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Linear regression is a standard tool for numerical prediction, but there are several estimators to choose from, and this choice can greatly affect prediction accuracy. This thesis empirically explores how the out-of-sample predictive performance of ordinary least squares (OLS), ridge regression (Ridge), and the lasso (Lasso) is affected by varying key model conditions, given a baseline setting. Fo
