The R statistics bootcamp is a 30-hour hands-on course on statistical methods for people with a basic statistics backround as well as seasoned researchers. Using the open source software and programming language R, we will:

  • briefly recap basic aspects of statistical evaluation, descriptive statistics (about 1 day);
  • discuss monofactorial statistical tests for frequencies, means, dispersions, correlations; practicing them, and understanding how many are just special (limiting) cases of regression methods (about 1 day);
  • explore different multifactorial and multivariate methods, especially regression approaches and hierarchical cluster analysis. Detailed discussion of linear and binary logistic regression to (i) understand what exactly regression coefficients and summary statistics mean and (ii) visualize their results. Selected cases of poisson and/or multinomial regression (about 1-1.5 days);
  • practice mixed-effects/multilevel modeling, including crossed random effects and nested random effects. Associated aspects of model selection procedures, visualization and validation. Time and interest permitting, also contrast settings and regression approaches allowing to model curvature (about 1 day);
  • spend the remainder of the workshop on exploratory methods – mostly hierarchical cluster analysis and follow-up evaluation statistics.

For all statistical methods to be explored, we will discuss how to test their assumptions and visualize their results with annotated statistical graphs, and sometimes we will re-analyze published data. Participants will also get small functions they can use for their own statistical applications. Also, time permitting, there will be a small section on how to write small statistical/visualization functions yourself.


Morning Sessions: 9:00am-12:15pm with a coffee break

Lunch: 12:15pm-1:45pm

Afternoon Sessions: 1:45pm-5:00pm with a coffee break