STA 213
Introduction to Statistical Methods
Syllabus

Duke University
Fall 2005
MW 2:50-405
04 Sanford

Instructor:
Michael Lavine
Office: 218 Old Chemistry Building
Phone: 684-2152
Email: michael@stat.duke.edu

Office Hours: by appointment or drop in


Teaching Assistant:

Name
Email
Phone
Office Hours
Office Location
Natesh Pillainatesh@stat.duke.edu 1:00-3:00 MWOld Chem 211 B
Scotland Lemanscl13@stat.duke.edu 1:00-3:00 TThOld Chem 211 B

The Course:

The required text is Statistics, 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.

The text recommended for further reading is Statistical Inference by George Casella and Roger Berger. The second text contains more mathematical details than the first. Those of you who plan to take subsequent courses in Duke Statistics should become familiar with the details in the second text. I plan to cover Chapters 1, 2, 3, 4, 5 and 7 of Statistics which correspond roughly to Chapters 2-7 of Statistical Inference.

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 Duke Statistics computers, the Duke computers, and is 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 weekly homeworks. There will not be quizzes or exams. From the homework problems assigned each week I will select one 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 won and should flow from your understanding of the problem. Grades will be based on homeworks.

Advice:

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 Aug. 22, 2005 by Michael Lavine