of Department
plot(Employeedata$Department,xlim=c(),ylim=c(),ylab="Count Of Employee",main="Variation Across Department")
Histogram showing Education Background
hist(Employeedata$Education,col='grey')
Variation of Different Education Field
plot(Employeedata$EducationField, ylab="Number OF Employees",main="Employees in Departments")
Histogram showing Environment Satisfaction
hist(Employeedata$EnvironmentSatisfaction,col='grey')
Histogram Showing Hourly Rate
hist(Employeedata$HourlyRate,col='grey')
JobInvolvement Variance
hist(Employeedata$JobInvolvement,col='grey')
JobLevel Variance
hist(Employeedata$JobLevel,col='grey')
JobSatisfaction Variance
hist(Employeedata$JobSatisfaction,col='grey',main="Employee Satisfaction Variance")
Monthly Income Variance
hist(Employeedata$MonthlyIncome,breaks=20,col='grey',xlim=c(0,20000))
Monthly Rate Variance
hist(Employeedata$MonthlyRate,col='grey',xlim=c(0,30000))
Number of Companies Worked
hist(Employeedata$NumCompaniesWorked,col='grey')
Percentage Hike Given Variation
hist(Employeedata$PercentSalaryHike,col='grey')
Performance Rating Distribution
hist(Employeedata$PerformanceRating,xlim=c(3,4),col='grey')
Distribution Of Relationship Satisfaction
hist(Employeedata$RelationshipSatisfaction,col='grey')
Distribution Of StockOptionLevel
hist(Employeedata$StockOptionLevel,col='grey')
Distribution Of TotalWorking years
hist(Employeedata$TotalWorkingYears,col='grey', main="Employee Working Years",breaks=25)
TrainingTime Last Year
hist(Employeedata$TrainingTimesLastYear,col='grey', main="Employee's Training Time Last Year")
Variance Of WorkLifeBalance among Employees
hist(Employeedata$WorkLifeBalance,col='grey', main="Employee's Work Life Balance Ratings")
Variation of YearsAtCompany
hist(Employeedata$YearsAtCompany,col='grey', main="Employee's Years At Company")
Variation of YearsInCurrentRole
hist(Employeedata$YearsInCurrentRole,col='grey',xlim=c(0,20),main="Employee's Years In Current Role")
Variation of YearsSinceLastPromotion
hist(Employeedata$YearsSinceLastPromotion,xlim=c(0,18),ylim=c(0,1000),col='grey',main="Employee's Years Since Last Promotion")
Variation of Employee’s Years With Current Manager
hist(Employeedata$YearsWithCurrManager,col='grey',main="Employee's Years With current Managers")
Creating Covariance Correlation Matrix
cor(Num,use="complete")## ï..Age DailyRate DistanceFromHome
## ï..Age 1.000000000 0.0106609426 -0.001686120
## DailyRate 0.010660943 1.0000000000 -0.004985337
## DistanceFromHome -0.001686120 -0.0049853374 1.000000000
## Education 0.208033731 -0.0168064332 0.021041826
## EnvironmentSatisfaction 0.010146428 0.0183548543 -0.016075327
## HourlyRate 0.024286543 0.0233814215 0.031130586
## JobInvolvement 0.029819959 0.0461348740 0.008783280
## JobLevel 0.509604228 0.0029663349 0.005302731
## JobSatisfaction -0.004891877 0.0305710078 -0.003668839
## MonthlyIncome 0.497854567 0.0077070589 -0.017014445
## MonthlyRate 0.028051167 -0.0321816015 0.027472864
## NumCompaniesWorked 0.299634758 0.0381534343 -0.029250804
## PercentSalaryHike 0.003633585 0.0227036775 0.040235377
## PerformanceRating 0.001903896 0.0004732963 0.027109618
## RelationshipSatisfaction 0.053534720 0.0078460310 0.006557475
## StockOptionLevel 0.037509712 0.0421427964 0.044871999
## TotalWorkingYears 0.680380536 0.0145147387 0.004628426
## TrainingTimesLastYear -0.019620819 0.0024525427 -0.036942234
## WorkLifeBalance -0.021490028 -0.0378480510 -0.026556004
## YearsAtCompany 0.311308770 -0.0340547676 0.009507720
## YearsInCurrentRole 0.212901056 0.0099320150 0.018844999
## YearsSinceLastPromotion 0.216513368 -0.0332289848 0.010028836
## YearsWithCurrManager 0.202088602 -0.0263631782 0.014406048
## Attrition1 -0.159205007 -0.0566519919 0.077923583
## BusinessTravel1 -0.011807332 -0.0155388905 -0.009696041
## Department1 -0.031882283 0.0071087137 0.017224804
## EducationField1 -0.040872848 0.0377092287 0.002013453
## Gender1 -0.036310550 -0.0117161379 -0.001850528
## JobRole1 -0.112807055 -0.0033580191 0.003574933
## MaritalStatus1 -0.095028910 -0.0695856414 -0.014437031
## OverTime1 0.028062357 0.0091349699 0.025513635
## Education EnvironmentSatisfaction
## ï..Age 0.208033731 0.0101464279
## DailyRate -0.016806433 0.0183548543
## DistanceFromHome 0.021041826 -0.0160753270
## Education 1.000000000 -0.0271283133
## EnvironmentSatisfaction -0.027128313 1.0000000000
## HourlyRate 0.016774829 -0.0498569562
## JobInvolvement 0.042437634 -0.0082775982
## JobLevel 0.101588886 0.0012116994
## JobSatisfaction -0.011296117 -0.0067843526
## MonthlyIncome 0.094960677 -0.0062590878
## MonthlyRate -0.026084197 0.0375996229
## NumCompaniesWorked 0.126316560 0.0125943232
## PercentSalaryHike -0.011110941 -0.0317011952
## PerformanceRating -0.024538791 -0.0295479523
## RelationshipSatisfaction -0.009118377 0.0076653835
## StockOptionLevel 0.018422220 0.0034321578
## TotalWorkingYears 0.148279697 -0.0026930704
## TrainingTimesLastYear -0.025100241 -0.0193593083
## WorkLifeBalance 0.009819189 0.0276272955
## YearsAtCompany 0.069113696 0.0014575492
## YearsInCurrentRole 0.060235554 0.0180074601
## YearsSinceLastPromotion 0.054254334 0.0161936056
## YearsWithCurrManager 0.069065378 -0.0049987226
## Attrition1 -0.031372820 -0.1033689783
## BusinessTravel1 -0.008669840 -0.0113099347
## Department1 0.007996422 -0.0193952706
## EducationField1 -0.039592150 0.0431634907
## Gender1 -0.016546827 0.0005083139
## JobRole1 0.007721031 -0.0145621843
## MaritalStatus1 0.004052654 -0.0035934733
## OverTime1 -0.020321767 0.0701317268
## HourlyRate JobInvolvement JobLevel
## ï..Age 0.0242865426 0.029819959 0.5096042284
## DailyRate 0.0233814215 0.046134874 0.0029663349
## DistanceFromHome 0.0311305856 0.008783280 0.0053027306
## Education 0.0167748289 0.042437634 0.1015888862
## EnvironmentSatisfaction -0.0498569562 -0.008277598 0.0012116994
## HourlyRate 1.0000000000 0.042860641 -0.0278534864
## JobInvolvement 0.0428606410 1.000000000 -0.0126298827
## JobLevel -0.0278534864 -0.012629883 1.0000000000
## JobSatisfaction -0.0713346244 -0.021475910 -0.0019437080
## MonthlyIncome -0.0157943044 -0.015271491 0.9502999135
## MonthlyRate -0.0152967496 -0.016322079 0.0395629510
## NumCompaniesWorked 0.0221568834 0.015012413 0.1425011238
## PercentSalaryHike -0.0090619863 -0.017204572 -0.0347304923
## PerformanceRating -0.0021716974 -0.029071333 -0.0212220821
## RelationshipSatisfaction 0.0013304528 0.034296821 0.0216415105
## StockOptionLevel 0.0502633991 0.021522640 0.0139839105
## TotalWorkingYears -0.0023336818 -0.005533182 0.7822078045
## TrainingTimesLastYear -0.0085476852 -0.015337826 -0.0181905502
## WorkLifeBalance -0.0046072338 -0.014616593 0.0378177456
## YearsAtCompany -0.0195816162 -0.021355427 0.5347386874
## YearsInCurrentRole -0.0241062202 0.008716963 0.3894467329
## YearsSinceLastPromotion -0.0267155861 -0.024184292 0.3538853470
## YearsWithCurrManager -0.0201232002 0.025975808 0.3752806078
## Attrition1 -0.0068455496 -0.130015957 -0.1691047509
## BusinessTravel1 -0.0041639831 0.029299959 -0.0116958303
## Department1 -0.0041437079 -0.024586062 0.1019631058
## EducationField1 -0.0219412191 -0.002655278 -0.0449326718
## Gender1 -0.0004782971 0.017959755 -0.0394031027
## JobRole1 -0.0165515330 0.007401840 -0.0668959855
## MaritalStatus1 -0.0178605059 -0.038497019 -0.0767694781
## OverTime1 -0.0077819744 -0.003506711 0.0005440478
## JobSatisfaction MonthlyIncome MonthlyRate
## ï..Age -0.0048918771 0.497854567 0.0280511671
## DailyRate 0.0305710078 0.007707059 -0.0321816015
## DistanceFromHome -0.0036688392 -0.017014445 0.0274728635
## Education -0.0112961167 0.094960677 -0.0260841972
## EnvironmentSatisfaction -0.0067843526 -0.006259088 0.0375996229
## HourlyRate -0.0713346244 -0.015794304 -0.0152967496
## JobInvolvement -0.0214759103 -0.015271491 -0.0163220791
## JobLevel -0.0019437080 0.950299913 0.0395629510
