title: Multivariate multiple regression prediction models: a Euclidean distance approach.
abstract: An extension of a multiple regression prediction model to multiple response variables is presented. An algorithm using least sum of Euclidean distances between the multivariate observed and model-predicted response values provides regression coefficients, a measure of effect size, and inferential procedures for evaluating the extended multivariate multiple regression prediction model.
authors.affiliation: Department of Statistics, Colorado State University, Fort Collins 80523-1877, USA. mielke@lamar.colostate.edu
authors.forename: Paul W
authors.initials: PW
authors.lastname: Mielke
authors.forename: Kenneth J
authors.initials: KJ
authors.lastname: Berry
mesh_terms: D000293:Adolescent; D001077:Aptitude Tests / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008297:Male; D008432:Mathematical Computing; D015233:Models, Statistical; D015999:Multivariate Analysis; D011594:Psychometrics / Q000379:methods*; D012044:Regression Analysis