Practice with ioslides

Sure! Here's a concise 4-sentence summary: Linear model selection involves identifying a subset of predictors that best explains the response variable, balancing model complexity and predictive accuracy. Methods like best subset selection, forward selection, and backward elimination help choose the most relevant variables. Regularization techniques such as ridge regression and the lasso improve model performance by introducing a penalty on the size of coefficients to reduce overfitting. While ridge shrinks all coefficients toward zero, the lasso can force some to be exactly zero, thus performing variable selection as well.

DATA 622 Assignment 3

Discusses Simple Linear Regression and demonstrates its use in R.

Do problems 7.2 and 7.5 in Kuhn and Johnson. There are only two but they have many parts. Please submit both a link to your Rpubs and the .rmd file.