JSM 2005 - Toronto

Abstract #304136

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 140
Type: Contributed
Date/Time: Monday, August 8, 2005 : 10:30 AM to 12:20 PM
Sponsor: General Methodology
Abstract - #304136
Title: Sample Sizes when Using Multiple Linear Regression for Prediction
Author(s): Gregory Knofczynski*+
Companies: Armstrong Atlantic State University
Address: 11935 Abercorn Street, Savannah, GA, 31419, United States
Keywords: sample size ; multiple linear regression ; monte carlo ; prediction
Abstract:

When using multiple regression for prediction purposes, the size of the sample needed must usually be addressed. Models with varying numbers of independent variables were examined. Minimum sample sizes were determined for multiple scenarios at each number of independent variables. The scenarios arrive from varying the levels of correlations between the criterion variable and the predictor variables and among the predictor variables. Two minimum sample sizes were determined for each scenario, a good and an excellent prediction level. The relationship between the squared multiple correlation coefficients and the minimum necessary sample sizes, the relationship between the largest correlation coefficient between the criterion variable and any predictor variable, and the minimum sample sizes were examined. A definite relationship was found between the squared multiple correlation coefficient and the minimum sample size. Also, if only the correlation coefficient between the criterion variable and a predictor variable and the number of predictor variables in the model is known, minimum sample sizes are recommended.

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Revised March 2005