Assign 8

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(hr1$average_montly_hours ~ hr1$Employee_Status)

## 
##  Welch Two Sample t-test
## 
## data:  hr1$average_montly_hours by hr1$Employee_Status
## t = 7.5323, df = 4875.1, p-value = 5.907e-14
## alternative hypothesis: true difference in means between group left and group stayed is not equal to 0
## 95 percent confidence interval:
##   6.183384 10.534631
## sample estimates:
##   mean in group left mean in group stayed 
##             207.4192             199.0602

There is a signifigant difference between means, where employees that left at least 6 hours more

Descrptive: employees that left, on average, work more hours, at least 3% more

Prescriptive: to reduce employee attrition, average monthly hours can be reduced by 3% for those who work longer hours

plot_ly(
  hr1,
  x = ~Employee_Status,
  y = ~average_montly_hours,
  type = 'box',
  color = ~Employee_Status,
  colors = c('#1e9b20', 'blue')
) %>%
   layout(title = 'employees that left, on average, work more hours, at least 3% more')

Technical interpretation of t-test

The p-value helps determine if the difference in means is statistically significant.

Here, if the p-value is below a threshold (typically 0.05), it suggests that the difference in average monthly hours

between employees who stayed and those who left is statistically significant.