## JobSatisfaction 1.0000000000 -0.007156742 0.0006439169
## MonthlyIncome -0.0071567424 1.000000000 0.0348136261
## MonthlyRate 0.0006439169 0.034813626 1.0000000000
## NumCompaniesWorked -0.0556994260 0.149515216 0.0175213534
## PercentSalaryHike 0.0200020394 -0.027268586 -0.0064293459
## PerformanceRating 0.0022971971 -0.017120138 -0.0098114285
## RelationshipSatisfaction -0.0124535932 0.025873436 -0.0040853293
## StockOptionLevel 0.0106902261 0.005407677 -0.0343228302
## TotalWorkingYears -0.0201850727 0.772893246 0.0264424712
## TrainingTimesLastYear -0.0057793350 -0.021736277 0.0014668806
## WorkLifeBalance -0.0194587102 0.030683082 0.0079631575
## YearsAtCompany -0.0038026279 0.514284826 -0.0236551067
## YearsInCurrentRole -0.0023047852 0.363817667 -0.0128148744
## YearsSinceLastPromotion -0.0182135678 0.344977638 0.0015667995
## YearsWithCurrManager -0.0276562139 0.344078883 -0.0367459053
## Attrition1 -0.1034811261 -0.159839582 0.0151702125
## BusinessTravel1 0.0086659585 -0.013449947 -0.0084404916
## Department1 0.0210008789 0.053129698 0.0236420721
## EducationField1 -0.0344008137 -0.041070150 -0.0271816823
## Gender1 0.0332516974 -0.031858492 -0.0414822051
## JobRole1 0.0224686645 -0.082090118 0.0080868505
## MaritalStatus1 0.0243599530 -0.075449582 0.0239374266
## OverTime1 0.0245394811 0.006089285 0.0214311446
## NumCompaniesWorked PercentSalaryHike
## ï..Age 0.299634758 0.003633585
## DailyRate 0.038153434 0.022703677
## DistanceFromHome -0.029250804 0.040235377
## Education 0.126316560 -0.011110941
## EnvironmentSatisfaction 0.012594323 -0.031701195
## HourlyRate 0.022156883 -0.009061986
## JobInvolvement 0.015012413 -0.017204572
## JobLevel 0.142501124 -0.034730492
## JobSatisfaction -0.055699426 0.020002039
## MonthlyIncome 0.149515216 -0.027268586
## MonthlyRate 0.017521353 -0.006429346
## NumCompaniesWorked 1.000000000 -0.010238309
## PercentSalaryHike -0.010238309 1.000000000
## PerformanceRating -0.014094873 0.773549996
## RelationshipSatisfaction 0.052733049 -0.040490081
## StockOptionLevel 0.030075475 0.007527748
## TotalWorkingYears 0.237638590 -0.020608488
## TrainingTimesLastYear -0.066054072 -0.005221012
## WorkLifeBalance -0.008365685 -0.003279636
## YearsAtCompany -0.118421340 -0.035991262
## YearsInCurrentRole -0.090753934 -0.001520027
## YearsSinceLastPromotion -0.036813892 -0.022154313
## YearsWithCurrManager -0.110319155 -0.011985248
## Attrition1 0.043493739 -0.013478202
## BusinessTravel1 -0.030742984 -0.025726943
## Department1 -0.035881612 -0.007840161
## EducationField1 -0.008663043 -0.011213502
## Gender1 -0.039147450 0.002732648
## JobRole1 -0.056838388 0.003231282
## MaritalStatus1 -0.035505389 0.012492329
## OverTime1 -0.020785821 -0.005432827
## PerformanceRating RelationshipSatisfaction
## ï..Age 0.0019038955 0.0535347197
## DailyRate 0.0004732963 0.0078460310
## DistanceFromHome 0.0271096185 0.0065574746
## Education -0.0245387912 -0.0091183767
## EnvironmentSatisfaction -0.0295479523 0.0076653835
## HourlyRate -0.0021716974 0.0013304528
## JobInvolvement -0.0290713334 0.0342968206
## JobLevel -0.0212220821 0.0216415105
## JobSatisfaction 0.0022971971 -0.0124535932
## MonthlyIncome -0.0171201382 0.0258734361
## MonthlyRate -0.0098114285 -0.0040853293
## NumCompaniesWorked -0.0140948728 0.0527330486
## PercentSalaryHike 0.7735499964 -0.0404900811
## PerformanceRating 1.0000000000 -0.0313514554
## RelationshipSatisfaction -0.0313514554 1.0000000000
## StockOptionLevel 0.0035064716 -0.0459524907
## TotalWorkingYears 0.0067436679 0.0240542918
## TrainingTimesLastYear -0.0155788817 0.0024965264
## WorkLifeBalance 0.0025723613 0.0196044057
## YearsAtCompany 0.0034351261 0.0193667869
## YearsInCurrentRole 0.0349862604 -0.0151229149
## YearsSinceLastPromotion 0.0178960661 0.0334925021
## YearsWithCurrManager 0.0228271689 -0.0008674968
## Attrition1 0.0028887517 -0.0458722789
## BusinessTravel1 0.0016833372 0.0089263034
