Predictive Modeling with Logistic Regression using SAS

This course covers predictive modeling using SAS/STAT software with emphasis on the LOGISTIC procedure. This course also discusses selecting variables and interactions, recoding categorical variables based on the smooth weight of evidence, assessing models, treating missing values, and using efficiency techniques for massive data sets. You learn to use logistic regression to model an individual's behavior as a function of known inputs, create effect plots and odds ratio plots, handle missing data values, and tackle multicollinearity in your predictors. You also learn to assess model performance and compare models

• Apply logistic regression for behavior modeling
• Create effect and odds ratio plots
• Handle missing data and multicollinearity
• Assess and compare model performance

• Basic familiarity with modeling concepts
• Readiness for applied exercises or case studies