Comparison of Forward Selection and Backward Elimination Regression Techniques

This technical report compares two regression techniques: forward selection and backward elimination. Both methods aim to identify significant predictor variables impacting a response variable. Forward selection starts with an empty model, adding predictor variables iteratively based on their p-values, while backward elimination begins with all predictor variables, removing them based on the largest p-values. The report details the steps involved in each technique, including the criteria for adding or removing variables, and provides example data to illustrate the processes. It further compares the final models generated by each technique, highlighting the differences in the number of predictor variables retained. The report concludes with a summary of the results from both methods, indicating that despite differing numbers of predictors, both models effectively reflect the actual results. This document serves as a guide for understanding these regression techniques and their applications in modeling.