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.

options(repos = c(CRAN = "https://cran.rstudio.com/"))
install.packages("plotly")

## 
## The downloaded binary packages are in
##  /var/folders/q1/gl10h4f94b3dsmnpk2jdysgr0000gn/T//Rtmp3vbT8m/downloaded_packages

install.packages("dplyr")

## 
## The downloaded binary packages are in
##  /var/folders/q1/gl10h4f94b3dsmnpk2jdysgr0000gn/T//Rtmp3vbT8m/downloaded_packages

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

Number 1

Perform the chi-square test (.5 point) Choose any two appropriate variables from the data and perform the chi-square test, displaying the results.

department_test <- table(hr$Department, hr$left)
print(department_test)

##              
##                  0    1
##   accounting   563  204
##   hr           524  215
##   IT           954  273
##   management   539   91
##   marketing    655  203
##   product_mng  704  198
##   RandD        666  121
##   sales       3126 1014
##   support     1674  555
##   technical   2023  697

chi_department <- chisq.test(department_test)
print(chi_department)

## 
##  Pearson's Chi-squared test
## 
## data:  department_test
## X-squared = 86.825, df = 9, p-value = 7.042e-15

Interpret the results in technical terms (.5 point) For each chi-square test, explain what the test’s p-value means (significance).

The P-value is extremely small, meaning that the probability of these results being random is very small. There is a dependence between department and left the company.

Interpret the results in non-technical terms (1 point) For each chi-square test, what do the results mean in non-techical terms.

Some departments have a higher turnover rate compared to others most likely due to things like satisfaction level, salary, or workload.

Create a plot that helps visualize the chi-square test (.5 point) For each chi-square test, create a graph to help visualize the difference between means, if any. The title must be the non-technical interpretation.

ggplot(hr, aes(x = Department, fill = factor(left))) +
  geom_bar(position = "dodge") +
  labs(
    title = "Employee Turnover by Department",
    x = "Department",
    y = "Count of Employees",
    fill = "Left the Job"
  ) +
  theme_minimal()

Number 2

Perform the chi-square test (.5 point) Choose any two appropriate variables from the data and perform the chi-square test, displaying the results.

 salary_test <- table(hr$salary, hr$left)
  print(salary_test)

##         
##             0    1
##   high   1155   82
##   low    5144 2172
##   medium 5129 1317

  chi_salary <- chisq.test(salary_test)
  print(chi_salary)

## 
##  Pearson's Chi-squared test
## 
## data:  salary_test
## X-squared = 381.23, df = 2, p-value < 2.2e-16

Interpret the results in technical terms (.5 point) For each chi-square test, explain what the test’s p-value means (significance).

The P-value is extremely small, meaning that the probability of these results being random is very small. There is a dependence between salary and left the company.

Interpret the results in non-technical terms (1 point) For each chi-square test, what do the results mean in non-techical terms.

Those employees who had a high salary are mostly likely to have a lower employee turnover while those with a low salary most likely will have a higher turnover.

Create a plot that helps visualize the chi-square test (.5 point) For each chi-square test, create a graph to help visualize the difference between means, if any. The title must be the non-technical interpretation.

ggplot(hr, aes(x = salary, fill = factor(left))) +
  geom_bar(position = "dodge") +
  labs(
    title = "Employee Turnover by Salary",
    x = "Salary",
    y = "Count of Employees",
    fill = "Left the Job"
  ) +
  theme_minimal()

Number 3

Perform the chi-square test (.5 point) Choose any two appropriate variables from the data and perform the chi-square test, displaying the results.

accident_test <- table(hr$Work_accident, hr$left)
  print(accident_test)

##    
##        0    1
##   0 9428 3402
##   1 2000  169

  chi_accident <- chisq.test(accident_test)
  print(chi_accident)

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  accident_test
## X-squared = 357.56, df = 1, p-value < 2.2e-16

Interpret the results in technical terms (.5 point) For each chi-square test, explain what the test’s p-value means (significance).

The P-value is extremely small, meaning that the probability of these results being random is very small. There is a dependence between having a work accident and leaving the company.

