Starter Code:

library(readr)

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.
library(plotly)
## Loading required package: ggplot2
## 
## Attaching package: 'plotly'
## 
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## 
## The following object is masked from 'package:stats':
## 
##     filter
## 
## The following object is masked from 'package:graphics':
## 
##     layout
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
options(scipen=999)

NOTE: Left = 1, Stayed = 0

T-Test 1: last evaluation vs left

t.test(hr$last_evaluation ~ hr$left)
## 
##  Welch Two Sample t-test
## 
## data:  hr$last_evaluation by hr$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

P-value interpretation:

The p-value is 0.4683, which is a lot higher than the significance threshold I keep of 0.05; this suggests that there is a no statistically significant difference between the means of either group.

T-Test Interpretation:

The difference between the means is .718-.715, which is not very significant.

Non-Technical Interpretation:

Evaluation ratings aren’t significantly different between active and non-active employees.

Plot:

plot_ly(hr, 
        x = ~factor(left, levels = c(0, 1), labels = c('Stayed', 'Left')), 
        y = ~last_evaluation, 
        type = 'box') %>%
  layout(
    title = 'Evaluation ratings are not significantly different between active and non-active employees.',
    xaxis = list(title = 'Left Status'),
    yaxis = list(title = 'Last Evaluation')
  )

T-Test 2: average monthly hours vs left

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 = 0.00000000000005907
## 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 interpretation:

The p-value is extremely small, which means the differenct betweens means of average monthly hours and left status is significant.

T-Test Interpretation:

The difference between the means is 199.0602-207.4192, which is a difference of around 8 hours a month. That’s a whole extra work day.

Non-Technical Interpretation:

Employees who stayed worked less than whose who left.

Plot:

plot_ly(hr, 
        x = ~factor(left, levels = c(0, 1), labels = c('Stayed', 'Left')), 
        y = ~average_montly_hours, 
        type = 'box') %>%
  layout(
    title = 'Employees who stayed worked less than whose who left.',
    xaxis = list(title = 'Left Status'),
    yaxis = list(title = 'Average Monthly Hours')
  )

T-Test 3: number of projects vs left

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 interpretation:

The p-value is less than the significance level of .05, which means there is a tiny bit of a difference between the mean number of projects of either groups.

T-Test Interpretation:

The difference between the means is 3.786664-3.855503, which is a difference of around a tenth of a project.

Non-Technical Interpretation:

Employees who stayed did a little less project work than those who left.

Plot:

plot_ly(hr, 
        x = ~factor(left, levels = c(0, 1), labels = c('Stayed', 'Left')), 
        y = ~number_project, 
        type = 'box') %>%
  layout(
    title = 'Employees who stayed did a little less project work than those who left.',
    xaxis = list(title = 'Left Status'),
    yaxis = list(title = 'Number of Projects')
  )

T-Test 4: time spend company vs left

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 < 0.00000000000000022
## 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 interpretation:

The p-value is teeny tiny, which means there is a significant different between the years spent at the company between either group.

T-Test Interpretation:

The difference between the means is 3.876505-3.380032, which is a difference of around six months.

Non-Technical Interpretation:

Employees who stayed worked at the company longer than those who left.

Plot:

plot_ly(hr, 
        x = ~factor(left, levels = c(0, 1), labels = c('Stayed', 'Left')), 
        y = ~time_spend_company, 
        type = 'box') %>%
  layout(
    title = 'Employees who stayed worked at the company longer than those who left.',
    xaxis = list(title = 'Left Status'),
    yaxis = list(title = 'Years with Company')
  )