Multicollinearity in regression occurs when independent variables have strong correlations, impacting coefficient estimation. Perfect multicollinearity leads to regression model issues, while imperfect multicollinearity affects coefficient estimation. Detection methods and consequences, such as incr

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Multicollinearity in regression analysis can be assessed using various tests such as Variable Inflation Factors (VIF) and R^2 value. VIF measures the strength of correlation between independent variables, while an R^2 value close to 1 indicates high multicollinearity. The Farrar Glauber test and con

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Multicollinearity is a crucial issue in regression analysis, affecting the accuracy of estimators and hypothesis testing. Detecting multicollinearity involves examining factors like high R-squared values, low t-statistics, and correlations among independent variables. Ways to identify multicollinear

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