Statistical Consulting

Course Announcement

Time: Monday, Wednesday, Friday at 2:00 - 2:50 p.m.
Location: Taft Hall 215

Instructor: Jie Yang, SEO 513
Office Hours: Monday, Wednesday, Friday at 11:50 a.m. - 12:50 p.m.

Teaching Materials: Lecture notes and handouts
Reference Books:

• Javier Cabrera, Andrew McDougall, Statistical Consulting, Springer, 2010.  Dataset.
  
• Sanford Weisberg, Applied Linear Regression, 3rd edition, Wiley, 2005.  R Package.
  
• John Fox, Sanford Weisberg, An R Companion to Applied Regression, 2nd edition, SAGE, 2011.  R package.
  
• Peter McCullagh, John A. Nelder, Generalized Linear Models, 2nd Edition, Chapman & Hall, 1989.   Data Sets.
  
• Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edition, Springer, 2009.  R package.
  
• David J. Hand, Heikki Mannila, Padhraic Smyth, Principles of Data Mining, MIT Press, 2001.
  
• David R. Cox, Nancy Reid, The Theory of the Design of Experiments, Chapman & Hall, 2000.   R code.

Projects: Students are required to work in groups on course projects and submit their reports. The group size can be 1, 2 or 3. The reports should be submitted in class on the due day. For each project problem, a report should include statement of the problem, model selections and verification, conclusion, suggestion and discussion if necessary. Any statistical software may be used for course projects, although R program is recommended and illustrated. If R program is used, the attachment of R code is recommended. The list of names of the group members should be part of the cover page of the report.
Presentation: Students are required to present their course projects at least once during the course period. The presentations should be done in class or in office hours. Each presentation may last up to 30 minutes.
Exam: November 13rd, Friday, 2:00 p.m. - 2:50 p.m.   No makeup exam will be given without a valid excuse..
Grading Policy: Exam: 25 points; three course projects: 10 points each; presentation: 20 points; one final project: 25 points.
Grading Scale: 95% A , 85% B , 75% C , 65% D

Course Syllabus

Project

• Project 1: Horse Kick, due 09/14/2015
  
• Project 2: Berkeley Guidance Study, due 10/05/2015
  
• Project 3: Beetle Mortality Data, due 10/26/2015
  
• Final Project: Judge-Jury Agreement, due 11/25/2015
  

Using R

• Download R for Free -- the most popular software used by statisticians
  
• Learn R in 15 Minutes
  
• Use R to Compute Numerical Integrals
  
• Downloadable Books on R:
  • An Introduction to R, by William N. Venables, David M. Smith and the R Development Core Team
    
  • Using R for Data Analysis and Graphics - Introduction, Code and Commentary, by John H. Maindonald
    

    More R Books in Different Languages ...

  
  
• R Code for the Course:
  • Introduction to R
    
  • R tips
    
  • Examples for hypothesis testing
    
  • Examples for scatter plot and missing data , data with missing values
    
  • Examples for separated points and data transformation
    
  • Examples for multiple linear regression and variable selection , pavement data
    
  • Bronchitis Example for generalized linear models, bronchitis data
    
  • Budworm Data Example for generalized linear models
    
  • Ship Damage Data Example for generalized linear models
    

More References

• Gopal K Kanji, 100 Statistical Tests, 3rd edition, SAGE, 2006.