**Learning objectives**

- define generalized linear models (GLM)
- define linear and logistic regression as special cases of GLMs
- distinguish between additive and multiplicative models
- define Pearson and deviance residuals
- describe application of the Wald test

**Outline**

- Brief overview of multiple regression (Vittinghoff 4.1-4.3)
- Linear Regression as a GLM (Vittinghoff 4.1-4.3)
- Logistic Regression as a GLM (Vittinghoff 5.1-5.3)
- Statistical inference for logistic regression (Vittinghoff 5.1-5.3)

**Learning Objectives**

- Define “tidy” data
- Load a dataset in R and perform basic exploratory data analysis
- Create descriptive “Table 1” of a study sample using the
`tableone`

package - Create a customized bar plot using the
`ggplot2`

package

**Exercises**

- Load the contraceptive use dataset into R
- Create an “Epi Table 1” of sample characteristics
- Create a barplot stratified by age and showing the relative proportions of participants using contraceptives among those who do and do not want more children.
- Repeat the barplot showing percentages instead of counts