Assignment 8

library(readr)
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

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.

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

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

Interpret the results in technical terms (.5 point) For each t-test, explain what the test’s p-value means (significance).

The p-value is not very small, therefore the difference between means of last evalutation by left is not significant.

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

Last evaluation is not a deciding factor in whether employees decide to leave.

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

plot_data <- hr %>% 
  mutate(left = as.factor(ifelse(left == 0 , 'Not Left' , 'Left')))

plot_ly(plot_data , 
        x = ~left ,
        y = ~last_evaluation ,
        type = 'box')%>%
  layout(title = "Last evaluation is not a deciding factor in whether employees decide to leave",
         xaxis = list(title = "Employee Status"),
         yaxis = list(title = "Last Evaluation"))

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

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 = 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

Interpret the results in technical terms (.5 point) For each t-test, explain what the test’s p-value means (significance).

The p-value is very small, therefore the difference between means of average monthly hours by left is significant.

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

Those who left the company and those who stayed had a significant difference in average monthly hours.

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

plot_data <- hr %>% 
  mutate(left = as.factor(ifelse(left == 0 , 'Not Left' , 'Left')))

plot_ly(plot_data , 
        x = ~left ,
        y = ~average_montly_hours ,
        type = 'box') %>%
  layout(title = "Difference in monthly hours of those who left and those who stayed",
         xaxis = list(title = "Employee Status"),
         yaxis = list(title = "Average Monthly Hours"))

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

t.test(hr$satisfaction_level ~ hr$left)

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

Interpret the results in technical terms (.5 point) For each t-test, explain what the test’s p-value means (significance).

The p-value is very small, therefore the difference between means of satisfaction level by left is significant.

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

Those who left the company and those who stayed has a significant difference in levels of job satisfaction.

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

plot_data <- hr %>% 
  mutate(left = as.factor(ifelse(left == 0 , 'Not Left' , 'Left')))

plot_ly(plot_data , 
        x = ~left ,
        y = ~satisfaction_level ,
        type = 'box') %>%
layout(title = "Difference in satisfaction levels of those who left and those who stayed",
       xaxis = list(title = "Employee Status"),
       yaxis = list(title = "Satisfaction Level"))

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

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 < 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

Interpret the results in technical terms (.5 point) For each t-test, explain what the test’s p-value means (significance).

The p-value is very small, therefore the difference between means of time spend company by left is significant.

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

Those employees who left the company and those who stayed had significance differences in lengths of employment.

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

plot_data <- hr %>% 
  mutate(left = as.factor(ifelse(left == 0, 'Not Left', 'Left')))

plot_ly(plot_data, 
        x = ~left, 
        y = ~time_spend_company, 
        type = 'box') %>%
  layout(title = "Difference in time spent at company of those who left and those who stayed",
         xaxis = list(title = "Employee Status"),
         yaxis = list(title = "Time Spent at Company"))

Assignment 8

Stephanie Reap

2024-11-13

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

Interpret the results in technical terms (.5 point) For each t-test, explain what the test’s p-value means (significance).

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

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

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

Interpret the results in technical terms (.5 point) For each t-test, explain what the test’s p-value means (significance).

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

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

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

Interpret the results in technical terms (.5 point) For each t-test, explain what the test’s p-value means (significance).

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

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

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

Interpret the results in technical terms (.5 point) For each t-test, explain what the test’s p-value means (significance).

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

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