Number of predictors and multicollinearity: What are their effects on error and bias in regression?

The present Monte Carlo simulation study adds to the literature by analyzing parameter bias, rates of Type I and Type II error, and variance inflation factor (VIF) values produced under various multicollinearity conditions by multiple regressions with two, four, and six predictors. Findings indicate...

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Bibliographic Details

Published in:	Communications in statistics. Simulation and computation Vol. 48; no. 1; pp. 27 - 38
Main Authors:	Lavery, Matthew Ryan, Acharya, Parul, Sivo, Stephen A., Xu, Lihua
Format:	Journal Article
Language:	English
Published:	Philadelphia Taylor & Francis 02.01.2019 Taylor & Francis Ltd
Subjects:	Bias Collinearity Computer simulation Error analysis Monte Carlo simulation Monte Carlo simulation study Multicollinearity Multiple regression Parameters Regression analysis Statistical methods
ISSN:	0361-0918, 1532-4141
Online Access:	Get full text
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