R統計軟體在環境衛生之資料分析

For each session, a 1-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. 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), 3. Deal with data when the underlying statistical assumptions do no hold.

not required

6. Technical Discussion (2 times) Each group will have three face-to-face discussion session with the Instructor regarding data management, coding issue, analysis procedure, and result throughout the semester. All details of statistical analysis will be discussed in a group-based fashion. 7. Optional Topic (2-3 times) Since the background of students in class varies every year, then this course will provide 2-3 OPTIONAL topics that will be delivered given students’ need. These topics may include causal mediation analysis (CMA), GEE, mixed model, time-series analysis, PCA, survival analysis, bootstrap, and so on. Students are encouraged to request the new topics that fit their research interests. 7. Optional Topic (2-3 times) Since the background of students in class varies every year, then this course will provide 2-3 OPTIONAL topics that will be delivered given students’ need. These topics may include causal mediation analysis (CMA), GEE, mixed model, time-series analysis, PCA, survival analysis, bootstrap, and so on. Students are encouraged to request the new topics that fit their research interests.

3. Grading Criteria: 60% Final project (real-world dataset will be distributed for analysis) 20% Two homework assignments (10% for each) 20% Class Participation 4. 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. 5. 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.

Course Text:  Peter Dalgaard. Introductory Statistics with R (Paperback) 2nd Edition. Springer-Verlag New York, Inc. ISBN 978-0-387-79053-4  Hothorn and Everitt. A Handbook of Statistical Analysis Using R, 3rd Edition. CRC Press. ISBN 978-1-4822-0458-2.  Edward McNeil. Data Management and Visualisation using R. ISBN 978-6-162-71594-5  P. M. McCullagh and John A. Nelder. Generalzied Linear Models. 2nd Edition. Chapman and Hall, London. ISBN 978-0-412-31760-6  T. J. Hastie and R. J. Tibshirani. Generalize Additive Models. Chapman and Hall, London. ISBN 978-0-412-34390-2  David Rupert, M. P. Wand, and R. J. Carroll. Semiparametric Regression. Cambridge University Press. ISBN 978-0-511-75545-3  W. N. Venables and B. D. Ripley. 2002 Modern Applied Statistics with S. 4th Edition. Springer. ISBN 0-387-95457-0 Other Useful Reference:  An Introduction to R. Online manual at R website at http://cran.r-project.org/manuals.html  Andreas Krause, Lelvin Olson. 2005. The Basics of S-PLUS. 4th Edition. Springer-Berlag, New York. ISBN 0-387-26109-5  Jose Pinheiro and Douglas Bate. 2000. Mixed-effects models in S and S-PLUS. Springer-Verlag, Berlin. ISBN 0-387-98957-9

地點

By appointment

時間

By appointment

聯絡方式

email: - wenchipan@nycu.edu.tw - wenchipan@post.harvard.edu