The Advanced Statistical Modeling rubric is designed to provide students with a comprehensive understanding of modern statistical techniques and their applications in research and industry. This course covers advanced topics such as generalized linear models; mixed-effects models; Bayesian inference; and machine learning approaches to statistical analysis. Students will develop the ability to select; implement; and interpret sophisticated modeling techniques to address complex real-world problems. Through a combination of theoretical instruction and hands-on practice; students will gain proficiency in using statistical software to fit; evaluate; and refine models. The course emphasizes critical thinking; enabling students to assess model assumptions; diagnose issues; and justify methodological choices. By working with real datasets; students will learn to communicate statistical findings clearly and effectively to both technical and non-technical audiences. For those pursuing the research pathway; the rubric includes training in developing novel statistical methodologies and evaluating their performance through simulation studies. Taught students will focus on applying existing methods to diverse domains such as healthcare; finance; and social sciences. Both pathways foster a deep appreciation for the ethical considerations and limitations inherent in statistical modeling. Upon completion; students will be equipped with the skills to contribute meaningfully to data-driven decision-making in academic; governmental; or corporate settings. The course prepares graduates for further doctoral study or careers as statisticians; data scientists; or analysts; where advanced modeling expertise is highly valued. The emphasis on rigor; reproducibility; and practical application ensures that students leave with a strong foundation for tackling contemporary statistical challenges.