## Department1 -0.0246035428 -0.0224144254
## EducationField1 -0.0056142134 -0.0043777110
## Gender1 -0.0138590184 0.0228683700
## JobRole1 -0.0244593806 -0.0250233999
## MaritalStatus1 0.0052066260 0.0225490707
## OverTime1 0.0043691201 0.0484928029
## StockOptionLevel TotalWorkingYears
## ï..Age 0.0375097124 0.680380536
## DailyRate 0.0421427964 0.014514739
## DistanceFromHome 0.0448719989 0.004628426
## Education 0.0184222202 0.148279697
## EnvironmentSatisfaction 0.0034321578 -0.002693070
## HourlyRate 0.0502633991 -0.002333682
## JobInvolvement 0.0215226404 -0.005533182
## JobLevel 0.0139839105 0.782207805
## JobSatisfaction 0.0106902261 -0.020185073
## MonthlyIncome 0.0054076767 0.772893246
## MonthlyRate -0.0343228302 0.026442471
## NumCompaniesWorked 0.0300754751 0.237638590
## PercentSalaryHike 0.0075277478 -0.020608488
## PerformanceRating 0.0035064716 0.006743668
## RelationshipSatisfaction -0.0459524907 0.024054292
## StockOptionLevel 1.0000000000 0.010135969
## TotalWorkingYears 0.0101359693 1.000000000
## TrainingTimesLastYear 0.0112740696 -0.035661571
## WorkLifeBalance 0.0041287300 0.001007646
## YearsAtCompany 0.0150580080 0.628133155
## YearsInCurrentRole 0.0508178728 0.460364638
## YearsSinceLastPromotion 0.0143521849 0.404857759
## YearsWithCurrManager 0.0246982266 0.459188397
## Attrition1 -0.1371449189 -0.171063246
## BusinessTravel1 -0.0282569533 0.007972110
## Department1 -0.0121929144 -0.015761512
## EducationField1 -0.0161847132 -0.027847626
## Gender1 0.0127157138 -0.046880939
## JobRole1 -0.0174347521 -0.131447230
## MaritalStatus1 -0.6625772917 -0.077886352
## OverTime1 -0.0004486707 0.012754266
## TrainingTimesLastYear WorkLifeBalance
## ï..Age -0.019620819 -0.021490028
## DailyRate 0.002452543 -0.037848051
## DistanceFromHome -0.036942234 -0.026556004
## Education -0.025100241 0.009819189
## EnvironmentSatisfaction -0.019359308 0.027627295
## HourlyRate -0.008547685 -0.004607234
## JobInvolvement -0.015337826 -0.014616593
## JobLevel -0.018190550 0.037817746
## JobSatisfaction -0.005779335 -0.019458710
## MonthlyIncome -0.021736277 0.030683082
## MonthlyRate 0.001466881 0.007963158
## NumCompaniesWorked -0.066054072 -0.008365685
## PercentSalaryHike -0.005221012 -0.003279636
## PerformanceRating -0.015578882 0.002572361
## RelationshipSatisfaction 0.002496526 0.019604406
## StockOptionLevel 0.011274070 0.004128730
## TotalWorkingYears -0.035661571 0.001007646
## TrainingTimesLastYear 1.000000000 0.028072207
## WorkLifeBalance 0.028072207 1.000000000
## YearsAtCompany 0.003568666 0.012089185
## YearsInCurrentRole -0.005737504 0.049856498
## YearsSinceLastPromotion -0.002066536 0.008941249
## YearsWithCurrManager -0.004095526 0.002759440
## Attrition1 -0.059477799 -0.063939047
## BusinessTravel1 0.016357219 0.004208799
## Department1 0.036875066 0.026382525
## EducationField1 0.049195353 0.041191131
## Gender1 -0.038786735 -0.002752679
## JobRole1 0.002694826 0.022249350
## MaritalStatus1 0.010628607 0.014708294
## OverTime1 -0.079113372 -0.027091878
## YearsAtCompany YearsInCurrentRole
## ï..Age 0.311308770 0.212901056
## DailyRate -0.034054768 0.009932015
## DistanceFromHome 0.009507720 0.018844999
## Education 0.069113696 0.060235554
## EnvironmentSatisfaction 0.001457549 0.018007460
## HourlyRate -0.019581616 -0.024106220
## JobInvolvement -0.021355427 0.008716963
## JobLevel 0.534738687 0.389446733
## JobSatisfaction -0.003802628 -0.002304785
## MonthlyIncome 0.514284826 0.363817667
## MonthlyRate -0.023655107 -0.012814874
## NumCompaniesWorked -0.118421340 -0.090753934
## PercentSalaryHike -0.035991262 -0.001520027
## PerformanceRating 0.003435126 0.034986260
## RelationshipSatisfaction 0.019366787 -0.015122915
## StockOptionLevel 0.015058008 0.050817873
## TotalWorkingYears 0.628133155 0.460364638
## TrainingTimesLastYear 0.003568666 -0.005737504
## WorkLifeBalance 0.012089185 0.049856498
## YearsAtCompany 