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⇱ Course: Categorical Data Analysis Using Logistic Regression

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Categorical Data Analysis Using Logistic Regression
CDALR : CDAL42

Learn How To

  • use the FREQ procedure for preliminary analyses
  • recognize when logistic regression is appropriate
  • write code in the LOGISTIC procedure for binary, ordinal, and nominal logistic regression
  • create effect plots and odds ratio plots using ODS Statistical Graphics
  • create empirical logit plots
  • eliminate redundancy in model effects using PROC VARCLUS
  • use automatic model building options in PROC LOGISTIC
  • assess models for fit and influential observations using PROC LOGISTIC
  • assess functional form of the model effects using PROC GENMOD
  • create ROC curves for measuring sensitivity and specificity
  • perform exact and conditional logistic regression with PROC LOGISTIC
  • analyze repeated and clustered data using Generalized Estimating Equations (GEE's) in the GENMOD procedure.

Who Should Attend

Biostatisticians, epidemiologists, social scientists, and physical scientists who analyze categorical response data and predictive modelers who would like to learn more about the statistical background of logistic regression

Prerequisites

Before attending this course, you should
  • have a working knowledge of statistical modeling, including concepts of regression, analysis of variance, and contingency table analysis, which you can obtain in the Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression course
  • have an understanding of basic syntax in SAS procedures and DATA steps
  • have experience in executing SAS programs and creating SAS data sets, which you can gain by completing the SAS Programming 1: Essentials course
  • have experience analyzing frequency tables using SAS software
  • have completed a course in statistics that covers linear regression and logistic regression, which you can achieve by completing the Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression course.

SAS Products Covered

SAS/STAT

Course Outline

Categorical Data and Contingency Table Analysis
  • introduction to categorical data
  • associations among categorical variables
  • stratified contingency table analysis
Binary Logistic Regression
  • introduction to logistic regression
  • adding categorical predictors and the CLASS statement
Model Building
  • empirical logit plots
  • variable clustering for variable reduction
  • confounding and interactions
  • automatic model selection
  • customized tests
Model Illustration and Assessment
  • interaction illustration
  • model assessment
  • ROC curves
  • outlier detection
Multinomial Logistic Regression
  • nominal logistic regression
  • ordinal logistic regression
Advanced Topics
  • correlated observations
  • GEE regression models
  • conditional logistic regression
  • failure to converge and small samples
Live Class Schedule
Duration: 21 hours
Step into our live classes and experience a dynamic learning environment where you can ask questions, share ideas, and connect with your instructor and classmates. With on-demand lab hours, you can explore the material at your own pace. Our globally acclaimed instructors will motivate you to think bigger, so you can take what you've learned and achieve your biggest goals.
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Live Class Schedule SOLD SEPARATELY
LANGUAGE
EVENT FEE
05-07,10-12 AUG 2026
Live Web, US
1:00 PM-4:30 PM EDT
English
2,400 USD
Private Training
Get training tailored specifically for your team, led by expert SAS instructors. Choose from virtual sessions, or training at your location (or ours). Perfect for teams seeking a customized curriculum and plenty of interaction with a SAS specialist. We'll schedule it at a time that works for you.
Coaching Services
Take your training to the next level with personalized coaching. While private training offers structured coursework, coaching provides hands-on, real-time support from a subject matter expert. As you work with your own data, you'll receive expert guidance to help you uncover insights, unlock the full potential of your data, and make faster progress. Perfect for those looking to apply what they’ve learned and see quicker results.