ABSTRACT
This chapter discusses the primary analytic advantage of multiple regression techniques and the types of variables upon which regression analysis may be performed. It discusses the fraction of the many multivariate statistics that are available for the analysis of both interval and non-interval data. The least squares procedure for multiple regression works in a manner similar to bivariate regression in that it passes a line through a plotting of the values of cases on several variables in such a way as to minimize the sum of the squared distance of each point from that line. The chapter discusses adaptations to three of the most commonly encountered problems: learn how to cope with the issues in the applications of multiple regression, and get a sense of the flexibility of multiple regression as an analytic technique. In the social sciences, important variables often are not measured at the interval/ratio level, thus violating the assumption of interval-level measurement.
