## Summary

Multicollinearity is a phenomenon in which one predictor variable in a multiple regression model can be linearly predicted from the others with a substantial degree of accuracy.
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It occurs when independent variables in a regression model are correlated
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, and can result in less reliable statistical inferences.
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The term was first used by Ragnar Frisch.
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According to

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## Summaries from the best pages on the web

Summary
In statistics , multicollinearity (also collinearity ) is a phenomenon in which one predictor variable in a multiple regression model can be linearly predicted from the others with a substantial degree of accuracy

Multicollinearity - Wikipedia
wikipedia.org

Summary
Multicollinearity is a statistical concept where several independent variables in a model are correlated. Two variables are considered to be perfectly collinear if their correlation coefficient is +/- 1.0. Multicollinearity among independent variables will result in less reliable statistical inferences.

Multicollinearity: Meaning, Examples and FAQ
investopedia.com

Summary
The term multicollinearity was first used by Ragnar Frisch. It describes a perfect or exact relationship between the regression exploratory variables. Linear regression analysis assumes that there is no perfect exact relationship among exploratory variables.

Multicollinearity - Statistics Solutions
statisticssolutions.com

10.4 - Multicollinearity Multicollinearity exists when two or more of the predictors in a regression model are moderately or highly correlated with one ...

10.4 - Multicollinearity | STAT 462
psu.edu

One of the assumptions of Classical Linear Regression Model is that there is no exact collinearity between the explanatory variables. If the explanatory ...

Multicollinearity in R | DataScience+
datascienceplus.com