Introduction to R and Computational Statistics
Daniel J. Denis · 2020
A basic understanding of vectors and matrices is essential for understanding modern statistics and data analysis. Most of the concepts are very intuitive and can be explained using demonstrations in R. In statistical procedures such as principal component analysis and factor analysis, people will be required to enter a covariance or correlation matrix as an input to the analysis, and hence will often have to build such a matrix so that they can run the statistical method. Many statistical analyses, in one way or another, come down to solving for eigenvalues and eigenvectors of a correlation or covariance matrix. This is more obvious in some procedures such as principal component analysis, but it is also true that eigenvalues and eigenvectors “show up” in many other statistical methods such as regression, discriminant analysis, and MANOVA.