The aim of this short course is to explain why we have to analyse variances if we want to compare group means using ANOVA techniques. This lecture is best taken with the Linear regression with R course. Please note that we cannot go into the specific data analysis problems of your particular project.

Instructor: András Aszódi.
Topics

Comparing the means of several samples by analyzing variances: the intuition behind ANOVA.
One-way ANOVA techniques: prerequisites, omnibus F-test, post hoc tests.
Power and sample size calculations.
The relationship between ANOVA and linear regression (this is optional).
Combination of effects: two-way ANOVA.
Comparing linear regressions: Analysis of Covariance (ANCOVA).

Out of scope

This course will not teach you bioinformatics. In particular, no high-throughput sequencing data will be used because they are impractically large, and not everyone on campus is working with sequencing.
Prerequisites
Mandatory: Good understanding of basic statistics concepts. If you have attended our Think Statistics with R course, then you are all set.
Recommended: Basic familiarity with R. Our R as a programming language course provides you with the necessary knowledge.
“Bring Your Own Data”

You can bring your own data to this course and run a one-way ANOVA on it.

Please prepare a comma-separated-values (CSV) file with UNIX line endings (\n) that contains several (more than 2) columns corresponding to the groups of data whose means you would like to compare. All groups should contain the same number of observations (a “balanced” ANOVA design).
Practical information

Number of participants: minimum 5, maximum 10.

Length: The course takes one half-day, from 09:00 to 13:00 with two short breaks.