Assignment 8

library(readr)
library(dplyr)

## 
## 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

library(ggplot2)

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.

T-test 1: Satisfaction Level vs. Employee Status

t1 <- t.test(satisfaction_level ~ left, data = hr)
t1

## 
##  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

• Technical Interpretation: The p-value is very small indicating there is a statistically significant difference in average satisfaction between employees who left and stayed.

• Non-technical Interpretation: Employees who left the company had on average a lower satisfaction level than those who stayed.

ggplot(hr, aes(x = factor(left, labels = c("Stayed", "Left")), y = satisfaction_level, fill = factor(left))) +
  geom_boxplot() +
  labs(title = "Employees who left had lower satisfaction levels",
       x = "Employee Status", y = "Satisfaction Level") +
  theme_minimal()

T-test 2: Last Evaluation vs. Employee Status

t2 <- t.test(last_evaluation ~ left, data = hr)
t2

## 
##  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

• Technical Interpretation: Since the p-value is greater than 0.05, there is no significant difference between the last evaluation scores of employees who left and who stayed.

• Non-technical Interpretation: Employees who left and employees who stayed had similar performance evaluations overall, but those who left had slightly higher.

ggplot(hr, aes(x = factor(left, labels = c("Stayed", "Left")), y = last_evaluation, fill = factor(left))) +
  geom_boxplot() +
  labs(title = "Employees who left and stayed had similar last evaluations",
       x = "Employee Status", y = "Last Evaluation Score") +
  theme_minimal()

T-test 3: Average Monthly Hours vs. Employee Status

t3 <- t.test(average_montly_hours ~ left, data = hr)
t3

## 
##  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

• Technical Interpretation: The p-value is very small indicating that there is a statistically significant difference in the average monthly hours worked between employees who left vs. stayed.

• Non-technical Interpretation: Employees who left worked more hours per month on average compared to those who stayed.

ggplot(hr, aes(x = factor(left, labels = c("Stayed", "Left")), y = average_montly_hours, fill = factor(left))) +
  geom_boxplot() +
  labs(title = "Employees who left worked more hours per month",
       x = "Employee Status", y = "Average Monthly Hours") +
  theme_minimal()

T-test 4: Time Spent at Company vs. Employee Status

t4 <- t.test(time_spend_company ~ left, data = hr)
t4

## 
##  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

• Technical Interpretation: Since the p-value is small, we can conclude time spent at the company significantly differs between those who left and stayed.

• Non-technical Interpretation: Employees who left the company had been there longer on average before leaving.

ggplot(hr, aes(x = factor(left, labels = c("Stayed", "Left")), y = time_spend_company, fill = factor(left))) +
  geom_boxplot() +
  labs(title = "Employees who left had spent more years at the company",
       x = "Employee Status", y = "Years at Company") +
  theme_minimal()