• Students will be exposed to realistic statistical and scientific problems which appear in typical interactions between statisticians and researchers.
• The lectures will be centered around case studies. Ideally some case studies should be presented by invited speakers from the business and scientific community.
• The first part of the course will be dedicated to cover the practice of applied regression analysis and design of experiments, together with basic SAS and SPSS programming.
• An important objective of the course is to help develop communication skills, both written and verbal. All student will be required to do presentations and to write reports.

Consulting teams are given an assignment for every case study, with the full data set and they must produce a report and in most cases give a presentation. For every case study the teams have a leader that will give the presentations and write the report. The position of team leader will rotate for different case studies.

It is of great importance to identify good case studies since they are the centerpiece of the course. We have decided to consider case studies from the following groups.

• GROUP I : Simple real case studies where the answer is well known, but with some interesting statistical and scientific issues to be discussed.

 

 

Case studies of Group I will be presented in a one hour lecture followed by a discussion. As a result of the discussion each team is assigned a project. The data for the case study must be available to the student.

The following week the students will present the team reports.

• GROUP II : Real cases, complicated studies where it was hard to pick up the statistical problem but where the students have to do a lot of thinking and the answer may or may not be well known.

 

 

Case studies of Group II will be presented in a one hour lecture followed by a discussion. As a result of the discussion each team is assigned an initial project. The data for the case study must be available to the student.

The following class the students will present the preliminary reports and continue the discussion of the principal issues and come up with the final objectives.

On the third class they will do the final presentations.

• Week 1. Introduction and organization of the course. The students will form consulting teams that will work together in the preparation and presentation of several reports involving the analysis of case studies.

History of Science and the role of Statistics. Introduction to the scientific method.
References: "Statistics: A guide to the unknown", J. Tanur, et al. , "A history of the Sciences" Mason.
• Week 2. Statistical Consulting Environment. The role of the statistician within a scientific environment. Communicating with researchers from other areas. Report writing.
• Week 3,4,5. Methodological aspects.

An review of Statistical tools that will be used through the class.
An overview of computational tools that are available to the students.
• Statistical software such as SAS, and SPSS.
• For the advance students I will give notes in Splus, C and Fortran libraries for scientific programming such as CMLIB, Numerical Recipes, IMSL.
List of references for all the topics. The students will have access to a small selection of books on statistical methodology. This will be done through the departmental library and the math library reserve.
• Week 6. Case Study 1(from Group I). Introduction to case studies. Description of the procedure that will be followed for case studies. Format for report writing and presentations by students.

 

 

Presentation of Case Study 1. Discussion.

Assignment of projects to the student teams.

• Week 7 and so on. Case Study 2(from Group I).

 

 

Presentations by consulting teams of the reports from Case Study 1.

Presentation of Case Study 2. Discussion.

Assignment of projects to the student teams.
 




RECOMMENDED BOOKS:

1. Frank C. Dilorio.
             SAS, Applications Programming. A Gentle Introduction.  
             PWS-Kent. 

2. D. J. Hand and B. S. Everitt (eds) (1987),
            The statistical consultant in action,
                Cambridge University Press.

3. J. Tanur, et al (eds) (1978),
                Statistics: A Guide to the Unknown, (2nd ed),
                Holden-Day.

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

BIOSTATISTICS:

5. R. G. Miller, B. Efron, B. W. Brown and L. E. Moses (eds) (1980),   Biostatistics Casebook,
                 John Wiley.

PHARMACOSTATISTICS:

6. K. E. Peace (ed) (1988),   Biopharmaceutical statistics for drug development,   Marcel Dekker.

DATA:

7. D. F. Andrews (ed)    Data, Springer-Verlag.