Which of the following statements about multicollinearity is true?

a. Multicollinearity occurs when two or more independent variables are highly correlated
b. Multicollinearity is usually not an issue when the regression model is only being used for forecasting
c. Multicollinearity is usually not an issue when the regression model is only being used to understand net relationships
d. Multicollinearity can typically be reduced by decreasing the sample size
e. Multicollinearity can typically be reduced by adding more independent variables

Multicollinearity usuall occurs when an independent variables in a regression model is correlated. This correlation is a problem which occurs because independent variables should be independent. If the degree of correlation between variables is high enough, it would cause problems when you fit the models or try to interpret the results.

shivishivangi1679 shivishivangi1679 · Answer 2 · 2022-03-29T08:38:19+02:00

According to the statement of multicollinearity, option b is the correct answer it occurs when two or more independent variables are highly correlated.

What is Multicollinearity?

Multicollinearity refers to the occurrence of two or more independent variables which are highly correlated with one another in a regression model.

The statement means that in a regression model, independent variables can be predicted from another independent variable.

For example,

Weight and height.
Water consumption and household income.

Multicollinearity can also occur when new dependent variables are created which are dependent on others.

It can be an issue when the regression model is only being used to understand the relationship. It is unable to distinguish between the separate effect of an independent variable on the dependent one.

So, option a is correct in that is multicollinearity occurs when two or more independent variables are highly correlated.

Learn more about multicollinearity here:

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