In order to understand how the computer carries out regression analysis—which automatically leads to a better understanding of regression—it is necessary to understand something about matrix math. This section provides a brief introduction to matrix math.
Matrices are used to describe linear equations by tracking the coefficients of linear transformations and to record data that depend on multiple parameters.
A matrix is a set of numbers in a two-dimensional space. Matrices are almost always designated with capital letters in boldface type. In general, a matrix X has n rows and m columns, as shown in Equation A.1, where the first subscript denotes the row and the second denotes the column.
A matrix is said to have dimensions equal to n and m so that the matrix X has dimensions n × m (rows first, columns second). The following is a typical example of a 2 × 3 matrix.
A vector is a matrix with dimensions n × 1 or 1 × m, as shown in Equation A.2.
The following are typical examples of a 3 × 1 vector and a 1 × 3 vector.
A scalar can be considered a matrix of dimensions 1 × 1. The individual entries of ...