Although we had already touched on linear regression in previous classes, this class drove home what linear regression is, some of the math behind it, and what it could be used for. Fittingly, the final project focused on using linear regression to analyze any fitting data set. My group selected a data set of house sales, seeking to determine how can we predict house prices. We constructed a linear regression model through trial and error, utilizing the best subsets method. In the end, we determined that the best model included the predictor variables square footage, number of bedrooms, number of bathrooms, age of house, number of garages, and quality of house. The strength of our model’s predictive power was confirmed by internal and external validation techniques.