Predictive Modeling Using Logistic Regression

This course covers predictive modeling using SAS/STAT software with emphasis on the LOGISTIC procedure.

Duration 14 Days
Certificate SAS Global
Language English

Fees 14400 + taxes

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Course Description

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

About Program

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.

Format of Training

All the benefits of the classroom without the travel:

Led by an expert instructor who can virtually look over your shoulder. Discuss, share, exchange ideas with students from different countries

Classroom training options include courses offered in our regional training centers or via our Live Web classroom.

Taught by certified instructors at high-tech facilities across the country

  • A SAS expert at your side.
  • Focused learning away from the office
  • Networking opportunities
  • State-of-the-art facilities
  • Electronic course notes downloadable to your device and permission to print
  • Business Knowledge Series: in-depth courses on the latest business topics
  • We offer Connected Classes! Watch for courses in Cary, New York, Arlington, Dallas and San Francisco that connect remote students via our Live Web classroom.

Train when and where you want

  • Eliminate travel costs and time away
  • Enjoy award-winning e-Courses

SAS e-Learning courses do not include SAS software. You must have SAS software installed to complete the practice exercises.


Before attending this course, you should

This course addresses SAS/STAT software.

Training Features

  • 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.

Course Curriculum

Predictive Modeling

  • business applications
  • analytical challenges

Fitting the Model

  • parameter estimation
  • adjustments for oversampling

Preparing the Input Variables

  • missing values
  • categorical inputs
  • variable clustering
  • variable screening
  • subset selection

Classifier Performance

  • ROC curves and Lift charts
  • optimal cutoffs
  • K-S statistic
  • c statistic
  • profit
  • evaluating a series of models

Course Fees





+ Applicable Taxes
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