Syllabus and overview
This course is a hands-on introduction (in a computer lab) 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 by extending chi-squared tests to binomial logistic regression for binary dependent variables and Poisson regression for dependent variables that are counts and then proceed to ordinal logistic and multinomial regression. Finally, there will be one session on repeated-measures ANOVAs and mixed-effects/multilevel models. In addition to these modeling techniques, we will also explore the exploratory method of hierarchical cluster analysis to find structure in large, potentially messy data sets. We will use the open source software tool the open source software tool R 
and code from the second edition of my book Statistics for Linguistics with R.
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Graded assignments