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Sep 18, 2017 2:08 PM
(1792 views)

Hello,

I am completing a logistic regression model with multiple independent variables. I am having a hard time determining how to interpret the estimate/tests produced by SAS.

I had read that the "Effect likelihood test" had the best approximation for my P values to determine significance (since my data set is not large enough for the Wald test) and was using the Odds Ratios to determine what that effect was. However, for several values the effect likelihood ratio test determined significance (Prob>ChiSq was less than 0.05) but the 95% CI for the Odds Ratio cross 1 (and therefore is not significant). How do I interpret these differences?

Additionally, I would like to make sure that the model looked at whether the predictor values were actually independent. How do you test for this and what do you do if the "independent" variables are not actually independent?

Happy to send screenshots if needed. Thank you in advance

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In the odds ratio table JMP reports the Wald Chi square p-values and the Wald confidence limits. However, in the Effect Tests table, the Likelihood Ratio tests are reported by default. So they may be different from Wald tests in the Odds Ratio table. Effect Wald tests can be requested in the red triangle.

There is a test called Hausman-Wu test for testing endogeneity of an explanatory variable.

http://support.sas.com/documentation/cdl/en/etsug/63939/HTML/default/viewer.htm#etsug_model_sect051..... It is not currently available in JMP.

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In the odds ratio table JMP reports the Wald Chi square p-values and the Wald confidence limits. However, in the Effect Tests table, the Likelihood Ratio tests are reported by default. So they may be different from Wald tests in the Odds Ratio table. Effect Wald tests can be requested in the red triangle.

There is a test called Hausman-Wu test for testing endogeneity of an explanatory variable.

http://support.sas.com/documentation/cdl/en/etsug/63939/HTML/default/viewer.htm#etsug_model_sect051..... It is not currently available in JMP.