The article discusses how to choose independent variables in linear regression models. It covers different methods like stepwise regression and polynomial regression. The researchers also talk about dealing with multicollinearity using techniques like ridge regression and principal component regression. They explain how to select the right variables for a good regression model. The study shows that the principal components method can be a good alternative to ridge regression for handling multicollinearity.