Multivariate Statistical Tests

Andreas Tilevik
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Andreas Tilevik: University of Skövde

Chapter Chapter 9 in Multivariate Statistics and Machine Learning in R For Beginners, 2025, pp 127-146 from Springer

Abstract: Abstract This chapter focuses on multivariate statistical tests, which are powerful tools for analyzing and interpreting data with multiple outcome variables. Unlike univariate tests that examine one dependent variable at a time, multivariate tests consider the relationships and interactions among multiple dependent variables simultaneously. This chapter begins by introducing Hotelling’s T-squared test and MANOVA, which can be seen as the multivariate counterparts of the t-test and ANOVA, respectively. It also shows how to compute PERMANOVA, a non-parametric alternative to MANOVA. This chapter ends with canonical correlation analysis, a multivariate method for examining the correlation between two sets of variables.

Date: 2025
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DOI: 10.1007/978-3-032-01851-9_9

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