# Applied Multivariate Statistics with RMultivariate Normal Distribution

Applied Multivariate Statistics with R: Multivariate Normal Distribution [IN THIS CHAPTER, we generalize the bivariate normal distribution from the previous chapter to an arbitrary number of dimensions. We also make use of the matrix notation. The mathematics is generally more dense and relies on the linear algebra notation covered in Chap. 4 In Sect. 4.5 we pointed out there is a limit on what computations we can reasonably perform by hand. For this reason, we illustrate these various operations with the help of R.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

# Applied Multivariate Statistics with RMultivariate Normal Distribution

33 pages

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# References (4)

Publisher
Springer International Publishing
© Springer International Publishing Switzerland 2015
ISBN
978-3-319-14092-6
Pages
173 –205
DOI
10.1007/978-3-319-14093-3_7
Publisher site
See Chapter on Publisher Site

### Abstract

[IN THIS CHAPTER, we generalize the bivariate normal distribution from the previous chapter to an arbitrary number of dimensions. We also make use of the matrix notation. The mathematics is generally more dense and relies on the linear algebra notation covered in Chap. 4 In Sect. 4.5 we pointed out there is a limit on what computations we can reasonably perform by hand. For this reason, we illustrate these various operations with the help of R.]

Published: May 22, 2015

Keywords: Mahalanobis Distance; Variance Matrix; Multivariate Normal Distribution; Bivariate Normal Distribution; Autocorrelation Matrix