PSYC 2021. Statistical methods ISummer term S1 2018
Tuesday and Thursday, 2:30 - 5:30
syllabus | course webpage
Description: This course covers the fundamental concepts and application of descriptive statistics. It provides an introduction to probability and inferential statistics, including hypothesis testing with the normal and t distributions.
PSYC 6229. Statistical modelling of perception and cognitionWinter term 2019
syllabus | github
Description: This graduate course covers fundamental statistical concepts and their application to statistical modelling in psychology. Topics in statistical foundations include probability, random variables, common statistical distributions, and Bayes' theorem. To illustrate these concepts we cover classic statistical models of behaviour and physiology, such as signal detection theory, optimal cue combination, diffusion models of reaction times, probability summation, and ideal observers. We also discuss model fitting and testing, e.g., parameter estimation, bootstrapping, goodness of fit, and model selection. The course uses R, a statistical programming language, for illustrations and problems.
PSYC 6273. Computer programming for experimental psychologyFall term 2018
syllabus | github
Description: This graduate course covers computer programming methods that are useful in experimental psychology. The course assumes no previous programming experience, and brings students to the point where they are able to write useful programs to advance their own research. Classes are held in a computer laboratory, and each week's class consists of a lecture followed by programming practice on assigned problems. Topics include the MATLAB programming language, data files, curve fitting, Monte Carlo simulations, statistical tests, journal-quality data plots, 2D and 3D graphics, and interfacing to external devices.