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Predictive Modeling Using Logistic Regression

GK# 2470

Course Overview


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


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Viewing outline for:

Classroom Live Outline

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


Viewing labs for:

Classroom Live Labs

Exercises or hands-on workshops are included with most SAS courses.



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
Course Delivery

This course is available in the following formats:

Classroom Live

Receive face-to-face instruction at one of our training center locations.

Duration: 2 day

Virtual Classroom Live

Experience expert-led online training from the convenience of your home, office or anywhere with an internet connection.

Duration: 4 day

Request this course in a different delivery format.