Q1 -> Corrolation between current Employment
Status and Satisfaction Level
##
## Welch Two Sample t-test
##
## data: hr$satisfaction_level by hr$Employee_Status
## 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
Conclusion: there is a direct link between satisfaction
level and employee status, as employee who choose to stay average a 0.28
better score than those who left
Q2 -> Corrolation between current Employment
Status and Average Monthly Hours
##
## Welch Two Sample t-test
##
## data: hr$average_montly_hours by hr$Employee_Status
## 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
Conclusion: on average, employees who have left the company
worked ~8 more hours a week, bring about a small correlation between the
factors
Q3 -> Corrolation between current Employment
Status and Last Evaluation
##
## Welch Two Sample t-test
##
## data: hr$last_evaluation by hr$Employee_Status
## 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
Conclusion: looking to the very high p-score of 0.4683, the
employees last evaluation has no effect on their decision to leave for
stay at the company