**Learning objectives**

- identify systematic and random components of a multiple linear regression model
- define terminology used in a multiple linear regression model
- define and explain the use of dummy variables
- interpret multiple linear regression coefficients for continuous and categorical variables
- use model formulae to multiple linear models
- define and interpret interactions between variables
- interpret ANOVA tables

**Outline**

- multiple regression terminology and notation
- continuous & categorical predictors
- interactions
- ANOVA tables
- Model formulae

**Learning objectives**

- Load a tab-separated dataset into R
- Create a simple scatterplot using
`ggplot2`

- Fit and analyze a multiple linear regression model
- Compare two nested models using a nested Analysis of Variance (partial F test)

**Exercises**

- Load the
`cholesterol.tsv`

dataset into R - Fit linear models with age, state, and interaction terms as predictors
- Compare a simple linear regression to a multiple linear regression using Analysis of Variance partial F-test
- Use backwards selection from a full model with interactions to choose the best prediction model