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Binary Logistic Regression: What, When and How

Sue Walsh, Technical Support Statistician, SAS

 

Analysts in many application areas often have a response variable that has only two possible levels, with one of those being the desired outcome. Binary logistic regression will allow the analyst to predict the probability of the desired outcome, determine which input variables are most closely associated with those outcomes, and produce odds ratios that provide a measure of the effect on the outcome. This presentation will provide an introduction to the analysis of this type using binary logistic regression in the Fit Y by X and Fit Model platforms of JMP. It will discuss the interpretation of the results including p-values, odds ratios, graphical displays and goodness of fit statistics.

Comments

Hi,

 

If I remember right there was a possibility to add confidence Intervalls to the Profiler View. I'm using JMP - no JMP Pro. I cannot find the right click. Can you help me? An interactive Help would be appreciated.

Many thanks Peter

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