Multiple linear regression For a homework we have to determine the effect of a predictor variable on an outcome variable using simple linear regression. We have lots of data (about 300 variables) and we may include some other covariates in our regression model. Why would we include other covariates and how do you decide which of those 300 variables we want to include in our regression model?

There are many approaches to variable selection. First and foremost, you need to determine if each variable even makes sense for your question. For example, would you expect height to be related to the height of their in-laws? No...but it could be thrown into the variable mix anyway. First step is to use common sense to get an idea of what variables seem important, then you can get systematic Below are a couple major approaches:
-Stepwise regression (forward, backwards, etc)
-Lasso and ridge regression
