This course covers predictive modelling 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.
Predictive Modeling Using Logistic Regression
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 Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression course.
Who should attend
Modellers, analysts and statisticians who need to build predictive models, particularly models from the banking, financial services, direct marketing, insurance and telecommunications industries
Learn how to
- use logistic regression to model an individual's behaviour 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.
- 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 cut-offs
- K-S statistic
- c statistic
- evaluating a series of models
This course addresses
This is a QA approved partner course
Face-to-face learning in the comfort of our quality nationwide centres, with free refreshments and Wi-Fi.
Find dates and prices
Online booking is currently not available for this course, to find out more please call us on 0345 074 7998 or email us at firstname.lastname@example.org to discuss how we can help.
Fully accredited to ensure we provide the highest possible standards in learning