Logistic Regression: How do you interpret output?

Originally published on RamG Data Analytics & Insights (www.ramganalytics.com)

In the previous blog, we  elaborated on Why and How to learn Predictive Modelling?

One of the commonly used statistical techniques is Logistic Regression.  In this blog focus is to understand logistic regression out. We are using SAS for executing logistics regression but similar results & statistics will be published for logistic regression in other tools such as SPSS and R.

The output of a logistic regression is explained in a simplified way. Logistic Regression output has model selection and performance criteria or statistics.  We have used default SAS Logistic Regression output to illustrate important statistics. Number of additional options could be used  for any specific requirements such as getting ROC curve or C table.

SAS has following important section in Logistic Regression output

• Model Information
• Response Profile and Model Convergence Status
• Model Fit Statistics and Testing Global Null Hypothesis: BETA=0
• Analysis of Maximum Likelihood Estimates and Odds Ratio Estimates
• Association of Predicted Probabilities and Observed Responses

Model Information

Model information provides details on the input dataset name and response variable used. Logistic Regression can be used for building an ordinal and multinomial regression model. So, it has information on whether a binary logit or different model is built.

Below is an example of two Logistic Regression Outputs one with Response variable (Target or Dependent Variable)  “Florence” as Binary and Second Logistic Regression Model with  Response Variable (Target or dependent variable) “F”  as Ordinal

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