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## Pearson's Chi-squared test with Yates' continuity correction
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## data: hr1$Work_accident and hr1$left
## X-squared = 357.56, df = 1, p-value < 2.2e-16We reject the Ho as the p-value is < alpha (0.001) There is a dependence between work accident and employees that left vs. stayed
Employees who 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: hr1$promotion_last_5years and hr1$left
## X-squared = 56.262, df = 1, p-value = 6.344e-14We reject the Ho as the p-value is < alpha (0.001) There is a dependence between promotion in the last 5 years and employees that left vs. stayed
Employees who were promoted in the last 5 years are less likely to leave the company.
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## Pearson's Chi-squared test
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## data: hr1$salary and hr1$left
## X-squared = 381.23, df = 2, p-value < 2.2e-16We reject the Ho as the p-value is < alpha (0.001) There is a dependence between salary level and employees that left vs. stayed
Employees with higher salaries are less likely to leave the company.
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## Pearson's Chi-squared test
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## data: hr1$number_project and hr1$left
## X-squared = 5373.6, df = 5, p-value < 2.2e-16We reject the Ho as the p-value is < alpha (0.001) There is a dependence between number of projects and employees that left vs. stayed
Employees with a very high or very low number of projects are more likely to leave the company.