Syllabus and overview
This course is a hands-on introduction to more advanced statistical methods to analyze observational and experimental data. After a small recap of monofactorial methods and graphs and an introduction to a process called modeling or model selection, we systematically extend monofactorial tests to their multifactorial and multivariate counterparts. We begin with the linear model and extend correlations and t-tests to multiple linear regression, ANOVAs, and ANCOVAs. We then broaden the scope to the powerful methods included in generalized linear modeling (such as binomial logistic regression for binary dependent variables and Poisson regression for dependent variables that are counts) as well as ordinal logistic and multinomial regression. There is also one session on tree-based methods (classification and regression trees as well as random forests). In addition to these modeling techniques, we also discuss the exploratory method of hierarchical cluster analysis to find structure in large, potentially messy data sets. We use the open source software tool the open source software tool R 
and the second edition of my book Statistics for Linguistics with R.
Downloads
Files for session 01
Files for session 02
Files for session 03
Files for session 04
Files for session 05
Files for session 06
Files for session 07
Files for session 08
Files for session 09
Files for session 10
Graded assignments