Predictive Modeling Using Logistic Regression

Duration: 16 hours

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.

Learn how to:

  • Use logistic regression to model an individual’s behavior as a function of known inputs
  • Create effect plots and odds ratio plots using ODS Statistical Graphics
  • Handle missing data values
  • Tackle multicollinearity in your predictors
  • Assess model performance and compare models.

Who should attend: Modelers, analysts and statisticians who need to build predictive models, particularly models from the banking, financial services, direct marketing, insurance and telecommunications industries.

Prerequisites:

Before attending this course, you should:

  • Have experience executing SAS programs and creating SAS data sets, which you can gain from the Programming2 – Essentials course.
  • Have experience building statistical models using SAS software.
  • Have completed a statistics course that covers linear regression and logistic regression, such as the Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression course.

This course addresses SAS/STAT software.