Department of Statistics Home

M.S. Applied Statistics Exam Syllabus

M.S. Exams
Applied | Theory | Thesis


Background

Statistical competence demands solid grounding in theory from which statistical methods derive and familiarity with the application of these methods. This exam requires demonstration of the ability to apply statistical methods and communicate results. It consists of two parts: (1) analyzing data and submitting written reports, and (2) answering oral questions on the data analysis.

In the References there are a number of items which present informative case studies and these may be selectively and critically read. Cox and Snell is particularly appropriate, since many of their examples derive from the annual practical exam at Imperial College. The Canadian Journal of Statistics has a section which presents worked out analyses of consulting type problems.

This exam is oriented toward the applied course work of M.S. students. Specifically, the methodology in STAT 502, 504 are assumed. STAT 527, 536, 570-571, while not required for the Exam, provide useful background and experience.

Written Reports

The exam requires a written report on the analysis of a given dataset. A copy of each dataset will be made available to each student. The reports should be prepared for a critical client who is unacquainted with statistical theory, computer technology, and statistical jargon. Each report should be limited to at most 20 pages including appendices and figures and should include most of the following sections (you have some flexibility here; what is suggested is a compromise with the recommendations of Ehrenburg, 1982):

  1. Abstract: This should consist of a brief statement of the results of your investigation (not the objectives).
  2. Introduction: This should include a clear statement of the examinee's interpretation of the scientific questions addressed by the data. Background provided with the data may need to be amplified. The goal of the statistical analysis should be clear to all who read the introduction.
  3. Methodology: Describe precisely the models used, any theory developed to implement these models, the methods used to analyze these data assumptions, and so on. Explain why the methods are appropriate.
  4. Results: Results of analyses should be presented simply and clearly. Use graphical displays tables and discussion. Undigested computer output is not appropriate here. Reference to labeled points in computer output in the appendix is suitable.
  5. Discussion: This section should describe the scientific and statistical issues raised by the results described in the preceding sections. Limitations in data, scientific background and statistical expertise should be pointed out. Suggestions for further analysis or other data are appropriate. Summarize (again) your conclusions about the issues of the scientific concern. Back up your assertions with references to your Results, graphs, tables, etc.
  6. Appendices: There can be one or more appendices.

Criteria used to judge performancewill include the following factors:

Oral Exams

After the report is graded, the examining committee meets each student in an oral wrap-up. This oral questioning provides an opportunity to explore various points in greater depth and to clear up questions such as those relating to choice of models, priorities and analyses not reported. Questioning is not necessarily limited to just those topics covered by the student in the written portion of the exam. The examining committee will provide the student with a written evaluation of the students analysis.

Taking the Exam

Picking up a copy of the exam constitutes "taking the exam." There will be a due date for the written reports and failure to turn in the reports will constitute failure. Errata sheets will be accepted by the committee at the time of the oral exam. Obviously, such errata should be kept to a minimum.

References

Cochran, W.G. (1983). Planning and Analysis of Observational Studies. New York: Wiley.

Cox, D.R. and Snell, E.J. (1981) Applied Statistics: Principles and Examples. London: Chapman and Hall.

Ehrenburg, A.S.C. (1982). Writing Technical Papers or Reports. The American Statistician, 36, 326-329.

Miller R.C. Jr., Efron B., Brown B.W. Jr. and Moses, L.E. (1980). Biostatistics Casebook. New York: Wiley.