This project undertakes a comprehensive economic analysis, beginning with a multiple ordinary least squares (OLS) regression model to predict CEO salaries, incorporating variables such as return on assets, firm size, volatility, CEO age, gender, and board independence. The model's performance is evaluated through R-squared, adjusted R-squared, ANOVA, and parameter estimates. The second part of the project delves into commodity price analysis, focusing on wheat, sugar, and chicken prices from 2019 to 2021. It examines weak-form efficiency and utilizes descriptive statistics, including histograms and time series plots, to understand price behavior. Furthermore, the project employs an autoregressive (AR) model to forecast future price movements, providing model statistics and interpretations for each commodity.