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## Pearson's Chi-squared test with Yates' continuity correction
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## data: hr$promotion_last_5years and hr$left
## X-squared = 56.262, df = 1, p-value = 6.344e-14p-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 dependency between the promotion in the last 5 years and leaving.
non-technical interpretation: Employees that did not get a promotion are more likely to leave
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## Pearson's Chi-squared test
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## data: hr$Department and hr$left
## X-squared = 86.825, df = 9, p-value = 7.042e-15p-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 dependency between the department and leaving.
non-technical interpretation: Depending on the department, employees may leave more or less.
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## Pearson's Chi-squared test
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## data: hr$salary and hr$left
## X-squared = 381.23, df = 2, p-value < 2.2e-16p-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 dependency between the salary and leaving.
non-technical interpretation: If your salary is low, you are the most likely to leave the company.
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## Pearson's Chi-squared test with Yates' continuity correction
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## data: hr$Work_accident and hr$left
## X-squared = 357.56, df = 1, p-value < 2.2e-16p-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 dependency between if the employee had a work accident and leaving.
non-technical interpretation: If you have haven’t had a work accident, you are 3.375x more likely to leave.