Multiple Linear Regression in R: Choosing Predictors with Best Subset Selection

In this video, I demonstrate how to perform a multiple linear regression in R using the Best Subset Selection (BSS) method. We will use the regsubsets() function from the leaps package to identify the combination of predictors that best explains the variation in the response variable, Balance. You will learn: How to apply regsubsets() to evaluate all possible subsets of predictors. How to compare models using Adjusted R² and Residual Standard Error (RSE). How to determine the optimal number of predictors to include in the final model. How to extract the coefficients of the best-fitting model. After watching this video, you will have a basic understanding of the theory behind Best Subset Selection as well as the skills to implement this method in R and apply it to your own datasets. Here is a link to the R notebook I used in this video: https://drive.google.com/file/d/1McMj...