## 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.
  1. Department
## 
##  Pearson's Chi-squared test
## 
## data:  hr$Department and hr$left
## X-squared = 86.825, df = 9, p-value = 7.042e-15

Technical Interpretation: The chi-square test comparing department and left resulted in a test statistic of X² = 86.825 with 9 degrees of freedom and a p-value of 7.042e-15, indicating a statistically significant association between the two variables.

What the p-value means: The p-value (7.042e-15) is far below the common significance level of 0.05, meaning we reject the null hypothesis and conclude that an employee’s department is related to whether or not they left the company.

Non-Technical Interpretation: Employees’ decisions to leave the company vary depending on which department they work in — some departments see higher turnover than others.

  1. salary
## 
##  Pearson's Chi-squared test
## 
## data:  hr$salary and hr$left
## X-squared = 381.23, df = 2, p-value < 2.2e-16

Technical Interpretation: The chi-square test comparing salary and left resulted in a test statistic of X² = 381.23 with 2 degrees of freedom and a p-value less than 2.2e-16, indicating a highly significant association between the two variables.

What the p-value means: Since the p-value is far below 0.05, we reject the null hypothesis and conclude that an employee’s salary level is significantly related to whether or not they left the company.

Non-Technical Interpretation: Employees’ decisions to leave the company vary depending on their salary level — some salary groups have higher turnover than others.

  1. Work accident
## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  hr$Work_accident and hr$left
## X-squared = 357.56, df = 1, p-value < 2.2e-16

Technical Interpretation: The chi-square test comparing Work_accident and left resulted in a test statistic of X² = 357.56 with 1 degree of freedom and a p-value less than 2.2e-16, indicating a highly significant association between the two variables.

What the p-value means: The p-value is far below 0.05, so we reject the null hypothesis and conclude that whether or not an employee had a work accident is significantly related to whether they left the company.

Non-Technical Interpretation: Employees’ decisions to leave the company vary depending on whether they had a work accident — those who did may be less likely to leave.

  1. Promotion last 5 years
## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  hr$promotion_last_5years and hr$left
## X-squared = 56.262, df = 1, p-value = 6.344e-14

Technical Interpretation: The chi-square test comparing promotion_last_5years and left resulted in a test statistic of X² = 56.262 with 1 degree of freedom and a p-value of 6.344e-14, indicating a statistically significant association between the two variables.

What the p-value means: Since the p-value is far below 0.05, we reject the null hypothesis and conclude that whether or not an employee received a promotion in the last 5 years is significantly related to whether they left the company.

Non-Technical Interpretation:Employees’ decisions to leave the company vary depending on whether they received a promotion in the last 5 years — those who didn’t may be more likely to leave.