Categorical Data Analysis Using Logistic Regression

Course code: CDAL42

This course focuses on analyzing categorical response data in scientific fields. The SAS/STAT procedures addressed are PROC FREQ, PROC LOGISTIC, PROC VARCLUS, and PROC GENMOD. The ODS Statistical Graphics procedures used are PROC SGPLOT and PROC SGPANEL. The course is not designed for predictive modelers in business fields, although predictive modelers can benefit from the content of this course.
1 800 EUR

2 178 EUR including VAT

Selection of dates
Do you have a question?
+420 731 175 867

and certified lecturers

recognized certifications

Wide range of technical
and soft skills courses

Great customer

Making courses
exactly to measure your needs

Course dates

Starting date: Upon request

Type: Upon request

Course duration: 21 hours

Language: en

Price without VAT: 1 800 EUR


Type Course
Language Price without VAT
Upon request Upon request 21 hours en 1 800 EUR Register
G Guaranteed course

Didn't find a suitable date?

Write to us about listing an alternative tailor-made date.


Target group

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

Course structure

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
  • confounding and interactions
  • automatic model selection
  • variable clustering for variable reduction
  • customized tests

Model Illustration and Assessment

  • interaction illustration
  • model sssessment
  • ROC curves
  • outlier detection

Multinomial Logistic Regression

  • ordinal logistic regression
  • nominal logistic regression

Advanced Topics

  • correlated observations
  • GEE regression models
  • conditional logistic regression
  • failure to converge and small samples


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.
  • Do you need advice or a tailor-made course?


    product support

    ComGate payment gateway MasterCard Logo Visa logo