Question: Share a one-page summary of your project. (500 words or fewer) In your summary, include a brief description of the context and the dependent variable of interest. Make an argument for the viability of your model. Aspects of your argument may be

### 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 Changers (employees who switched jobs), Job Stayers (employees who remained in the same position), Age, Industry, and Gender. With a specific focus on the dependent variable—median pay change—this research aims to illuminate the trends and dynamics influencing compensation growth in the contemporary labor market. ### Context and Dependent Variable The context of this analysis lies in the increasingly dynamic nature of employment, where employees frequently weigh the benefits of changing jobs against the security of staying in the same position. The core dependent variable is the change in median salary, measured year-over-year, which serves as a financial benchmark for evaluating the efficacy of job mobility compared to job retention. ### Model Viability The results yielded a p-value of 1.27553E-16, demonstrating that the independent variables significantly impact the median pay change, reinforcing the robustness of our model. The R-squared value of 0.795 indicates that approximately 80% of the variability in median salary change can be explained by the included independent variables. These statistical details point to the effectiveness of the model in capturing the factors that influence salary changes. Moreover, the regression analysis revealed that Job Changers exhibited a notably higher y-intercept, indicating that they experience significantly greater median pay increases than Job Stayers. This finding emphasizes the potential benefits of job mobility, reinforcing the assertion that switching jobs may lead to more substantial financial rewards. ### Linear Relationships and Nonlinearities The relationships between several independent variables and salary changes exhibited consistency, highlighting the linear nature of the associations. However, some variables demonstrated multicollinearity—such as Age and Industry—showing a correlation coefficient exceeding 0.8. This multicollinearity suggests that while these variables may contribute to median pay changes, their interdependencies may obfuscate the individual effects. Careful consideration of these relationships is crucial for stakeholders aiming to navigate compensation decisions. ### Excluded Drivers and Their Impacts While the model effectively captured many significant predictors of salary change, some potentially relevant attributes were not included. Factors such as education level, duration of employment in current job, geographic location, and economic conditions (e.g., inflation rates) could provide further insights into the dynamics of salary change. The exclusion of these variables may limit the comprehensiveness of the analysis and introduce biases. Data availability was a significant constraint, as obtaining detailed information on these excluded variables may have been impractical. Nevertheless, including them could enrich the model and provide stakeholders with a more nuanced understanding of the drivers behind compensation dynamics. ### Conclusion In summary, this analysis demonstrates that job changers generally experience greater median pay increases compared to job stayers. The strong statistical significance and explanatory power of the model support the finding, although considerations around multicollinearity and the exclusion of certain relevant variables warrant further investigation. Stakeholders can leverage these findings when making informed decisions about employment strategies in pursuit of optimal salary outcomes.