Assignment 8

library(readr)

## Warning: package 'readr' was built under R version 4.4.2

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.4.2

library(dplyr)

## Warning: package 'dplyr' was built under R version 4.4.2

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

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...
## 
## 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.

Conduct Test 1

t_test1 <- t.test(satisfaction_level ~ left, data = hr)

Print Test 1

print(t_test1)

## 
##  Welch Two Sample t-test
## 
## data:  satisfaction_level by 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

Display Test 1

ggplot(hr, aes(x = factor(left), y = satisfaction_level)) +
  geom_boxplot() +
  labs(title = "Comparison of Satisfaction Levels between Employees Who Stayed and Left",
       x = "Left (0 = Stayed, 1 = Left)",
       y = "Satisfaction Level")

Technical Interpretation: The p-value here tells us if there’s a statistically significant difference in satisfaction levels between employees who left (left = 1) and those who stayed (left = 0). A p-value < 0.05 suggests a significant difference.

Non-Technical Interpretation: This test helps us understand if employees who left the company had different satisfaction levels compared to those who stayed. A low p-value would indicate that dissatisfaction is likely linked to employees leaving.

Conduct Test 2

t_test2 <- t.test(last_evaluation ~ left, data = hr)

Print Test 2

print(t_test2)

## 
##  Welch Two Sample t-test
## 
## data:  last_evaluation by left
## t = -0.72534, df = 5154.9, p-value = 0.4683
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
##  -0.009772224  0.004493874
## sample estimates:
## mean in group 0 mean in group 1 
##       0.7154734       0.7181126

Display Test 2

ggplot(hr, aes(x = factor(left), y = last_evaluation)) +
  geom_boxplot() +
  labs(title = "Comparison of Last Evaluation Scores between Employees Who Stayed and Left",
       x = "Left (0 = Stayed, 1 = Left)",
       y = "Last Evaluation Score")

Technical Interpretation: The p-value shows whether there’s a significant difference in the last evaluation scores between those who left and those who stayed.

Non-Technical Interpretation: This test checks if employees who left received different performance evaluations than those who stayed. A significant result might indicate that high or low evaluations correlate with attrition.

Conduct Test 3

t_test3 <- t.test(average_montly_hours ~ left, data = hr)

Print Test 3

print(t_test3)

## 
##  Welch Two Sample t-test
## 
## data:  average_montly_hours by 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

Display Test 3

ggplot(hr, aes(x = factor(left), y = average_montly_hours)) +
  geom_boxplot() +
  labs(title = "Comparison of Average Monthly Hours between Employees Who Stayed and Left",
       x = "Left (0 = Stayed, 1 = Left)",
       y = "Average Monthly Hours")

Technical Interpretation: The p-value tells us if there’s a statistically significant difference in the average monthly hours worked by employees who left versus those who stayed.

Non-Technical Interpretation: This test helps us determine if overwork or underwork could be a reason employees are leaving. A significant result suggests that employees working different hours may have higher attrition rates.

Conduct Test 4

t_test4 <- t.test(time_spend_company ~ left, data = hr)

Print Test 4

print(t_test4)

## 
##  Welch Two Sample t-test
## 
## data:  time_spend_company by 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

Display Test 4

ggplot(hr, aes(x = factor(left), y = time_spend_company)) +
  geom_boxplot() +
  labs(title = "Comparison of Tenure between Employees Who Stayed and Left",
       x = "Left (0 = Stayed, 1 = Left)",
       y = "Years Spent at Company")

Technical Interpretation: This p-value tests if there’s a significant difference in the number of years employees have spent at the company between those who left and those who stayed.

Non-Technical Interpretation: This test shows if the duration of employment impacts the likelihood of employees leaving. A significant p-value would indicate that those who stayed may have a different tenure than those who left.