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Multivariate Linear Regression

In: Linear Regression

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  • David J. Olive

    (Southern Illinois University, Department of Mathematics)

Abstract

This chapter will show that multivariate linear regression with m ≥ 2 response variables is nearly as easy to use, at least if m is small, as multiple linear regression which has m = 1 response variable. Plots for checking the model are given, and prediction regions that are robust to nonnormality are developed. For hypothesis testing, it is shown that the Wilks’ lambda statistic, Hotelling Lawley trace statistic, and Pillai’s trace statistic are robust to nonnormality.

Suggested Citation

  • David J. Olive, 2017. "Multivariate Linear Regression," Springer Books, in: Linear Regression, chapter 0, pages 343-387, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-55252-1_12
    DOI: 10.1007/978-3-319-55252-1_12
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