Exploiting Sources of Variation in your Data: the ANOVA Approach

Starts on 11.01.2022

AI and Data Science


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° DA2122-M5 EN
Tags: Postacademische opleiding


To emphasize the practical approach in this course all classes will take place in a pc room.

Analysis of variance (ANOVA) is a statistical tool used in the comparison of means of a random variable over populations that differ in one or more characteristics (factors), e.g. treatment, age, sex, subject, etc.

In this course we will focus on correct execution of data analysis and understanding its results. We pay attention to expressing these conclusions in a correct and understandable way.

The different methods will be extensively illustrated with examples from scientific studies in a variety of fields.

Exercises are worked out behind PC using the R software. If preferred, participants with sufficient experience can use SPSS.

This course is part of a larger course series in Data Analysis consisting of 19 individual modules. Find more information and enroll for this module via www.ipvw-ices.ugent.be


First, we cover one-way ANOVA, where only one factor is of concern. Depending on the type of the factor, the conclusions pertain to just those factor levels included in the study (fixed factor model), or to a population of factor levels of which we observed a sample (random effects model).

In two-way and multi-way ANOVA where populations differ in more than one characteristic, the effects of factors are studied simultaneously. This yields information about the main effects of each of the factors as well as about any special joint effects (factorial design).

We also consider nested designs, where each level of a second (mostly random) factor occurs in conjunction with only one level of the first factor. One special challenge in multi-way ANOVA lies in verifying the assumptions that must be satisfied.

Course number:
Short- en long-term programmes
Area of interest:
AI and Data Science, Sciences
Academic year:
2021 - 2022
Starting date:
Els Adriaens
Contact person:
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