ABSTRACT
CA produces a map where the rows and columns are depicted together as points, with an interpretation that depends on the choice amongst the many scaling options for the row and column points. Geometrically, we have seen how the positions of the row points depend on the positions of the column points, and vice versa. In this chapter we focus on the mathematical relationships between the row and column points, known as the transition equations. In addition, since regression analysis is a well-known method in statistics, we show how the row and column results and the original data can be connected through linear regression models. This chapter can be skipped without losing the thread of the presentation of the geometric interpretation of CA.
