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Linear Regression – Assumption – 2 (Effect Of Multicollinearity In Regression Model.)
Effect Of Multicollinearity In Regression Model. Table Of Contents: What Is Multicollinearity? Effects Of Multicollinearity. (1) What Is Multicollinearity? Multicollinearity refers to a situation in multiple regression where two or more independent variables are highly correlated. This means that these variables share a significant amount of the same information, making it difficult for the regression model to separate their individual effects on the dependent variable. Here’s how multicollinearity affects regression coefficients: (2) Effects Of Multicollinearity. 1. Instability of Coefficients When multicollinearity exists, small changes in the data can lead to large changes in the estimated regression coefficients. This happens because
