# Modeling

## 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