Chi-square test 1
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## Pearson's Chi-squared test with Yates' continuity correction
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## data: hr$left and hr$Work_accident
## 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
occurance of a work accident and whether the individual left.
non-technical interpretation: People are less likely to leave if
they didn’t have an work accident
Chi-square test 2
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## Pearson's Chi-squared test with Yates' continuity correction
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## data: hr$left and hr$promotion_last_5years
## 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 a
promotion in the last 5 years and whether the individual left.
non-technical interpretation: People who were promoted in the last 5
years are less likely to leave.
Chi-square test 3
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## Pearson's Chi-squared test
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## data: hr$left and hr$Department
## 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
individual’s department and whether the individual left.
non-technical interpretation: Most departments have different
amounts of people that left.
Chi-square test 4
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## Pearson's Chi-squared test
##
## data: hr$left and hr$salary
## 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 salary
and whether the individual left.
non-technical interpretation: The lower one’s salary is the more
likely they are to leave.