Predictive Modeling Using Logistic Regression



In this course, you will learn about predictive modeling using SAS/STAT software with emphasis on the LOGISTIC procedure. You will also learn about 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.


SAS Statistical Business Analysis Using SAS 9: Regression and Modeling


  • Modelers, analysts and statisticians who need to build predictive models, particularly models from the banking, financial services, direct marketing, insurance and telecommunications industries


  • Experience executing SAS programs and creating SAS data sets, which you can gain from the SAS Programming 2: Data Manipulation Techniques course
  • Experience building statistical models using SAS software
  • Completion of a statistics course that covers linear regression and logistic regression, such as the Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression course

Learning Objectives

  • 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

1. Predictive Modeling

  • Business applications
  • Analytical challenges

2. Fitting the Model

  • Parameter estimation
  • Adjustments for oversampling

3. Preparing the Input Variables

  • Missing values
  • Categorical inputs
  • Variable clustering
  • Variable screening
  • Subset selection

4. Classifier Performance

  • ROC curves and Lift charts
  • Optimal cutoffs
  • K-S statistic
  • c statistic
  • Profit
  • Evaluating a series of models