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Implications in regression results when assumption of multic

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One of the assumptions of regression analysis is that the independent variables are not correlated. What are the practical implications of interpreting regression results when this assumption, known as multicollinearity, is violated?

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Solution Summary

Discussion (218 words) is in everyday language that explains this common problem among predictor variables.

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Multicollinearity occurs when two predictor (independent) variables predict the same outcome (dependent variable). The "degree" is how much of the explanatory power the two explanatory (independent) variables have in common. We use correlation to predict the level of overlap (multicollinearity) and whether our model can handle ...

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Carol Sargent, PhD
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  • BSc, University of Virginia
  • MSc, University of Virginia
  • PhD, Georgia State University
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