Question 1
##
## 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
p-value interpretation: The small p-value indicates that the
relationship between promotions and attrition is unlikely due to
chance.
chi - squared interpretation test: There is a significant dependency
between receiving a promotion and the likelihood of leaving.
non-technical interpreation test: Employees without promotions in
the last five years are more likely to leave the company.
Question 2
##
## 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
p-value interpretation: The small p-value indicates that the
relationship between work accident and attrition is unlikely due to
chance.
chi - squared interpretation test: There is a significant dependency
between work accident and the likelihood of leaving.
non-technical interpreation test: Employees without work accidents
are less likely to leave the company.
Question 3
##
## Pearson's Chi-squared test
##
## data: hr$Department and hr$left
## X-squared = 86.825, df = 9, p-value = 7.042e-15
p-value interpretation: The small p-value indicates that the
relationship between dependancy and attrition is unlikely due to
chance.
chi - squared interpretation test: There is a small dependency
between department and the likelihood of leaving.
non-technical interpreation test: Employees in management are least
likely to leave the company
Question 4
##
## Pearson's Chi-squared test
##
## data: hr$salary and hr$left
## X-squared = 381.23, df = 2, p-value < 2.2e-16
p-value interpretation: The small p-value indicates that the
relationship between salary and attrition is unlikely due to
chance.
chi - squared interpretation test: There is a significant dependency
between salary and the likelihood of leaving.
non-technical interpreation test: Employees with a low salary are
more likely to leave the company.