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BAD 64013 Summer 2006 Booth

BUSINESS ADMINISTRATION
NON-PARAMETRIC STATISTICS
64013/74013
 
Instructor:              Dr. David Booth
Office:                   A428 BSA
Phone (Office):     672-1143
Office Hours:        TBA
e-mail: dbooth@bsa3.kent.edu
Please note that if I am not in my office at these times you will find a note on my door telling you where I am. Please then go to that location to see me. Please feel free to call me or leave a note in my mailbox if you need to contact me.
 
Textbook: S.J. Richter & J.J. Higgins, A SAS Companion for Nonparametric Statistics, Duxbury
E. S. Keeping, Intro. To Stat. Inference, Dover
 
Course objectives:
 
At the end of the course the student will have:
1)      Learned the basic methods of nonparametric statistics, both classical & modern.
2)      Learned to apply these basic techniques to real situations
These skills will prepare you for more advanced work in your major, either in college or on the job.
 
Attendance and Make-up Policy:
 
In general, students are expected to attend class and are responsible for any material discussed and/or assigned. With respect to make-up, the general policy is no make-up of missed work (including exams) is allowed, and no late work will be accepted. The only exceptions are:
1)      A prearranged situation (e.g., course field trips, athletic trip, etc.)
2)      Emergency illness, death in the family, etc., in this case the instructor should be notified as soon as possible.
3)      Contact the instructor early
 
Performance Evaluation:
 
There will be 1 hourly examination, worth 100 points Exam formats will be open book.
There will be a term paper on a parametric or semi-parametric method, of your choice, that is not covered in the textbook. The purpose of this paper is to help you in the future when you have to use a new statistical method.  The paper will be worth 100 points.
Academic dishonesty, in all forms, is prohibited. All material handed in is in the public domain. This syllabus is a guide, not an absolute contract. The grading scale is 90+ A, 80+ B, etc.
 
Students with Disabilities:
 
In accordance with University policy, if you have a documented disability and require accommodations to obtain equal access in this course, please contact the instructor at the beginning of the semester or when given an assignment for which an accommodation is required. Students with disabilities must verify their eligibility through the Office of Student Disability Services (SDS) in the Michael Schwartz Student Services Center (672-3391).
 
 

Tentative Course Outline

 
Section
Topic & References
Suggested Problems
 
 
 
Classical 1 and 2 sample statistics.
Class Notes & Chapts 10&11 of E.S.Keeping, Intro. To Stat. Inference,QA278.K25, along with parts of the text.
 
 
 
 
Robust Statistics and nonparametric regression
UMAP Module 626, COMAP
 
 
 
 
Generalized Additive Models and Nonparametric regression
T.Hastie & R. Tibshirani, Generalized Additive Models, Dekker( QA276 H387X 1990), and smoothing articles of the instructor’s
 
 
 
 
Bootstrap & Permutation Tests
Higgins, Intro to Modern Nonparametric Stats
 
Neural Networks as Nonparametric Regression
Lee, Bayesian Nonparametrics via Neural Networks;Hastie,Tibshirani & Friedman, The Elements of Statistical Learning; instructor’s papers
 

 

 
 

 

 
 

 

 
 

 

 
 

 

 
 

 

 
 

 

 
 

 

 
 

 

 
 

 

 
 

 

 
 

 

 
 

 

 
 

 

 
 

 

 
 

 

 
 

 

 
 

 

 
 

 

 
 

 

 
 

 

 
 

 

 
 

 

 
 

 

 
 

 

 
 

 

 
 

 

 
 

 

 
 

 

 
 

FINAL EXAM

 
 

 

 
 
 
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