## categorical data analysis ii - sta541

### Course Objectives

• Determine the correct statistical analysis for a given set of data.
• Utilize statistical software to analyze linear models and correctly interpret the output.
• Utilize statistical software to perform goodness of link assessment for logistic regression model, RECEIVER OPERATING CHARACTERISTIC CURVES (ROC’S), and OPTIMAL OPERATING POINTS (OOP’s), and correctly interpret the output.
• Utilize statistical software to analyze Count outcomes using Poisson Regression, Negative Binomial Regression models, and Generalized Poisson regression models to address under-dispersion and over-dispersion for count data and correctly interpret the output.
• Utilize statistical software to analyze inflated Count OUTCOMES models, though ZERO-INFLATED OUTCOME TECHNIQUES AND ZERO-ALTERED MODELS, and correctly interpret the output.
• Utilize statistical software to perform FINITE MIXTURE MODELS to accommodate multimodal outcomes.
• Discuss goodness-of-fit techniques for ZERO-ALTERED, ZERO-INFLATED MODELS, AND FINITE MIXTURE MODELS through VUONG’S TEST.
• INTRODUCTION TO EXTENSION OF TECHNIQUES TO REPEATED MEASURES DATA.
• INTRODUCTION TO CATEGORIZED TIME TO EVENTS.
• Communicate the results of these statistical analyses in a concise, simple way that would be understandable to a non-statistician.

### Course Topics

Students completing this course should:

• Be competent on the analysis of categorical outcomes presented in STA 507.
• Be competent in logistic and categorical regression modeling, building, and diagnostics.
• Understand the difference between underdispersion and overdispersion.
• Be introduced to inflated outcomes.
• Be introduced to the Receiver Operating Characteristic (ROC) curves and Optimal Operating Points (OOP).
• Be introduced to Mixture Models.
• Demonstrate competence with PROC LOGISTIC, PROC GENMOD, PROC COUNTREG, PROC FMM , PROC NLMIXED, and PROC GLIMMIX.