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Multivariate Statistical Tests

In: Multivariate Statistics and Machine Learning in R For Beginners

Author

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  • Andreas Tilevik

    (University of Skövde)

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.

Suggested Citation

  • Andreas Tilevik, 2025. "Multivariate Statistical Tests," Springer Books, in: Multivariate Statistics and Machine Learning in R For Beginners, chapter 0, pages 127-146, Springer.
    • Handle: RePEc:spr:sprchp:978-3-032-01851-9_9
    DOI: 10.1007/978-3-032-01851-9_9
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