TESTS TO CHECK MULTICOLLINEARITY
VIF, R^2, FarrarGlauber test, and correlation matrix are essential tools to detect multicollinearity in regression analysis. VIF measures the correlation strength between independent variables, R^2 helps assess how well a variable is explained by others, and the FarrarGlauber test identifies multicollinearity presence. Furthermore, constructing a correlation matrix among explanatory variables provides insights into potential problems. Detecting and addressing multicollinearity is crucial for accurate regression results.
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TESTS TO CHECK MULTICOLLINEARITY Anindita Chakravarty
VIF (Variable Inflation Factors). VIF determines the strength of the correlation between the independent variables. It is predicted by taking a variable and regressing it against every other variable. VIF score of an independent variable represents how well the variable is explained by other independent variables.
R^2 value is determined to find out how well an independent variable is described by the other independent variables. A high value of R^2 means that the variable is highly correlated with the other variables. This is captured by the VIF which is denoted below: VIF= 1 1- R2
So, the closer the R^2 value to 1, the higher the value of VIF and the higher the multicollinearity with the particular independent variable. NOTE: VIF starts at 1 and has no upper limit VIF = 1, no correlation between the independent variable and the other variables VIF exceeding 5 or multicollinearity between this independent variable and the others 10 indicates high
FarrarGlauber test: If the variables are found to be orthogonal, there is no multicollinearity; if orthogonal, then at multicollinearity is present. the variables are not least some degree of The Farrar Glauber test has also been criticized by other researchers
Construction of a correlation matrix Construction of a correlation matrix among the explanatory variables will yield indications as to the likelihood that any given couplet of right-hand- side variables are problems. creating multicollinearity Correlation values (off-diagonal elements) of at least 0.4 are sometimes interpreted as indicating a multicollinearity problem.
