Advanced Regression Models with R Applications

October 5, 2019


In this workshop, we will talk about a variety of regression models, give their definitions, discussing goodness-of-fit criteria, presenting fitted models, interpreting estimated regression coefficients, and using the fitted models for prediction. The models will be limited to: linear regression, Box-Cox transformation, gamma regression, ordinary logistic regression, Poisson regression, beta regression, longitudinal (repeated measures) regression, and hierarchical model.

The workshop is designed to be hands-on. Participants are required to bring laptops and be ready to write R, analyzing data and interpreting results. For each model, we present an example with a complete R code, and then will an exercise to work on. Workshop participants should be familiar with algebraic expressions of different probability distributions and have a fundamental knowledge of simple linear regression: normally distributed random error, continuous and categorical independent variables (requiring creating dummy variables).

The material covered by the workshop will be taken from my recently published book “Advanced Regression Models with SAS and R Applications”, CRC Press, 2018.

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