Predictive Modeling Using Logistic Regression

Duration: 2.0 days

Audience

This course is designed for predictive modelers and data analysts with basic SAS software programming experience. The issues and techniques discussed in this course are directed toward database marketing, credit risk evaluation, fraud detection, and other predictive modeling applications from banking, financial services, direct marketing, insurance, and telecommunications. This course is not designed for biostatisticians and epidemiologists who are interested in inferential statistics. Persons interested in these applications should take the Categorical Data Analysis Using Logistic Regression course.

Course Description

This course covers predictive modeling using SAS/STAT software with emphasis on the LOGISTIC procedure. This course also discusses selecting variables, assessing models, treating missing values, and using efficiency techniques for massive data sets.

Prerequisites

Before attending this course, you should

• have experience executing SAS programs and creating SAS data sets, which you can gain from the SAS Programming I: 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 I: Introduction to ANOVA, Regression, and Logistic Regression course.

Course Contents

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 Many Models

• evaluating a series of models

Software Addressed

This course addresses the following software product(s): SAS/STAT, SAS/GRAPH.

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