The course introduces concepts in applied statistics. It overviews frequentist and Bayesian characterization of uncertainty for continuous and categorical data, principles of experimental design, and methods of causal inference. The course discusses the methodological foundations, as well as issues of practical implementation and use.
The methods discussed in the course are useful in any area of science and industry that collects and analyzes data. First, many areas rely on empirical research. Students working in these areas will benefit from a formal exposure to the scientific method, principles of experimental design, and analysis of data from designed and observational studies. Second, the success of most new scientific methods is determined by the quality of their evaluation. The concepts presented in this course will help students design method evaluation experiments, and analyze the resulting data.
Instructor: Prof. Olga Vitek Email: o.vitek@neu.edu Office hours: Tue and Fri 11:30am-12:30pm, or by appointment. WVH 310F. Mailbox: WVH 202
Teaching assistants: Mr. Sicheng Hao, Email: hao.sic@husky.neu.edu Office hours: Mon 10-11am, or by appointment. WVH 310.
Course policies and administration: Syllabus, Piazza, Blackboard. Academic integrity policy is strictly enforced.
(KNNL) Applied Linear Statistical Models. Kutner, Neter, Nachtsheim, Li, McGraw-Hill, 5th Edition, 2004. Datasets. (Agresti) Categorical Data Analysis. Agresti, Wiley, 3rd Edition, 2013. (Gelman) Bayesian Data Analysis. Gelman, Carlin, Stern, Rubin, Chapman & Hall/CRC, 3rd Edition, 2013.
(Faraway) Extending the linear model with R. Faraway, Chapman & Hall/CRC, 2006. (Albert) Bayesian computation with R. Albert, Springer 2007.