Predictive Modeling Using Logistic Regression
Register* for Upcoming Training
December 13 - 14, 2018
*Please Note: Completing the registration form will result in the generation of an invoice. Payment is expected within 30 days of course completion. Cancellation less than two weeks prior to class start date will result in a cancellation fee equal to 50% of the registration cost. No Exceptions.
TThis 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.
This course can help prepare you for the following certification exam(s): SAS Statistical Business Analysis Using SAS 9: Regression and Modeling, SAS Advanced Predictive Modeling.
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 2: Data Manipulation Techniques 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 1: Introduction to ANOVA, Regression, and Logistic Regression course.
This course addresses SAS/STAT software.
Predictive Modeling Using Logistic Regression: $1,600
Note: This course will last all day for the length of the course. Most classes will start at 9:00AM and end at 5:00PM.
- 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
Location: FedEx Institute of Technology, 365 Innovation Drive
Duration: 9:00 AM - 5:00 PM CST
We ask that attendees bring their own device in order to follow along with the class presentations. The temperature in our training room tends to be cooler, we advise you to bring a light jacket or sweater. Parking passes are provided for the garage across to the FedEx Institute of Technology (located on Innovation Drive).
If you have any additional questions please let us know at email@example.com.