Introducing a series of highly correlated independent variables into a regression analysis will make it difficult to interpret the estimated regression coefficients because they may tend to fluctuate widely in response to small changes in the model or data set.

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This phenomenon is known as multicollinearity, and it occurs when independent variables in a regression model are highly correlated. This correlation makes it difficult to determine the individual effect of each variable on the dependent variable because changes in one predictor are associated with changes in another.

Introducing a Series of Highly Correlated Independent Variables into a Regression