title: Computing aspects of power for multiple regression.
abstract: Rules of thumb for power in multiple regression research abound. Most such rules dictate the necessary sample size, but they are based only upon the number of predictor variables, usually ignoring other critical factors necessary to compute power accurately. Other guides to power in multiple regression typically use approximate rather than precise equations for the underlying distribution; entail complex preparatory computations; require interpolation with tabular presentation formats; run only under software such as Mathmatica or SAS that may not be immediately available to the user; or are sold to the user as parts of power computation packages. In contrast, the program we offer herein is immediately downloadable at no charge, runs under Windows, is interactive, self-explanatory, flexible to fit the user's own regression problems, and is as accurate as single precision computation ordinarily permits.
authors.affiliation: Tulane University, New Orleans, LA 70112, USA.
authors.forename: William P
authors.initials: WP
authors.lastname: Dunlap
authors.forename: Xue
authors.initials: X
authors.lastname: Xin
authors.forename: Leann
authors.initials: L
authors.lastname: Myers
mesh_terms: D006801:Humans; D011584:Psychology / Q000295:instrumentation*; D012044:Regression Analysis; D012984:Software