This course introduces students to concepts and techniques of Classical and Bayesian approaches for modern regression and predictive modelling. The course will blend theory and application using a range of examples. Topics include exploratory data analysis techniques and visualization of data, multiple linear regression and model building, predictive distributions, penalized and Bayesian estimation, model selection and model uncertainty including variable transformations, variable selection, and Bayesian model averaging, diagnostics and model checking, robust estimation, and hierarchical models.
All students should be comfortable with linear/matrix algebra and mathematical statistics at the level of STA 611 (Statistical Inference - Casella and Berger is an excellent resource) and familiar with the R programming language and should be familiar with linear regression. Students should be familiar have taken the introduction to Bayesian inference STA 360/601/602 or are currently co-registered in the course. Please see me if you have questions about the pre-requisites.
|Professor||Dr. Merlise Clydeemail@example.com|
|Professor Clyde||Old Chemistry 214A||after class Wed & Fri 11:30-12 and 2-3 pm|
|Eric Wang||Old Chemistry 211A||Thur 9-10 am|
|Liyu Gong||Old Chemistry 211A||Tues 4:30-6:30 pm|
Syllabus and other course information and policies are available on the Course Syllabus
Lecture slides, reading assignments, homework, and other materials are available on the Calendar
Links to Online Course Textbooks and other references, software and other useful resources are provided on the Resource menu
Link to Sakai allows access to Video Lectures, Gradebook, the Piazza discussion forum
Github for Class Github Organization for Team coding submissions
This course has achieved Duke’s Green Classroom Certification. The certification indicates that the faculty member teaching this course has taken significant steps to green the delivery of this course. Your faculty member has completed a checklist indicating their common practices in areas of this course that have an environmental impact, such as paper and energy consumption. Some common practices implemented by faculty to reduce the environmental impact of their course include allowing electronic submission of assignments, providing online readings and turning off lights and electronics in the classroom when they are not in use. The eco-friendly aspects of course delivery may vary by faculty, by course and throughout the semester. Learn more at http://sustainability.duke.edu/action/certifications/classroom/index.php.