Logistic Regression Research: 97% Concordance, No Sig Variables

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SUMMARY

The discussion centers on a logistic regression analysis conducted using SAS, which achieved a 97% concordance rate but revealed minimal significant variables. The lack of significant variables may stem from dependence issues among predictors or the concentration of significance in a few variables. To address this, participants recommend reducing the model complexity and employing stepwise regression techniques to identify the most impactful factors.

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Data analysts, statisticians, and researchers involved in predictive modeling and logistic regression analysis who seek to enhance their understanding of variable significance and model optimization.

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I am doing an independent research project and I have written a logistic regression program in SAS. The percent concordance is 97%, but hardly any variables are significant. Can anyone help me understand why this would happen?
 
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There are many possible reasons for this. Would you be able to describe the situation in more detail?
If all of the significance is in only a few variables, then you will not have many significant variables.
Otherwise, you may have some dependence issues, and the program can't determine which variables are better than others.
I would try reducing the model, and then applying some stepwise regression techniques to find the most significant factors.
 

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