1.000000000 0.758753737
## YearsInCurrentRole 0.758753737 1.000000000
## YearsSinceLastPromotion 0.618408865 0.548056248
## YearsWithCurrManager 0.769212425 0.714364762
## Attrition1 -0.134392214 -0.160545004
## BusinessTravel1 0.005212128 -0.005336424
## Department1 0.022920442 0.056315447
## EducationField1 -0.018692101 -0.010506203
## Gender1 -0.029747086 -0.041482729
## JobRole1 -0.074311010 -0.018338077
## MaritalStatus1 -0.059986004 -0.065821900
## OverTime1 -0.011687120 -0.029758009
## YearsSinceLastPromotion YearsWithCurrManager
## ï..Age 0.216513368 0.2020886024
## DailyRate -0.033228985 -0.0263631782
## DistanceFromHome 0.010028836 0.0144060484
## Education 0.054254334 0.0690653783
## EnvironmentSatisfaction 0.016193606 -0.0049987226
## HourlyRate -0.026715586 -0.0201232002
## JobInvolvement -0.024184292 0.0259758079
## JobLevel 0.353885347 0.3752806078
## JobSatisfaction -0.018213568 -0.0276562139
## MonthlyIncome 0.344977638 0.3440788833
## MonthlyRate 0.001566800 -0.0367459053
## NumCompaniesWorked -0.036813892 -0.1103191554
## PercentSalaryHike -0.022154313 -0.0119852485
## PerformanceRating 0.017896066 0.0228271689
## RelationshipSatisfaction 0.033492502 -0.0008674968
## StockOptionLevel 0.014352185 0.0246982266
## TotalWorkingYears 0.404857759 0.4591883971
## TrainingTimesLastYear -0.002066536 -0.0040955260
## WorkLifeBalance 0.008941249 0.0027594402
## YearsAtCompany 0.618408865 0.7692124251
## YearsInCurrentRole 0.548056248 0.7143647616
## YearsSinceLastPromotion 1.000000000 0.5102236358
## YearsWithCurrManager 0.510223636 1.0000000000
## Attrition1 -0.033018775 -0.1561993159
## BusinessTravel1 0.005222020 -0.0002285118
## Department1 0.040060967 0.0342824726
## EducationField1 0.002325656 -0.0041296947
## Gender1 -0.026984577 -0.0305989093
## JobRole1 -0.034749921 -0.0339826983
## MaritalStatus1 -0.030915079 -0.0385700737
## OverTime1 -0.012238823 -0.0415859987
## Attrition1 BusinessTravel1 Department1
## ï..Age -0.159205007 -0.0118073324 -0.031882283
## DailyRate -0.056651992 -0.0155388905 0.007108714
## DistanceFromHome 0.077923583 -0.0096960412 0.017224804
## Education -0.031372820 -0.0086698401 0.007996422
## EnvironmentSatisfaction -0.103368978 -0.0113099347 -0.019395271
## HourlyRate -0.006845550 -0.0041639831 -0.004143708
## JobInvolvement -0.130015957 0.0292999586 -0.024586062
## JobLevel -0.169104751 -0.0116958303 0.101963106
## JobSatisfaction -0.103481126 0.0086659585 0.021000879
## MonthlyIncome -0.159839582 -0.0134499467 0.053129698
## MonthlyRate 0.015170213 -0.0084404916 0.023642072
## NumCompaniesWorked 0.043493739 -0.0307429842 -0.035881612
## PercentSalaryHike -0.013478202 -0.0257269434 -0.007840161
## PerformanceRating 0.002888752 0.0016833372 -0.024603543
## RelationshipSatisfaction -0.045872279 0.0089263034 -0.022414425
## StockOptionLevel -0.137144919 -0.0282569533 -0.012192914
## TotalWorkingYears -0.171063246 0.0079721100 -0.015761512
## TrainingTimesLastYear -0.059477799 0.0163572195 0.036875066
## WorkLifeBalance -0.063939047 0.0042087993 0.026382525
## YearsAtCompany -0.134392214 0.0052121277 0.022920442
## YearsInCurrentRole -0.160545004 -0.0053364236 0.056315447
## YearsSinceLastPromotion -0.033018775 0.0052220205 0.040060967
## YearsWithCurrManager -0.156199316 -0.0002285118 0.034282473
## Attrition1 1.000000000 0.1270064832 0.063990596
## BusinessTravel1 0.127006483 1.0000000000 -0.002639604
## Department1 0.063990596 -0.0026396043 1.000000000
## EducationField1 0.026845546 -0.0234890783 0.013719502
## Gender1 0.029453253 -0.0448955126 -0.041583290
## JobRole1 0.057389161 0.0112895901 0.704252821
## MaritalStatus1 0.162070235 0.0309149876 0.056073435
## OverTime1 0.246117994 0.0427515162 0.007480968
## EducationField1 Gender1 JobRole1
## ï..Age -0.040872848 -0.0363105501 -0.112807055
## DailyRate 0.037709229 -0.0117161379 -0.003358019
## DistanceFromHome 0.002013453 -0.0018505280 0.003574933
## Education -0.039592150 -0.0165468274 0.007721031
## EnvironmentSatisfaction 0.043163491 0.0005083139 -0.014562184
## HourlyRate -0.021941219 -0.0004782971 -0.016551533
## JobInvolvement -0.002655278 0.0179597554 0.007401840
## JobLevel -0.044932672 -0.0394031027 -0.066895985
## JobSatisfaction -0.034400814 0.0332516974 0.022468664
## MonthlyIncome -0.041070150 -0.0318584918 -0.082090118
## MonthlyRate -0.027181682 -0.0414822051 0.008086851
## NumCompaniesWorked -0.008663043 -0.0391474496 -0.056838388
## PercentSalaryHike -0.011213502 0.0027326475 0.003231282
## PerformanceRating -0.005614213 -0.0138590184 -0.024459381
## RelationshipSatisfaction -0.004377711 0.0228683700 -0.025023400
## StockOptionLevel -0.016184713 0.0127157138 -0.017434752
## TotalWorkingYears -0.027847626 -0.0468809395 -0.131447230
## TrainingTimesLastYear 0.049195353 -0.0387867355 0.002694826
## WorkLifeBalance 0.041191131 -0.0027526788 0.022249350
## YearsAtCompany -0.018692101 -0.0297470859 -0.074311010
## YearsInCurrentRole -0.010506203 -0.0414827285 -0.018338077
## YearsSinceLastPromotion 0.002325656 -0.0269845772 -0.034749921
## YearsWithCurrManager -0.004129695 -0.0305989093 -0.033982698
## Attrition1 0.026845546 0.0294532532 0.057389161
## BusinessTravel1 -0.023489078 -0.0448955126 0.011289590
## Department1 0.013719502 -0.0415832902 0.704252821
## EducationField1 1.000000000 -0.0025040188 0.027910348
## Gender1 -0.002504019 1.0000000000 -0.043196964
## JobRole1 0.027910348 -0.0431969638 1.000000000
## MaritalStatus1 0.014419541 -0.0471825924 0.069833317
## OverTime1 0.002258600 -0.0419243480 0.043227665
## MaritalStatus1 OverTime1
## ï..Age -0.095028910 0.0280623571
## DailyRate -0.069585641 0.0091349699
## DistanceFromHome -0.014437031 0.0255136349
## Education 0.004052654 -0.0203217674
## EnvironmentSatisfaction -0.003593473 0.0701317268
## HourlyRate -0.017860506 -0.0077819744
## JobInvolvement -0.038497019 -0.0035067106
## JobLevel -0.076769478 0.0005440478
## JobSatisfaction 0.024359953 0.0245394811
## MonthlyIncome -0.075449582 0.0060892854
## MonthlyRate 0.023937427 0.0214311446
## NumCompaniesWorked -0.035505389 -0.0207858214
## PercentSalaryHike 0.012492329 -0.0054328267
## PerformanceRating 0.005206626 0.0043691201
## RelationshipSatisfaction 0.022549071 0.0484928029
## StockOptionLevel -0.662577292 -0.0004486707
## TotalWorkingYears -0.077886352 0.0127542663
## TrainingTimesLastYear 0.010628607 -0.0791133716
## WorkLifeBalance 0.014708294 -0.0270918785
## YearsAtCompany -0.059986004 -0.0116871205
## YearsInCurrentRole -0.065821900 -0.0297580089
## YearsSinceLastPromotion -0.030915079 -0.0122388226
## YearsWithCurrManager -0.038570074 -0.0415859987
## Attrition1 0.162070235 0.2461179942
## BusinessTravel1 0.030914988 0.0427515162
## Department1 0.056073435 0.0074809676
## EducationField1 0.014419541 0.0022585999
## Gender1 -0.047182592 -0.0419243480
## JobRole1 0.069833317 0.0432276651
## MaritalStatus1 1.000000000 -0.0175213816
## OverTime1 -0.017521382 1.0000000000Corrgram of variables
library("corrgram", lib.loc="~/R/win-library/3.4")
corrgram(Num,lower.panel=panel.shade,upper.panel=panel.pie,order=TRUE)
Correlation Tests
cor.test (Num[,"Attrition1"], Num[,"BusinessTravel1"])##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "BusinessTravel1"]
## t = 4.9059, df = 1468, p-value = 1.033e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.07637497 0.17698469
## sample estimates:
## cor
## 0.1270065Finding: Attrition Rate depend on Business Travel
cor.test (Num[,"Attrition1"], Num[,"DailyRate"])##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "DailyRate"]
## t = -2.1741, df = 1468, p-value = 0.02986
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.107468170 -0.005540584
## sample estimates:
## cor
## -0.05665199Finding: Attrition Rate depend a bit on Daily Rate
cor.test (Num[,"Attrition1"], Num[,"DistanceFromHome"])##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "DistanceFromHome"]
