R Markdown

## spc_tbl_ [14,999 × 10] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ satisfaction_level   : num [1:14999] 0.38 0.8 0.11 0.72 0.37 0.41 0.1 0.92 0.89 0.42 ...
##  $ last_evaluation      : num [1:14999] 0.53 0.86 0.88 0.87 0.52 0.5 0.77 0.85 1 0.53 ...
##  $ number_project       : num [1:14999] 2 5 7 5 2 2 6 5 5 2 ...
##  $ average_montly_hours : num [1:14999] 157 262 272 223 159 153 247 259 224 142 ...
##  $ time_spend_company   : num [1:14999] 3 6 4 5 3 3 4 5 5 3 ...
##  $ Work_accident        : num [1:14999] 0 0 0 0 0 0 0 0 0 0 ...
##  $ left                 : num [1:14999] 1 1 1 1 1 1 1 1 1 1 ...
##  $ promotion_last_5years: num [1:14999] 0 0 0 0 0 0 0 0 0 0 ...
##  $ Department           : chr [1:14999] "sales" "sales" "sales" "sales" ...
##  $ salary               : chr [1:14999] "low" "medium" "medium" "low" ...
##  - attr(*, "spec")=
##   .. cols(
##   ..   satisfaction_level = col_double(),
##   ..   last_evaluation = col_double(),
##   ..   number_project = col_double(),
##   ..   average_montly_hours = col_double(),
##   ..   time_spend_company = col_double(),
##   ..   Work_accident = col_double(),
##   ..   left = col_double(),
##   ..   promotion_last_5years = col_double(),
##   ..   Department = col_character(),
##   ..   salary = col_character()
##   .. )
##  - attr(*, "problems")=<externalptr>

1 Salary vs. left

## 
##  Pearson's Chi-squared test
## 
## data:  table1
## X-squared = 381.23, df = 2, p-value < 2.2e-16

p-value interpretation: The p-value is less than 0.1, therefore the probability of these results being random is very small.

Chi-square test interpretation: There is a dependence between salary level and whether an employee left the company.

Non-Technical Interpretation: Employees with low salaries are more likely to leave the company.

2: promotion_last_5years vs. left

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  table2
## X-squared = 56.262, df = 1, p-value = 6.344e-14

p-value interpretation: The p-value is less than 0.1, therefore the probability of these results being random is very small.

Chi-square test interpretation: There is a dependence between promotion history and whether an employee left.

Non-Technical Interpretation: Employees who didn’t get promoted in the last 5 years are more likely to leave.

3 department vs. left

## 
##  Pearson's Chi-squared test
## 
## data:  table3
## X-squared = 86.825, df = 9, p-value = 7.042e-15

p-value interpretation: The p-value is less than 0.1, therefore the probability of these results being random is very small.

Chi-square test interpretation: There is a dependence between department and attrition status.

Non-Technical Interpretation: Some departments, like sales and technical, have higher employee turnover rates.

4 Work_accident vs. left

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  table4
## X-squared = 357.56, df = 1, p-value < 2.2e-16

p-value interpretation: The p-value is less than 0.1, therefore the probability of these results being random is very small.

Chi-square test interpretation: There is a dependence between work accident history and whether an employee left.

Non-Technical Interpretation:Employees who had a work accident are less likely to leave the company.