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Topics

The course consists of 14 chapters organized in two parts. In the first part we discussed the normal and binomial distributions and the theory of generalized linear models while the second part is focused on Poisson distribution, loglinear models and other distributions belong to the exponential family. All the examples are illustrated using R and the code is available in the R program of the chapter.

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Useful R functions include:

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  • glm()

 

The course in slides format

The slides of the course are available online in both PowerPoint and PDF formats.

Part 1
 
The following chapters are covered in part 1:

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  • Analysis of Variance

  • Linear regression models with normal error.

  • Generalized linear models.

  • Exponential Family

  • Generalized linear model function in R

  • Models for Binary data.

  • Estimation and confidence intervals.

  • Inference.

  • Model Selection.

  • Model diagnostic.

Part 2

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The second part of the course consists of the following chapters:

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  • Poisson Regression.

  • Beyond Poisson and binomial distributions: models with different link  functions and/or distributions.

  • Poisson regression and log linear models.

  • Over dispersion.

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