## Categorical Data Analysis Using Logistic RegressionThis course focuses on analyzing categorical response data in scientific fields. The SAS/STAT procedures addressed are PROC FREQ, PROC LOGISTIC, 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.
- 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 logit plots
- 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.
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.
This course addresses SAS/STAT software. 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
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