Jacob Stoughton and Jakub Kepa
library(plotly)
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## last_plot
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## filter
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## layout
library(dplyr)
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library(readr)
library(plotly)
library(AER)
## Loading required package: car
## Loading required package: carData
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## recode
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hr <- read_csv('https://raw.githubusercontent.com/aiplanethub/Datasets/refs/heads/master/HR_comma_sep.csv')
## Rows: 14999 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Department, salary
## dbl (8): satisfaction_level, last_evaluation, number_project, average_montly...
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## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Perform four (4) t-tests using any appropriate variables
(continuous) by the variable left. Note that the variable left describes
whether the employee left the company (left = 1), or not (left = 0)
T-Test 1: Average Monthly Hours
t.test(hr$average_montly_hours ~ hr$left)
##
## Welch Two Sample t-test
##
## data: hr$average_montly_hours by hr$left
## t = -7.5323, df = 4875.1, p-value = 5.907e-14
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## -10.534631 -6.183384
## sample estimates:
## mean in group 0 mean in group 1
## 199.0602 207.4192
P-Value: The p-value is very low therefore there is a significant
difference of average monthly hours between employees who have left and
stayed.
T-Test: The t-test shows that the mean difference in hours betweeen
the two groups is significant
Non-technical Interpretation: Employees who work more monthly hours
are more likely to leave.
Visual:
plot_ly(hr,
x = ~factor(left, levels = c(0,1), labels = c("Stayed", "Left")) ,
y = ~average_montly_hours ,
type = 'box') %>%
layout(title = "People Who Work More Hours are More Likely to Leave",
yaxis = list(title = "Average Monthly Hours"),
xaxis = list(title = "Employment Status"))
T-Test 2: Time Spent at Company
t.test(hr$time_spend_company ~ hr$left)
##
## Welch Two Sample t-test
##
## data: hr$time_spend_company by hr$left
## t = -22.631, df = 9625.6, p-value < 2.2e-16
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## -0.5394767 -0.4534706
## sample estimates:
## mean in group 0 mean in group 1
## 3.380032 3.876505
P-Value: The p-value is very low therefore the difference in the
time spent at the company between employees who stayed and left is not
due to chance.
T-test: The t-test shows that the mean difference in time spent
between those who stayed and left is significant.
Non-technical Interpretation: Employees who have worked at the
company longer are more likely to leave.
Visual:
plot_ly(hr,
x = ~factor(left, levels = c(0,1), labels = c("Stayed", "Left")) ,
y = ~time_spend_company ,
type = 'box') %>%
layout(title = "Longer Tenured Employees are More Likely to Leave",
yaxis = list(title = "Time Spent At Company"),
xaxis = list(title = "Employment Status"))
T-Test 3: Satisfaction Level
t.test(hr$satisfaction_level ~ hr$left)
##
## Welch Two Sample t-test
##
## data: hr$satisfaction_level by hr$left
## t = 46.636, df = 5167, p-value < 2.2e-16
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## 0.2171815 0.2362417
## sample estimates:
## mean in group 0 mean in group 1
## 0.6668096 0.4400980
P-Value: The p-value is very low therefore the difference in the
satisfaction level of employees who stayed and left is not due to
chance.
T-Test: The t-test shows us that the mean satisfaction levels of the
employees who stayed and left are significantly different.
Non-technical Interpretation: Employees with a Lower Satisfaction
Level are More Likely to Leave.
Visual:
plot_ly(hr,
x = ~factor(left, levels = c(0,1), labels = c("Stayed", "Left")) ,
y = ~satisfaction_level ,
type = 'box') %>%
layout(title = "Employees with Lower Satisfaction Level are More Likely to Leave",
yaxis = list(title = "Employee Satisfaction Level"),
xaxis = list(title = "Employment Status"))
T-Test 4: Number of Projects
t.test(hr$number_project ~ hr$left)
##
## Welch Two Sample t-test
##
## data: hr$number_project by hr$left
## t = -2.1663, df = 4236.5, p-value = 0.03034
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## -0.131136535 -0.006540119
## sample estimates:
## mean in group 0 mean in group 1
## 3.786664 3.855503
P-Value: Using the significance level of 0.01 (due to large
dataset), the p-value is small, therefore any relations between the data
of the two groups is likely insignificant
T-Test: The means for number of projects between employees who left
and stayed are similar and not significant
Non-technical Interpretation: Number of projects has little to no
effect on employees leaving
Visual:
plot_ly(hr,
x = ~factor(left, levels = c(0,1), labels = c("Stayed", "Left")) ,
y = ~number_project ,
type = 'box') %>%
layout(title = "Number of Projects has Little Effect on Employees Leaving",
yaxis = list(title = "Number of Projects"),
xaxis = list(title = "Employment Status"))