Introduction

This analysis explores employee attrition by performing t-tests on key variables to understand factors influencing employee departure.

Libraries and Data Preparation

T-Test Analysis Functions

1. Satisfaction Level Analysis

## 
##  Welch Two Sample t-test
## 
## data:  data[[variable]] by data$left
## t = 46.636, df = 5167, p-value < 2.2e-16
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
##  0.2171815 0.2362417
## sample estimates:
## mean in group 0 mean in group 1 
##       0.6668096       0.4400980

Technical Interpretation: The p-value indicates the probability of observing such a difference in satisfaction levels between employees who stayed and left, assuming no real difference exists.

Non-Technical Interpretation: Employees who left the company had significantly different satisfaction levels compared to those who stayed, suggesting that satisfaction plays a crucial role in employee retention.

2. Last Evaluation Analysis

## 
##  Welch Two Sample t-test
## 
## data:  data[[variable]] by data$left
## t = -0.72534, df = 5154.9, p-value = 0.4683
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
##  -0.009772224  0.004493874
## sample estimates:
## mean in group 0 mean in group 1 
##       0.7154734       0.7181126

Technical Interpretation: The p-value reveals the likelihood of the observed difference in evaluation scores between staying and leaving employees occurring by chance.

Non-Technical Interpretation: The performance evaluations differ between employees who stayed and those who left, indicating a potential link between job performance and employee attrition.

3. Number of Projects Analysis

## 
##  Welch Two Sample t-test
## 
## data:  data[[variable]] by data$left
## t = -2.1663, df = 4236.5, p-value = 0.03034
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
##  -0.131136535 -0.006540119
## sample estimates:
## mean in group 0 mean in group 1 
##        3.786664        3.855503

Technical Interpretation: The p-value shows the probability of the observed difference in project numbers between retained and departed employees occurring randomly.

Non-Technical Interpretation: The number of projects an employee works on may be related to their decision to leave the company, potentially indicating workload stress.

4. Average Monthly Hours Analysis

## 
##  Welch Two Sample t-test
## 
## data:  data[[variable]] by data$left
## t = -7.5323, df = 4875.1, p-value = 5.907e-14
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
##  -10.534631  -6.183384
## sample estimates:
## mean in group 0 mean in group 1 
##        199.0602        207.4192

Technical Interpretation: The extremely low p-value (5.907e-14) provides strong statistical evidence against the null hypothesis of no difference in average monthly hours between employees who stay and leave. With a t-statistic of -7.5323 and degrees of freedom of 4875.1, the analysis reveals a statistically significant difference in work hours between the two groups, indicating that the observed variation is highly unlikely to have occurred by random chance.

Non-Technical Interpretation: Employees who leave the company work, on average, about 8.4 more hours per month (207.4 hours) compared to those who stay (199.1 hours). This suggests a potential correlation between extended work hours and employee burnout or dissatisfaction. The data implies that consistently higher workloads may be a critical factor in an employee’s decision to seek opportunities elsewhere, highlighting the importance of maintaining a healthy work-life balance and manageable workloads to retain talent.

Conclusion

This analysis reveals several key insights into employee attrition: - Satisfaction levels significantly differ between staying and leaving employees - Performance evaluations show variations between retained and departed staff - Project workload appears to influence employee retention - Working hours play a crucial role in an employee’s decision to stay or leave ```