## R Modeling

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**Linear regression, logistic regression**

- Multiple linear regression with r
- Interpreting regression coefficients; finding a parsimonious model

**Generalized linear models**

- Logistic regression with r
- Multiple regression and logistic regression as special cases of the generalized linear model
- The need for a different model when the response variable is binary, the logistic transform and fitting the model to some simple examples, deviance residuals
- The Poisson model for count data.
- The problem of overdispersion

**Analyzing longitudinal data using r**

- Examples of longitudinal data
- Mixed-effects models for longitudinal data
- Simple graphics for longitudinal data and simple inference using the summary measure approach
- The ‘long-form’ of longitudinal data

**Generalized estimating equations**

- Modeling the correlational structure of the repeated measurements
- The dropout problem
- The generalized estimating equation approach for non-normal response variables in longitudinal data