## t = 2.9947, df = 1468, p-value = 0.002793
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.02690331 0.12853894
## sample estimates:
## cor
## 0.07792358Finding: Attrition Rate depends on DistanceFromHome too
cor.test (Num[,"Attrition1"], Num[,"Education"])##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "Education"]
## t = -1.2026, df = 1468, p-value = 0.2293
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.08236816 0.01978637
## sample estimates:
## cor
## -0.03137282Finding: Attrition Rate doesnot depend upon Education
cor.test (Num[,"Attrition1"], Num[,"EnvironmentSatisfaction"])##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "EnvironmentSatisfaction"]
## t = -3.9819, df = 1468, p-value = 7.172e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.15368421 -0.05251908
## sample estimates:
## cor
## -0.103369Finding: Attrition Rate Depend upon Environment Satisfaction
cor.test (Num[,"Attrition1"], Num[,"Gender1"])##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "Gender1"]
## t = 1.129, df = 1468, p-value = 0.2591
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.02170689 0.08045955
## sample estimates:
## cor
## 0.02945325Finding: Attrition Rate does not Depend upon Gender of Employee
cor.test (Num[,"Attrition1"], Num[,"HourlyRate"])##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "HourlyRate"]
## t = -0.26229, df = 1468, p-value = 0.7931
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.05795272 0.04429741
## sample estimates:
## cor
## -0.00684555Finding: Attrition Rate does not Depend upon HourlyRate
cor.test (Num[,"Attrition1"], Num[,"JobInvolvement"])##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "JobInvolvement"]
## t = -5.0241, df = 1468, p-value = 5.677e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.17994723 -0.07941641
## sample estimates:
## cor
## -0.130016Finding: Attrition Rate depends on Job Involvement Also
cor.test (Num[,"Attrition1"], Num[,"JobLevel"])##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "JobLevel"]
## t = -6.5738, df = 1468, p-value = 6.795e-11
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2183444 -0.1190062
## sample estimates:
## cor
## -0.1691048Finding: Attrition Rate depend upon JobLevel also
cor.test (Num[,"Attrition1"], Num[,"JobSatisfaction"])##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "JobSatisfaction"]
## t = -3.9862, df = 1468, p-value = 7.043e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.15379490 -0.05263213
## sample estimates:
## cor
## -0.1034811Finding: Attrition Rate depend upon JobSatisfaction also
cor.test (Num[,"Attrition1"], Num[,"MaritalStatus1"])##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "MaritalStatus1"]
## t = 6.2928, df = 1468, p-value = 4.106e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1118698 0.2114456
## sample estimates:
## cor
## 0.1620702Finding: Attrition Rate depend upon MaritalStatus
cor.test (Num[,"Attrition1"], Num[,"MonthlyIncome"])##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "MonthlyIncome"]
## t = -6.2039, df = 1468, p-value = 7.147e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2092570 -0.1096079
## sample estimates:
## cor
## -0.1598396Finding: Attrition Rate depend upon Monthly Income
cor.test (Num[,"Attrition1"], Num[,"MonthlyRate"])##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "MonthlyRate"]
## t = 0.58131, df = 1468, p-value = 0.5611
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.03598515 0.06624629
## sample estimates:
## cor
## 0.01517021Finding: Attrition Rate doesnot depend upon Monthly Rate
cor.test (Num[,"Attrition1"], Num[,"NumCompaniesWorked"])##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "NumCompaniesWorked"]
## t = 1.668, df = 1468, p-value = 0.09553
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.00765073 0.09441125
