How have total earnings across departments changed over time? Are some departments showing much higher growth than others?
– I will use time series analysis to look at trends in total earnings for each department over the years. Visualizations like line charts will help compare growth rates across departments. And statistical techniques like calculating the coefficient of variation will quantify the amount of variation in earnings changes over time. This can identify outliers with especially high or low growth compared to other departments.
What insights can statistical modeling reveal about key drivers of overtime pay? How do factors like job type and years of experience correlate with overtime?
– Using regression modeling, I can analyze how different variables like job category, department, years of service etc. influence overtime pay. Multiple linear regression will estimate the correlation between each independent variable (predictors like job type and experience) and the dependent overtime pay variable. Significant coefficients will reveal which factors have the biggest influence on overtime earnings.
Can clustering algorithms identify groups of departments with similar compensation patterns that may inform salary standardization policies?
– Yes, clustering algorithms like k-means can group departments together based on similarities in earnings data across fields like average base salary, ratio of overtime to base pay, changes over time, etc. Seeing which departments end up clustered can highlight which ones share common compensation trends. Policymakers could use this information to make data-driven decisions when standardizing salaries and pay scales across the city government.