Consider the following output from a regression analysis 0.98223752 0.96479055 M

Question

Consider the following output from a regression analysis 0.98223752 0.96479055 Multiple FR R Square Adjusted R 0.95518798 Square Standard Error 0.23310465 Observations 15 MS 5.459428 100.4721 2.82244E-08 0.054338 df Significance F Regression Residual Total 16.37828444 0.597715562 16.976 14 Coefficients Standard Error tStat 3.98076893 1.573044228 0.07321648 0.020889718 0.0323216 0.020895785 0.0038861 0.003833348 -valtte Intercept X1 X2 X3 2.530615 0.027943 3.504905 0.004928 1.5468 0.150182 1.01376 0.332478 Multicollinearity likely exists between variables: X1 and X.3 X2 and X3 X2 and Xl1 It cannot be determined from the information given.

Explanation / Answer

Multicollinearity can be determined using VIF and Correation Matrix.

Here the information provided is not sufficient hence

It cannot be determined from the information given

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