1.

## 
##  Welch Two Sample t-test
## 
## data:  hr1$average_montly_hours by hr1$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

P-value Interpretation: Given the small P value, there is a significant difference in the means.
Technical Terms: People that left, worked significantly more hours on average, at least 6 more hours (3% more).
Non-technical Terms: People that leave, work a little bit more than people that don’t leave. For retention, the company can simply have people that work more houes reduce their hours by 3%.

2.

## 
##  Welch Two Sample t-test
## 
## data:  hr1$last_evaluation by hr1$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

P-value Interpretation The P value is very large, which means there is not a significant difference in the means.
Technical Terms: The p-value of 0.4683 suggests that an employee’s last evaluation score has no statistically significant relationship with whether they left the company or not.
Non-technical Terms: A persons last evaluation had no relation to if an employee left the company or not. An employees retention is not dependent on their last evaluation.

3.

## 
##  Welch Two Sample t-test
## 
## data:  hr1$satisfaction_level by hr1$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

P-value Interpretation: Given the small P value, there is a significant difference in the means.
Technical Terms: The company would need to increase satisfaction levels by approximately 51.5% for employees who left to reach the same level of satisfaction as those who stayed.
Non-technical Terms: People that left, were significantly less satisfied than those that stayed. For retention, the company needs to increase satisfaction levels.

4.

## 
##  Welch Two Sample t-test
## 
## data:  hr1$number_project by hr1$Employee_Status
## 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

P-value Interpretation: Given the P value, there is not a significant difference in the means.

Technical Terms: The p-value of 0.03034 suggests that an employee’s number of projects has no statistically significant relationship with whether they left the company or not.

Non-technical Terms: Doesn’t matter how many projects an employee had, retention is not dependent on a person’s number of projects.