A term used to describe the case when the independent variables in a multiple regression model are correlated is _____.

Multicollinearity: In statistics, the term "multicollinearity" is described as a situation in which two or more than two "explanatory variables" in a particular "multiple regression model" are being linearly related. The perfect multicollinearity occurs when the correlation between two different independent variables is considered as equal to -1 or 1.

In other words, multicollinearity generally occurs when "independent variables" are being correlated in a regression model.

In the question above, the given statement represents multicollinearity.

priyankapandeyVT priyankapandeyVT · Answer 2 · 2022-03-07T06:33:11+01:00

Multicollinearity is the term used to describe the case when the independent variables in a multiple regression model are correlated.

What is multicollinearity?

The situation of Multicollinearity arises because of correlation of independent variables in a regression model. In simple words, correlation of two or more independent variables on another variable in a regression model.

For example- correlation of height weight and BMI, income and consumption. The perfect multicollinearity occurs when the correlation is between two different independent variables and is considered equal to -1 or 1. The problem of Multicollinearity arises due to no proper research while constructing the data.

Therefore, the term used to define the case of independent variables in a multiple regression model is correlated Multicollinearity.

Learn more about Multicollinearity here:

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