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This edition: Multiple Regression: Refining the Model

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Episode Details

This lesson further examines multiple regression by closely examining the predictor variables involved in the regression model. Researchers often want to simplify a multiple regression to focus on just a few variables, or even on a single variable. There are several reasons why they might do this. One is that they may not have the resources to continuously measure all of the explanatory variables. Or they may want to focus on one or two explanatory variables that are the best predictors of the response variable. In this lesson, you will learn how researchers determine which predictor(s) to keep and which ones to discard as they build a more refined model.
When statisticians refine multiple regression models, they use the t statistical test to evaluate the individual slope coefficients. This is a test with which you are already quite familiar. In this lesson, you’ll learn how the t test gives researchers a way to identify which predictors to keep and which to remove from the regression model. The lesson features a real life example of how scientists use these refinement techniques to keep the electric power grid going and also to predict how many solar panels customers need to produce their own electricity. Remember that the purpose of multiple regression is to accurately predict the value of a response variable based on the value of one or more explanatory, or predictor, variables. In this lesson, you will also learn how to apply the principles of inference to predicted values.

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