Interpret the results in non-technical terms (1 point) For each chi-square test, what do the results mean in non-techical terms.

Those employees who had work accidents were more likely to not leave the company probably due to things like receiving worker compensation or fear of not finding another job at a different company following the accident.

Create a plot that helps visualize the chi-square test (.5 point) For each chi-square test, create a graph to help visualize the difference between means, if any. The title must be the non-technical interpretation.

ggplot(hr, aes(x = Work_accident, fill = factor(left))) +
    geom_bar(position = "dodge") +
  labs(
    title = "Employee Turnover by Work Accident",
    x = "Work Accident",
    y = "Count of Employees",
    fill = "Left the Job"
  ) +
    theme_minimal()

Number 4

Perform the chi-square test (.5 point) Choose any two appropriate variables from the data and perform the chi-square test, displaying the results.

 promotion_test <- table(hr$promotion_last_5years, hr$left)
  print(promotion_test)

##    
##         0     1
##   0 11128  3552
##   1   300    19

  chi_promotion <- chisq.test(promotion_test)
  print(chi_promotion)

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  promotion_test
## X-squared = 56.262, df = 1, p-value = 6.344e-14

Interpret the results in technical terms (.5 point) For each chi-square test, explain what the test’s p-value means (significance).

The P-value is extremely small, meaning that the probability of these results being random is very small. There is a dependence between getting a promotion and staying at the company.

Interpret the results in non-technical terms (1 point) For each chi-square test, what do the results mean in non-techical terms.

Those employees who got a promotion are more likely to stay at the company.

Create a plot that helps visualize the chi-square test (.5 point) For each chi-square test, create a graph to help visualize the difference between means, if any. The title must be the non-technical interpretation.

ggplot(hr, aes(x = promotion_last_5years, fill = factor(left))) +
    geom_bar(position = "dodge") +
  labs(
    title = "Employee Turnover by Promotion in the Last 5 Years",
    x = "Promotion",
    y = "Count of Employees",
    fill = "Left the Job"
  ) +
    theme_minimal()

Assignment9

Alice Pifferi & Alex Senior

2024-11-22

Number 1

Perform the chi-square test (.5 point) Choose any two appropriate variables from the data and perform the chi-square test, displaying the results.

Interpret the results in technical terms (.5 point) For each chi-square test, explain what the test’s p-value means (significance).

Interpret the results in non-technical terms (1 point) For each chi-square test, what do the results mean in non-techical terms.

Create a plot that helps visualize the chi-square test (.5 point) For each chi-square test, create a graph to help visualize the difference between means, if any. The title must be the non-technical interpretation.

Number 2

Perform the chi-square test (.5 point) Choose any two appropriate variables from the data and perform the chi-square test, displaying the results.

Interpret the results in technical terms (.5 point) For each chi-square test, explain what the test’s p-value means (significance).

Interpret the results in non-technical terms (1 point) For each chi-square test, what do the results mean in non-techical terms.

Create a plot that helps visualize the chi-square test (.5 point) For each chi-square test, create a graph to help visualize the difference between means, if any. The title must be the non-technical interpretation.

Number 3

Perform the chi-square test (.5 point) Choose any two appropriate variables from the data and perform the chi-square test, displaying the results.

Interpret the results in technical terms (.5 point) For each chi-square test, explain what the test’s p-value means (significance).

Interpret the results in non-technical terms (1 point) For each chi-square test, what do the results mean in non-techical terms.

Create a plot that helps visualize the chi-square test (.5 point) For each chi-square test, create a graph to help visualize the difference between means, if any. The title must be the non-technical interpretation.

Number 4

Perform the chi-square test (.5 point) Choose any two appropriate variables from the data and perform the chi-square test, displaying the results.

Interpret the results in technical terms (.5 point) For each chi-square test, explain what the test’s p-value means (significance).

Interpret the results in non-technical terms (1 point) For each chi-square test, what do the results mean in non-techical terms.

Create a plot that helps visualize the chi-square test (.5 point) For each chi-square test, create a graph to help visualize the difference between means, if any. The title must be the non-technical interpretation.