Page 119 - Statistics II for Dummies

Chapter 6

How Can I Miss You If

You Won’t Leave? Regression

Model Selection

In This Chapter
▶ Evaluating different methods for choosing a multiple regression model
▶ Understanding how forward selection and backward selection work
▶ Using the best subsets methods to find a good model

uppose you’re trying to estimate some quantitative variable, y, and you
Shave many x variables available at your disposal. You have so many
variables related to y, in fact, that you feel like I do in my job every day —
overwhelmed with opportunity. Where do you go? What do you do? Never
fear, this chapter is for you.

In this chapter, you uncover criteria for determining when a model fits well.
I discuss different model selection procedures and all the details of the most
statistician-approved method for selecting the best model. Plus, you get to
find out what factors come into play when a punter kicks a football. (You can
think about that while you’re reading.)

Note that the term best has many connotations here. You can’t find one end-
all-be-all model that everyone comes up with in the end. That’s to say that
each data analyst can come up with a different model, and each model still
could do a good job of predicting y.

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