Data Simulation for Linear Models in R

In this three-week workshop, Roger Mundy teaches how to simulate data in a framework of linear models. Simulated data has the advantage that the “truth” is known. Hence, one can compare what a model reveals with truth. The perhaps most well-known use of simulated data is 'power analysis', i.e. determining the probability of an analysis to reveal significance, given a certain effect and sample size, an alpha level, and a test. However, it can also be used to test, for instance, if a method is working as planned, how an analysis behaves if assumptions are violated, or to estimate the width of confidence intervals.

Registration is possible until January 15th through the workshop webpage: For more information and registration, please visit the workshop page.