Difference between revisions of "Multiple Regression Analysis"
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| − | + | Collinearity is "a condition that exists when two predictors correlate very strongly" (Meyers, Gamst, & Guarino, 2017, p. 189). | |
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| + | Multicollinearity is a condition that exists when "more than two predictors correlate very strongly" (p. 189). | ||
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''Contribution by: Britany Kuslis, WCSU Cohort 8'' | ''Contribution by: Britany Kuslis, WCSU Cohort 8'' | ||
Revision as of 20:07, 16 November 2019
Collinearity and Multicollinearity
Collinearity is "a condition that exists when two predictors correlate very strongly" (Meyers, Gamst, & Guarino, 2017, p. 189).
Multicollinearity is a condition that exists when "more than two predictors correlate very strongly" (p. 189).
Contribution by: Britany Kuslis, WCSU Cohort 8
Reference: Meyers, S., Gamst, G., & Guarino, A.J. (2017). Applied multivariate research: Design and interpretation. Thousand Oaks, CA: Sage Publications.
