This course page is obsolete.  I'll prepare a new page next time I teach the course.

• Office hours. Semester schedule

• Course Hours: MWF 11, BP 326

• Description of this course: The emphasis of this course is to develop the fundamental statistical concepts of inference and hypothesis testing from a classical perspective using the tools of probability theory. Topics investigated include sampling and sample distributions, graphical data analysis, point and interval estimation, hypothesis testing and an introduction to Bayesian inference.

  Prerequisite: Math 217, Econ 260, or permission.

  See also Clark's Academic Catalog

• Textbook: Statistics and Data Analysis by Ajit C. Tamhane and Dorothy D. Dunlop, Prentice-Hall, 2000. ISBN 0-13-744426-5. Prentice-Hall's web site for the book.

• Course goals.
  • To provide students with a good understanding of the theory of statistics as described in the syllabus.
  • To show how statistical methods depend on the theoretical principles of probability.
  • To illustrate the use of statistics in science, both physical and social science
  • To examine the assumptions of statistical methods.
  • To make connections between statistics and other branches of mathematics, and to see some of the history of statistics.
  
  
• Syllabus

• Assignments, quizzes, tests, final: There will be numerous short assignments, mostly from the text, occasional quizzes, two tests during the semester, and a two-hour final exam during finals week.

  Practice problems will be assigned daily from the text to help you master the concepts discussed in class. Periodically, problems will be assigned to be turned in and graded. Although not all practice problems will be submitted for a grade, it is expected that you will keep up to date on the problems. Collected homework is due in class on the assigned day. No late assignments will be accepted.

  Tests are closed notebook, but you may bring one sheet of notes and a calculator. In the event that an emergency arises, you are responsible to contact me before the regularly scheduled exam to make alternative arrangements.

• The course grade will be determined as follows: 2/9 assignments and quizzes, 2/9 each of the two midterms, and 1/3 for the final exam.

• Math 217 class notes from Fall 2007

• Monday, Jan. 14. Welcome! Discussion of statistics (Chapter 1).
• Wednesday, Jan. 16. We'll review some high points of probability theory (Chapter 2). The purpose of this review is just to explain what I assume you already know. We'll start the summary today and continue it later.
  Summary of basic probability theory, part 1.
  Summary of basic probability theory, part 2.
  Summary of basic probability theory, part 3.
  Common probability distributions and a summary table for them. See also the Gallery of distributions at http://www.itl.nist.gov/div898/handbook/eda/section3/eda366.htm.
• Friday, Jan. 18. Notes.
  Begin a discussion of collecting data: types of statistical studies, control groups in a comparative study, sample surveys, prospective and retrospective studies, basic sampling designs, simple and other kinds of random sampling. (Chapter 3)
• Monday, Jan. 21. No class. Martin Luther King Day.
• Wednesday. Jan. 23. Notes.
  Experimental design.
• Friday, Jan. 25. Notes.
  Survey of the chapter on summarizing and exploring data.
  Due: From page 101, exercises 1, 3, 4, 7, 8, 9, 12, 13, 15.
• Monday, Jan. 28. Notes.
  Complete discussion on summarizing and exploring data. Distribution of the sample mean, central limit theorem, approximation of the binomial distribution by a normal distribution.
  Due: From page 105, exercises 19, 20.
• Wednesday, Jan. 30. Notes. Sample variance, the chi² distribution, the gamma function.
• Friday, Feb. 1. Notes. Student's t distribution and Snedecor-Fisher's F distribution.
  Due: From chap. 5, p. 189, exercises 4, 5, 8, 14.
• Monday, Feb. 4. Notes. Inferences about parameters based on data from a sample.
  Due: From chap. 5, p. 191, exercises 16, 17, 23.
• Wednesday, Feb. 6. More on point estimators.
• Friday, Feb. 8. Maximum likelihood estimators for discrete distributions. Notes.
  Due: From chap. 6, p. 229, exercises 1, 2ab, 5, 7.
• Monday, Feb. 11. No class.
• Wednesday, Feb. 13. Maximum likelihood estimators for continuous distributions, confidence intervals. Notes.
• Friday, Feb. 15. Confidence intervals for Bernoulli trials. Notes.
  Due: From chap. 15, p. 658, exercises 1, 3.
• Monday, Feb. 18. Review.
  Due: From chap. 6, p. 230, exercises 11, 13a, 14, 15ab.
• Wednesday, Feb. 20. First test on chapters 3, 4, and 5. Answers.
• Friday, Feb. 22. More on hypothesis tests. Notes.
• Monday, Feb. 25. Normal sample with known variance, any large sample: z-confidence intervals and hypothesis tests, hypothesis tests for the mean. Notes.
  Due: From chap. 6, p. 231, exercises 17-20, 22.
• Wednesday, Feb. 27. Small normal sample: t-distribution.
• Friday, Feb. 29.
  Due: From chap. 7, p. 262, exercises 1, 2, 9, 11, 12.
• March 3-7. Midterm break.
• Monday, Mar. 10. Inferences on variances. Chi²-intervals and tests. Notes.
• Wednesday, Mar. 12. Inferences for two samples. Notes.
• Friday, Mar. 14.
  Due: From chap. 7, p. 265, exercises 17, 18, 19.
• Monday, Mar. 17. Inferences with matched pairs design. Notes.
  Due: From chap. 8, p. 290, exercises 1, 2, 3, 9.
• Wednesday, Mar. 19. Inferences for proportions and count data. Notes.
• Friday, Mar. 21. Inferences for comparing two proportions. Introduction to the method of least squares. Notes.
• Monday, Mar. 24.
• Wednesday, Mar. 26. Second test on chapters 6, 15, 7, and 8. Answers.
• Friday, Mar. 28. The model for simple linear regression.
• Monday, Mar. 31. Analysis of the simple linear regression model. Notes.
• Wednesday, Apr. 2. Estimating the error variance σ² of the model. Statistical inferences based on the model. Notes.
• Friday, Apr. 4. Confidence and prediction intervals for simple linear regression, regression diagnostics; begin multiple linear regression. Notes.
• Monday, Apr. 7. Vectors and matrices. The multiple linear regression model in the notation of linear algebra. Notes.
• Wednesday, Apr. 9. More on multiple linear regression, intervals and hypothesis tests.
• Friday, Apr. 11. Introduction to Bayesian statistics, the Bernoulli process. Notes.
• Monday, Apr. 14. Student presentations on linear regression. Bayesian statistics and the Bernoulli process. Notes.
• Wednesday, Apr. 16. Bayesian analysis of the Poisson process.
• Friday, Apr. 18. Bayesian analysis of the normal process. Notes.
• Monday, Apr. 21. More on the normal process.
• Wednesday, Apr. 23. Student presentations. Chapter 11, exercises 2, 3, 4, 10, 15.
• Friday, Apr. 25. Student presentations. Review.
• Monday, Apr. 28. Last meeting. Take-home Final exam

This page is located on the web at

http://aleph0.clarku.edu/~djoyce/ma218/

David E. Joyce