TEST 1: Left, work accident

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

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

chi-square test interpretation: There is a dependency between work accidents and leaving the company.

non-technical interpretation: Employees that did not have a work accident are more than 3 times more likely to leave.

TEST 2) Left, promotion_last_5years

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

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

chi-square test interpretation: There is a dependency between promotions in the last 5 years and leaving the company.

non-technical interpretation: Employees who did not receive a promotion in the last 5 years are 4 times more likely to leave the company.

TEST 3) Left, Department

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

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

chi-square test interpretation: There is a dependency between departments and leaving the company.

non-technical interpretation: Employees in management and RandD are more likely to remain at the company.

TEST 4) Left, Salary

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

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

chi-square test interpretation: There is a dependency between salary and leaving the company.

non-technical interpretation: Employees with a low salary are over 4 times more likely to leave than employees with a high salary.