1. Satisfaction level

t.test(hr_renamed$satisfaction_level ~ hr_renamed$left)
## 
##  Welch Two Sample t-test
## 
## data:  hr_renamed$satisfaction_level by hr_renamed$left
## t = -46.636, df = 5167, p-value < 2.2e-16
## alternative hypothesis: true difference in means between group Left and group Stayed is not equal to 0
## 95 percent confidence interval:
##  -0.2362417 -0.2171815
## sample estimates:
##   mean in group Left mean in group Stayed 
##            0.4400980            0.6668096

Technical Interpretation: We can reject Ho because the p value is smaller than the alpha (.01) which means the difference in satisfaction_level between those that left vs. stayed is statistically significant.

Non-Technical Interpretation: Employees with lower satisfaction are more likely to leave.

2. Last Evaluation

t.test(hr_renamed$last_evaluation ~ hr_renamed$left)
## 
##  Welch Two Sample t-test
## 
## data:  hr_renamed$last_evaluation by hr_renamed$left
## t = 0.72534, df = 5154.9, p-value = 0.4683
## alternative hypothesis: true difference in means between group Left and group Stayed is not equal to 0
## 95 percent confidence interval:
##  -0.004493874  0.009772224
## sample estimates:
##   mean in group Left mean in group Stayed 
##            0.7181126            0.7154734

Technical Interpretation: We cannot reject Ho because the p value is significantly larger than the alpha (.01) which means the difference in last_evaluation between those that left vs. stayed is not statistically significant.

Non-Technical Interpretation: Last evaluation does not affect employment status

3. Average Monthly Hours

t.test(hr_renamed$average_monthly_hours ~ hr_renamed$left)
## 
##  Welch Two Sample t-test
## 
## data:  hr_renamed$average_monthly_hours by hr_renamed$left
## t = 7.5323, df = 4875.1, p-value = 5.907e-14
## alternative hypothesis: true difference in means between group Left and group Stayed is not equal to 0
## 95 percent confidence interval:
##   6.183384 10.534631
## sample estimates:
##   mean in group Left mean in group Stayed 
##             207.4192             199.0602

Technical Interpretation: We can reject Ho because the p value is smaller than the alpha (.01) which means the difference in average_monthly_hours between those that left vs. stayed is statistically significant.

Non-Technical Interpretation: Employees that have very high or very low monthly hours are more likely to leave. Those whose hours are closer to 200 per month are more likely to stay.

4. Number of Projects

t.test(hr_renamed$number_project ~ hr_renamed$left)
## 
##  Welch Two Sample t-test
## 
## data:  hr_renamed$number_project by hr_renamed$left
## t = 2.1663, df = 4236.5, p-value = 0.03034
## alternative hypothesis: true difference in means between group Left and group Stayed is not equal to 0
## 95 percent confidence interval:
##  0.006540119 0.131136535
## sample estimates:
##   mean in group Left mean in group Stayed 
##             3.855503             3.786664

Technical Interpretation: We cannot reject Ho because the p value is larger than the alpha (.01) which means the difference in the number of projects between those that left vs. stayed is not statistically significant.

Non-Technical Interpretation: Number of Projects does not effect employment status.