## sample estimates:
## cor
## 0.04349374Finding: Attrition Rate doesnot depend much on Number of Companies Worked
cor.test (Num[,"Attrition1"], Num[,"OverTime1"])##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "OverTime1"]
## t = 9.7292, df = 1468, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1974754 0.2935516
## sample estimates:
## cor
## 0.246118Finding: Attrition Rate strongly depend on OverTime
cor.test (Num[,"Attrition1"], Num[,"PercentSalaryHike"])##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "PercentSalaryHike"]
## t = -0.51646, df = 1468, p-value = 0.6056
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.06456117 0.03767522
## sample estimates:
## cor
## -0.0134782Finding: Attrition Rate doesnot depend upon PercentSalaryHike too according to coorelation test
cor.test (Num[,"Attrition1"], Num[,"PerformanceRating"])##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "PerformanceRating"]
## t = 0.11068, df = 1468, p-value = 0.9119
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.04824583 0.05400823
## sample estimates:
## cor
## 0.002888752Finding: Attrition Rate totally don’t depend on PerformanceRating and it is also consistent with our Two way contingency Table
cor.test (Num[,"Attrition1"], Num[,"RelationshipSatisfaction"])##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "RelationshipSatisfaction"]
## t = -1.7594, df = 1468, p-value = 0.07871
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.096772771 0.005267532
## sample estimates:
## cor
## -0.04587228Finding: Attrition Rate doesnot depend significantly on RelationshipSatisfaction
cor.test (Num[,"Attrition1"], Num[,"StockOptionLevel"])##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "StockOptionLevel"]
## t = -5.3048, df = 1468, p-value = 1.301e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.18696143 -0.08662486
## sample estimates:
## cor
## -0.1371449Finding: Attrition Rate depend upon StockOptionLevel
cor.test (Num[,"Attrition1"], Num[,"TotalWorkingYears"])##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "TotalWorkingYears"]
## t = -6.6523, df = 1468, p-value = 4.062e-11
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2202643 -0.1209940
## sample estimates:
## cor
## -0.1710632Attrition Rate depend on Total Working Years
cor.test (Num[,"Attrition1"], Num[,"WorkLifeBalance"])##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "WorkLifeBalance"]
## t = -2.4548, df = 1468, p-value = 0.01421
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.11469157 -0.01285361
## sample estimates:
## cor
## -0.06393905Finding: AttritionRate Depend on WorkLifeBalance
cor.test (Num[,"Attrition1"], Num[,"YearsAtCompany"])##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "YearsAtCompany"]
## t = -5.1963, df = 1468, p-value = 2.319e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.18425364 -0.08384084
## sample estimates:
## cor
## -0.1343922Finding: Attrition Rate depends on YearsAtCompany
cor.test (Num[,"Attrition1"], Num[,"YearsInCurrentRole"])##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "YearsInCurrentRole"]
## t = -6.232, df = 1468, p-value = 6.003e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2099491 -0.1103231
## sample estimates:
## cor
## -0.160545Finding: AttritionRate deoend on YearsInCurrentRole too
cor.test (Num[,"Attrition1"], Num[,"YearsSinceLastPromotion"])##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "YearsSinceLastPromotion"]
## t = -1.2658, df = 1468, p-value = 0.2058
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.08400442 0.01813930
## sample estimates:
## cor
## -0.03301878Finding: Attrition Rate doesnot depend much on YearsSinceLastPromotion
cor.test (Num[,"Attrition1"], Num[,"YearsWithCurrManager"])##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "YearsWithCurrManager"]
## t = -6.0591, df = 1468, p-value = 1.737e-09
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2056842 -0.1059177
## sample estimates:
## cor
## -0.1561993