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

• 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

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...