Predictive Modeling Using Logistic Regression - PMLR92
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.
|2 days - Classroom|
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
Before attending this course, you should
- have experience executing SAS programs and creating SAS data sets, which you can gain from the SAS Programming 1: 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 Introduction to Statistics Using SAS®: ANOVA, Linear Regression and Logistic Regression course.
- 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
- ROC curves and Lift charts
- optimal cutoffs
- K-S statistic
- c statistic
- evaluating a series of models
This course addresses the following software product(s): SAS/STAT, SAS/GRAPH.