### Project Summary The primary objective of this project was to analyze the impact of job switching versus job retention on year-over-year median salary changes. The study utilized a dataset containing several independent variables, including Job Chan

### Project Summary This project aimed to analyze the impact of job switching versus job retention on year-over-year median salary changes. Utilizing a dataset with various independent variables—Job Changers (employees who switched jobs), Job Stayers (those who did not), Age, Industry, and Gender—the research focused on the dependent variable of median pay change. The goal was to elucidate trends affecting compensation growth in today’s labor market. ### Context and Dependent Variable The analysis highlights the contemporary employment landscape, where employees often weigh the benefits of changing jobs against the security of remaining in the same position. The key dependent variable is the change in median salary, assessed year-over-year, providing a benchmark for evaluating job mobility versus job retention. ### Model Viability The results revealed a p-value of 1.27553E-16, indicating significant impacts of independent variables on median pay change. An R-squared value of 0.795 suggests that approximately 80% of the variability in salary change can be explained by these variables, reinforcing the model's robustness. Regression analysis indicated that Job Changers experienced significantly higher median pay increases than Job Stayers, suggesting that job mobility can yield substantial financial rewards. ### Linear Relationships and Nonlinearities The analysis identified mostly linear relationships between independent variables and salary changes. However, some variables exhibited multicollinearity, particularly Age and Industry, with a correlation coefficient exceeding 0.8. This multicollinearity complicates the assessment of individual variable effects, necessitating careful consideration for stakeholders navigating compensation strategies. ### Excluded Drivers and Their Impacts While the model captured significant predictors, certain relevant factors were excluded. Attributes such as education level, tenure in current jobs, geographic location, and economic conditions (e.g., inflation rates) could offer further insights into salary dynamics. The exclusion of these variables may limit the analysis's comprehensiveness and introduce biases. Data availability was a constraint, as obtaining detailed information on these factors proved impractical. ### Conclusion In conclusion, the analysis reveals that Job Changers generally see greater median pay increases than Job Stayers. The model's strong statistical significance supports this finding, though considerations around multicollinearity and excluded variables suggest the need for future research. Stakeholders can utilize these insights to inform employment strategies aimed at optimizing salary outcomes.