I am a bit confused as to how we find the singular values and therefore condition index number to detect multicollinearity in multiple linear regression analysis. Some mathematicians say the singular values are the square roots of the eigenvalues of the correlation matrix of the predictors of a model. While others says we use covariance matrix instead. Again some math publications said The singular values are the square roots of the eigenvalues of the square matrix X’X of multiple linear regression model. Then I treid using all three methods but when I cross checked with MATLAB results using collintest it does not match with either of my calculations. it does not explain how we go the output. can someone explain it to me?I am a bit confused as to how we find the singular values and therefore condition index number to detect multicollinearity in multiple linear regression analysis. Some mathematicians say the singular values are the square roots of the eigenvalues of the correlation matrix of the predictors of a model. While others says we use covariance matrix instead. Again some math publications said The singular values are the square roots of the eigenvalues of the square matrix X’X of multiple linear regression model. Then I treid using all three methods but when I cross checked with MATLAB results using collintest it does not match with either of my calculations. it does not explain how we go the output. can someone explain it to me? I am a bit confused as to how we find the singular values and therefore condition index number to detect multicollinearity in multiple linear regression analysis. Some mathematicians say the singular values are the square roots of the eigenvalues of the correlation matrix of the predictors of a model. While others says we use covariance matrix instead. Again some math publications said The singular values are the square roots of the eigenvalues of the square matrix X’X of multiple linear regression model. Then I treid using all three methods but when I cross checked with MATLAB results using collintest it does not match with either of my calculations. it does not explain how we go the output. can someone explain it to me? collintest single value decomposition MATLAB Answers — New Questions

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