# ANOVA with R

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 may be included
in 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. - Combination of effects: two-way ANOVA.

## 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.

If you are interested in the statistical background of gene expression analysis with high-throughput sequencing, then please take our RNA-Seq data analysis course.

## Prerequisites

Participants should have a good understanding of basic statistics. Our Statistics with R course helps you refresh your stats skills.

Basic familiarity with R is advantageous, in particular:

- Using the R Studio environment
- How to invoke R functions, pass optional/named parameters
- Some familiarity with simple plotting commands

If you have attended our R as a programming language training then you are well equipped to take this course, but this is not a strict pre-requisite.

## Practical information

**Number of participants:** minimum 5, maximum 10.

**Length:** The course takes one half-day,
from 09:30 to 13:00 with one break.