Overview
Description
This course provides a comprehensive overview of basic and advanced topics in regression analysis. It emphasizes statistical theory, research design, and practical applications in empirical research, along with hands-on development of procedural and computer skills. Topics include simple and multiple regression, diagnostics and remedial measures, model selection, generalized linear models, and path analysis.
Units
Lecture3
Catalog Details
Offering
Offered: Every Spring
Terms
spring
Attributes
Standard
Learning Outcomes
- describe and interpret concepts and theories involved in linear regression models (both simple and multiple) and generalized linear models (e.g., logistic, probit, and poisson).
- identify and appropriately utilize complicated models that involve polynomial or interaction terms.
- apply assumption checks and outlier checks for regression models, including conducting appropriate model diagnostics and using proper remedial measures.
- conduct model selection with attention to various model fit indices.
- use appropriate regression analysis and to write up results and discuss the implications in research manuscripts.
- utilize statistical software (e.g., R) to conduct statistical analysis.