The relationship of multiple linear regression to various multivariate statistical techniques is discussed. The importance of the standardized partial regression coefficient (beta weight) in multiple linear regression as it is applied in path, factor, LISREL, and discriminant analyses is emphasized. The multivariate methods discussed in this paper have in common the general linear model and are the same in several other respects: (1) they identify, partition, and control variance; (2) they are based on linear combinations of variables; and (3) the linear weights can be computed based on standardized partial regression coefficients. However, these methods have different applications. While multiple regression seeks to identify and estimate the amount of variance in the dependent variable attributed to one or more independent variables, path analysis attempts to identify relationships among a set of variables. Factor analysis tries to identify subsets of variables from a much larger set. The LISREL program determines the degree of model specification and measurement error. Discriminant analysis seeks to identify a linear combination of variables that can be used to assign subjects to groups. An understanding of multiple regression and general linear model techniques can greatly facilitate one's understanding of the testing of research questions in multivariate situations. Eight appendices contain computer program examples based on correlational input as illustrations of these methods. A 47-item list of references is provided. (SLD)