1. Chi-Square Test of Left with Salary
##
## Pearson's Chi-squared test
##
## data: hr$left and hr$salary
## X-squared = 381.23, df = 2, p-value < 2.2e-16
Technical Interpretation
The p-value is very small (p < 0.001). Therefore, we reject the
null hypothesis and conclude that there is a statistically significant
relationship between salary level and employee attrition. This indicates
that salary and whether an employee leaves the company are not
independent.
Non-Technical Interpretation
Employees with lower salaries are more likely to leave the company
compared to employees with medium or high salaries.
2. Chi-Square Test of Left with Department
##
## Pearson's Chi-squared test
##
## data: hr$left and hr$Department
## X-squared = 86.825, df = 9, p-value = 7.042e-15
Technical Interpretation
The p-value is less than 0.001, indicating that we reject the null
hypothesis. There is a statistically significant relationship between
department and employee attrition, meaning that attrition rates vary
across departments.
Non-Technical Interpretation
Some departments have higher employee turnover than others, meaning
employees in certain departments are more likely to leave the
company.
4. Chi-Square Test of Left with 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
Technical Interpretation
The p-value is less than 0.001, indicating that we reject the null
hypothesis. There is a statistically significant relationship between
experiencing a work accident and employee attrition.
Non-Technical Interpretation
Employees who have experienced a work accident are less likely to
leave the company compared to those who have not had a work
accident.