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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-15
Technical: The chi-square test yields a p-value of 7.042e-15, which is far below 0.05, indicating a statistically significant association between Department and left (X-squared = 86.825, df = 9).
Non-technical: Employees in some departments are much more likely to leave the company than those in others, showing that the department they work in plays a big role in whether they stay or go.
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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-16
Technical: With a p-value less than 2.2e-16 and a chi-square statistic of 381.23 (df = 2), there is a highly significant relationship between salary and left, rejecting the null hypothesis of independence.
Non-technical: Employees earning lower salaries are more likely to leave the company, while those with higher salaries tend to stay longer.
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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-16
Technical: The chi-square test with Yates’ continuity correction gives a p-value less than 2.2e-16 and an X-squared value of 357.56 (df = 1), showing a strong significant association between Work_accident and left.
Non-technical: Employees who have had a work accident are less likely to leave the company compared to those who haven’t, suggesting accidents might not push people to quit.
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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-14
Technical: The chi-square test with Yates’ correction results in a p-value of 6.344e-14 and an X-squared value of 56.262 (df = 1), indicating a significant relationship between promotion_last_5years and left.
Non-technical: Employees who got a promotion in the last 5 years are less likely to leave the company, meaning promotions might help keep people around.