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Course Structure and Topics

This course combines lectures on theory and concepts with significant time practicing statistical tools within the R environment. The course concludes with a 2-day project session where participants work independently to conduct a full analysis of a provided dataset and present their results. Note: Schedule does not include time for consulting about participants’ own projects.

This course covers:

  • Introduction to probability theory, random variables, and statistical distributions
  • Linear models (univariate, multivariate)
  • Generalized linear models (Poisson, quasi-Poisson, negative binomial, and binomial)
  • Testing for model assumptions and model fit
  • Proper use of data transformations to improve model performance and fulfill assumptions
  • Evaluating model performance with diagnostic tools
  • Preparing quality graphics and interpreting results
  • Dealing with common problems in data including missing data and collinearity
  • Tips and tricks of programming in R
  • Organizing analyses into data exploration, descriptive statistics, model application, model diagnostics, and discussion of results.

More advanced topics, including linear mixed models may be covered depending on available time.

Software and Programs

All exercises and some lectures will be performed in R, through user-friendly interface RStudio. Pre-course work will be required for all participants not familiar with the program R.

Required exercises for R preparation work will be emailed to participants at least one month prior to the course. All participants must have a basic familiarity working in the R environment.

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