1. Chi-square Test between Satisfaction Level and Leaving

## 
##  Pearson's Chi-squared test
## 
## data:  hr$satisfaction_level and hr$left
## X-squared = 7937.7, df = 91, p-value < 2.2e-16

p-value interpretation: The p-value is less than 2.2e-16, which is extremely small. This means there’s an almost zero chance that the observed association is due to random variation.

chi-square test interpretation: There is a very strong, statistically significant association between employee satisfaction level and whether they left the company.

non-technical interpretation: Employees with lower satisfaction levels are much more likely to leave the company.

2. Chi-square Test between Last Evalutation Level and Leaving

## 
##  Pearson's Chi-squared test
## 
## data:  hr$last_evaluation and hr$left
## X-squared = 2534.8, df = 64, p-value < 2.2e-16

p-value interpretation: The p-value is extremely small < 2.2e-16, which means there’s very strong evidence against the null hypothesis. This suggests that the observed results are highly unlikely to be due to random chance.

chi-square test interpretation: There is a statistically significant association between the employee’s last evaluation score and whether they left the company.

non-technical interpretation: Employees with lower evaluations are more likely to leave.

3. Chi-square Test between Time Spent at Company and Leaving

## 
##  Pearson's Chi-squared test
## 
## data:  hr$time_spend_company and hr$left
## X-squared = 2110.1, df = 7, p-value < 2.2e-16

p-value interpretation: The p-value is extremely small < 2.2e-16, indicating very strong evidence against the null hypothesis. The probability of the results occurring by chance is nearly zero.

chi-square test interpretation: There is a statistically significant association between the number of years employees have spent at the company and whether they left the company.

non-technical interpretation: Employees who have been with the company for fewer years are more likely to leave, while those who have been there longer tend to stay.

4. Chi-square Test between Number of Projects and Leaving

## 
##  Pearson's Chi-squared test
## 
## data:  hr$number_project and hr$left
## X-squared = 5373.6, df = 5, p-value < 2.2e-16

p-value interpretation: The p-value is extremely small < 2.2e-16, which indicates strong evidence against the null hypothesis. This suggests that the observed relationship is very unlikely to be due to random chance.

chi-square test interpretation: There is a statistically significant association between the number of projects an employee has worked on and whether they left the company.

non-technical interpretation: Employees who work on fewer projects are more likely to leave the company, while those working on more projects tend to stay.