Instructor: Calvin L. Williams, Ph.D. | Course: Statistical Inference |
Office: 0-323 Martin Hall | Class Location: M-204 Martin Hall |
Telephone: 656-5241 | Class Time: 9:30-10:45 TTH |
E-mail: calvinw@math.clemson.edu | Office Hours: MWF: 2:00-3:30 or By Appt. |
Course Web Page: | http://www.ces.clemson.edu/ ~ calvinw/mthsc804.html |
There are three main subdivisions within statistics: efficient summarization, tabulation and graphical display of data; design of experiments; and statistical inference. Data summarization was historically the first major statistical activity. Experimental design is of crucial importance before data are collected. However, it is statistical inference which has seen most research and practical application in recent years, and it is inference which forms the direction of this course. There are three main types of inference, namely point estimation, interval estimation and hypothesis testing. In point estimation, for each unknown parameter of interest a single value is computed from the data, and used as an estimate of that parameter. Instead of producing a single estimate of a parameter, interval estimation provides a range of values which have a predetermined high probability of including the true, but unknown, value of the parameter. Hypothesis testing sets up specific hypotheses regarding the parameters of interest and assesses the plausibility of any specified hypothesis by seeing whether the observed data support or refute that hypothesis. Although hypothesis testing can often be artificial in the sense that none of the proposed hypotheses will be exactly correct (for example, exact equality of p for two species of birds is unlikely), it is often a convenient way to proceed and underlies a substantial part of scientific research.
The statistical community has during the last 10 years experienced a significant transformation stimulated by the technological developments in statistical computing environments, theoretical developments in stochastic based inference and simulation.
Students will learn how to use statistical software to facilitate the understanding of the foundations of multivariate analysis. Statistical packages will include SAS, S-Plus, and MatLab.
Topics to be covered include:
There will be two 60 minutes in class examinations and a final examination. No makeup examinations will be given. Any student who misses an examination without a legitimate excuse,ie, a documented medical excuse, will receive a score of zero for that exam. A student with a legitimate excuse, will receive a final score based on all other class work. More than one missed exam with require withdrawal from the course and/or the receipt of a failing final grade.
There will also be several homework sets and/or take home projects assigned from the text as well as from material covered during class. Although it is imperative that each student be completely comfortable with these assigned problems and projects, group study is encouraged.
This project is an opportunity to use the statistical techniques we have learned in class, to answer real-life questions. Projects should be done individually. Each student should:
You will have about 2 months to layout your project. Plan your time accordingly.
The grade will be based on the final report, which should contain the following items.
Reports should be neatly typed, well-organized and attractive. Graphical displays (either computer-generated or hand-drawn) are encouraged. Generally, graphs are more effective if they are incorporated into the text, rather than hidden at the end of the report. You may also use a computer package to aid in the data analysis. If you do so, the results should be discussed in the text of your report, and the computer output itself may be included in an appendix.
A rough draft of the final report will be due approximately 2 weeks before the final report is due.
The project is worth 100 points. Grades will be based on:
Appropriate and correct procedures | 50 pts |
Well-written and attractive presentation | 20 pts |
Grammar, spelling and punctuation | 20 pts |
Complexity | 10 pts |
A project proposal (not graded) must be approved before the project is started. An approved proposal must be turned in with the final report. The proposal should state:
Due dates:
Proposal | November 4^{th} |
Rough draft | November 18^{th} |
Final Report | December 4^{th} |