We provide additional material for each method to ease the understanding and to improve your learning experience.

Fact sheet
Fact sheets summarize the main facts about a specific method
Table of content
Check the TOC of the corresponding book chapter
Excel example
Run your first multivaraite analysis with the help of Excel
R code + output
R code and output for the chocolate case study
Test your knowledge
Quizzes to test your knowledge
SPSS data set
Order the SPSS data set + syntax used in the book
Lecturer support
We offer lecturer slides as well as all tables and figures
We plan to offer videos to ease the understanding of specific issues

Basics of Data Analysis

The introduction serves as a refresher and reference to basic statistical measures.

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Regression Analysis

Regression Analysis is the most frequently used method of multivariate analysis.

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Analysis of Variance

Analysis of Variance (ANOVA) is frequently used to analysis experimental data.

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Discriminant Analysis

Discriminant Analysis aims to identify differences across groups.

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Logistic Regression

Logistic regression is suited if the dependent variable is categorical.

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Contingency Analysis

Contingency analysis tests whether categorical variables are associated.

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Factor Analysis

Factor analysis reduces a large number of variables to a smaller number of factors.

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Cluster Analysis

Cluster analysis groups subjects or objects that are similar.

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Conjoint Analysis

Conjoint analysis measures preferences and attribute importance for products.

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