Chapter 7: Multiple Linear Regression

This set of labs introduces the multiple linear regression model, both in the context of an inferential model and a predictive/explanatory model.
Lab Notes

1. Introduction to Multiple Regression

Introduces the multiple regression model in the context of estimating an association between a response variable and primary predictor of interest while adjusting for possible confounding variables.

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2. Evaluating Model Fit

Discusses the use of residual plots to check assumptions for multiple regression and introduces adjusted R2.

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3. Categorical Predictors with Several Levels and Inference in Regression

Extends on the topics introduced in Chapter 6, Lab 4 by discussing categorical predictors with more than two levels and generalizing inference in regression to the setting where there are several slope parameters.

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4. Interaction

Introduces the concept of a statistical interaction, specifically in the case of an interaction between a categorical variable and a numerical variable.

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5. Model Selection for Explanatory Models

Discusses explanatory modeling, in which the goal is to construct a model that explains the observed variation in the response variable. This is an application of multiple regression distinct from that presented in Lab 1.

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R (Base), Useful Links

The statistical software R is a widely used and stable software that is free. RStudio is a user-friendly interface for R.