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Multiple Linear Regression: The General Linear Model SIG Special Topics Paper Session
Sun, April 10, 10:35am to 12:05pm, Convention Center, Floor: Level One, Room 145 ASession Type: Paper Session
Abstract
This session will explore various considerations and techniques based on general linear models ranging from multinomial and ordinal regression to canonical correlations. Insights about selection criteria, number of predictors relating to multicollinearity, and methods of assessing intervention effects with small sample sizes will be presented.
Sub Unit
Chair
Papers
A Simulation Study Evaluating the Generalized Additive Model for Assessing Intervention Effects With Small Samples - William Holmes Finch, Ball State University; Maria E. Hernandez Finch, Ball State University
A Coefficient of Discrimination for Use With Multinomial and Ordinal Regression Models - Thomas J. Smith, Northern Illinois University; David A. Walker, Northern Illinois University; Cornelius McKenna, Kishwaukee College
Best Subset Selection Criteria for Multiple Linear Regression - Gordon P. Brooks, Ohio University - Athens; Pornchanok - Ruengvirayudh, Ohio University - Athens
Interpreting Canonical Correlation Results: Using Yhat Software to Analyze Canonical Variates - Kim Nimon, The University of Texas - Tyler; Linda Reichwein Zientek, Sam Houston State University
Number of Predictors and Multicollinearity: What Are Their Effects on Error and Bias in Regression? - Matthew R. Lavery, Polk County Public Schools; Parul Acharya, University of Central Florida; Stephen A. Sivo, University of Central Florida; Lihua Xu, University of Central Florida