Structure of Statistics: Multivariate Data Analysis

Matrix algebra avoids the plethora of summation signs and their associated subscripts and superscripts littering pages of most textbooks on statistical analysis and is necessary for understanding the multivariate methods of statistical analysis. This book presents concise description of multiple regression, principal components analysis, factor analysis, canonical analysis, and multivariate analysis of variance (MANOVA). The formal description of these methods is complemented by numerical illustrations using minute data matrices, consisting of different arrangements of numbers 0 1 2 3 4 5. If you would like to understand statistics, what really matters is to know how it's done.