Elementary Logistic Regression

Elementary Logistic Regression with Predictive Modeling

Bruce Lund
Friday, June 25, 2021, 10:00am-2:00pm Pacific Daylight Time

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Class Description

This class presents light theory, supported by simulations, for understanding binary logistic regression models using SASĀ®. This discussion of logistic regression begins at the beginning. No prior experience is assumed.

Once the basics of logistic regression are introduced, the class focuses on using logistic models in predictive modeling on large datasets. Examples from credit risk and automotive marketing are given. The class will be less focused on explanatory models as would arise in the bio-sciences.
Topics include: Logistic regression versus other methods; Likelihood function and maximum likelihood estimators; Statistics for predictor and overall model fit; Screening, binning, transforming of predictors (including weight of evidence coding); Discussion of multicollinearity; Predictor selection methods using PROC LOGISTIC, HPLOGISTIC, HPGENSELECT including best subsets, stepwise with sbc/aic, Lasso; Model validation and assessment including c statistic, R-squares classification error, and lift charts in the context of training, cross-validation, and validation samples.

Class uses BASE SAS and SAS/STAT. No usage of Viya or Enterprise Miner.

Meet the Instructor

Bruce Lund is a statistical modeling consultant and trainer. For 15 years he was a statistical and modeling consultant for OneMagnify of Detroit. Before OneMagnify, he was the customer database manager at Ford Motor Company and a mathematics professor at University of New Brunswick, Canada. He has a mathematics PhD from Stanford University. Bruce Lund has presented at SAS Global Forum, SAS AnalyticsX, ASA CSP, and at regional SAS user group conferences.