R統計回歸模式於環境流行病學之應用

1. Course Objectives: After taking the course, students will be able to 1. Use R program for statistical analysis and apply frequently used statistical models on their own research, 2. Learn how to model continuous exposures and outcomes in an advanced fashion (e.g. splines, polynomial regression), 3. Deal with data when the underlying statistical assumptions do no hold, 4. Apply state-of-art models in epidemiological studies. 2. Course Format For each session, a 2-hour lecture will be delivered followed by a 1-hour hands-on analysis using R statistical program. Students will be able to implement their own codes for statistical analysis taught in the class. 3. Homework Two homework assignments will be distributed throughout the course. Each assignment is based on the material covered in the previous topics. Students are strongly recommended to work in group but must hang-in their own answers along with R codes. 4. Final Project Students will work in groups to frame their major scientific questions based on the environmental dataset distributed at the beginning of the semester. Each group will propose their own scientific objectives by the 1st Progress Report. Preliminary results will be shown at the 2nd Progress Report. At the last week of the semester, each group will present their project in the class, and hang-in their final report within one week. The grade of final project will be based peer’s and instructor’s evaluation. 5. Technical Discussion (2 times) Each group will have two face-to-face discussion sessions with the instructor regarding data management, coding issue, analysis plan, and result throughout the semester. All details of statistical analysis will be discussed in a group-based fashion.

60% Final project (a real-world dataset will be distributed for analysis) 20% Two homework assignments (10% for each) 20% Class Participation