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

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Starting date: Upon request

Type: Upon request

Course duration: 21 hours

Language: en

Price without VAT: 1 800 EUR

	Starting date	Place	Type	Course duration	Language	Price without VAT
	Upon request		Upon request	21 hours	en	1 800 EUR	Register
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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

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.

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