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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 extremely small, indicating that the probability of these results being random is very small.
chi-square test interpretation: There is a strong dependence between work accidents and employee attrition.
non-technical interpretation: Employees who have had a work accident are less likely to leave the company.
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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, indicating that the probability of these results being random is very small.
chi-square test interpretation: There is a dependence between receiving a promotion and whether an employee leaves.
non-technical interpretation: Employees who were promoted in the last 5 years are much less likely to leave.
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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 extremely small, suggesting very strong evidence that the probability of these results being random is very small.
chi-square test interpretation: There is a strong dependence between salary level and whether employees leave.
non-technical interpretation: Employees with lower salaries are more likely to leave the company.
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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, indicating strong evidence that the probability of these results being random is very small.
chi-square test interpretation: There is a dependence between department and whether employees leave.
non-technical interpretation: Some departments experience higher employee turnover than others.