STA 114 --- MTH 136
Statistics
Syllabus
Duke University
Spring 2006
TTh 2:50-405
136 Social Sciences
|
Instructor:
| Michael Lavine
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|---|
| Office: 218 Old Chemistry Building |
| Phone: 684-2152 |
| Email: michael@stat.duke.edu |
Office Hours: by appointment or drop in
Teaching Assistant:
|
Name
|
Email
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Office Hours
|
Office Location
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|---|
| Rahul Satija | rahul.satija@duke.edu |
11-1 Th; 12:50-2:50 Tu | 211 Old Chem |
The Course:
The required text is Introduction to
Statistical Thought, written by me and available for free
download as a pdf file. The book has been only partially written.
Expect mistakes and omissions. Please help me improve it during the
semester. I plan to cover Chapters 2 through 5 and parts of others.
We will take both a theoretical and a computational approach.
Calculus will be important. I will write down integrals and
derivatives and expect you to know what they mean. We will also use
the computer software R which is on the ISDS computers, the
Duke computers, and available for free download at http://www.r-project.org/. Using
R will not only give you familiarity with a statistics package;
it will help you learn the theory better.
I hope you
will feel free to talk to me anytime about the class, either with
questions about the material or with comments about the text or the
way the course is conducted.
Assignments and Grading:
There will be quizzes approximately every week and homeworks. I
do not plan on giving exams. From the homework problems assigned each
week I will select one or more to be graded. You may discuss homework
problems together, but when you write them up the work must be your
own. You should not derive a solution together and then merely copy
it. You may work together to the point of understanding a problem.
But when you write it up the language must be your own and should flow
from your understanding of the problem. Grades will be based on
homework and quizzes.
Advice:
Other Material:
Denby and Pregibon
What is Bayesian Statistics
Read the book. Work lots of problems. Write code in R.
Write clearly, succintly and to the point. Work lots of problems.
Write code in R. Discuss with your colleagues. Work lots of
problems. Write code in R.
Last updated Jan. 11, 2006 by Michael Lavine