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

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

chi-square test interpretation: There is a dependence between the work accident and the chance to leave.

non-technical interpretation: People with work accidents are less likely to leave.

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

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

chi-square test interpretation: There is a dependence between the promotion in last 5 years and the chance to leave.

non-technical interpretation: People with promotions are less likely to leave.

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

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

chi-square test interpretation: There is a dependence between the salary and the chance to leave.

non-technical interpretation: People with higher salaries are less likely to leave.

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

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

chi-square test interpretation: There is a dependence between the department and the chance to leave.

non-technical interpretation: People in certain departments leave more often than others.