MIT
Department of Aeronautics and Astronautics
Statistical Methods in Experimental Design
Spring 2007 Schedule
16.470
Prof. M.L. Cummings
Tel: 2-1512
Office: 33-305
missyc@mit.edu
Office hours: By appt
Required Text: Applied Linear Statistical Models, 5/e, Kutner, Nachtsheim, Neter,& Li
Other useful texts: Schaum’s Outline of Theory and Problems of Statistics, Design and Analysis of Experiments, 5/e, Montgomery, and Experimental Design
Using ANOVA, Barabara G. Tabachnick & Linda S. Fidell
Course Objectives: For various designs of experiments, students will be proficient in generating relevant hypotheses to answer research questions, selecting
appropriate multilevel and /or multivariable statistical models, computing descriptive and inferential statistics, and effectively communicating these results
through interpretation and presentation of results. By the end of the course, students will be able to:
1. Explain the difference between descriptive and inferential statistics and provide examples of both;
2. Address assumptions and limitations of statistical models;
3. Formulate and apply hypothesis testing;
4. Compute required sample sizes for different effect sizes;
5. Conduct both parametric and nonparametric statistical analyses; models will include factorial ANOVA, randomized block designs, linear regression,
repeated measures models;
6. Assess whether or not the results statistically support the original research question;
7. Assess the validity of the use of various statistical tests in the literature (journal articles and conference papers)
8. Communicate interpretation of statistical results, both written and orally.
Date
Reading
Topic
Assignment
FEB 6
Appendix A1-A4
Introduction, Estimation, Describing Data
Review Slides
8
Appendix A5-A9,
website1,2
Statistical Inference & Hypothesis Testing
Case Study 1, See slides for additional problems
Ethical Implications of DOE
Social Science COUHES Training:
https://www.citiprogram.org/default.asp
15.2, 15.3, 15.5, 15.9, 15.11, 15.12, 15.23
13
15
1
2
CH15
DOE & Research Methods
http://en.wikipedia.org/wiki/Statistical_inference
http://www-micro.msb.le.ac.uk/1010/1011-20.html
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MIT
Department of Aeronautics and Astronautics
20
22
HOLIDAY
Regression & Correlation
MAR 1
CH 1, CH 2.1-2.3,
2.6-2.10
CH 6.1- 6.2, CH
7.4-7.6, CH 9.4 –
9.5
CH 16
6
CH 17
Treatment Comparisons
8
CH 18 excl. 18.7
ANOVA diagnostics & remedial measures
13
Chap 16, Sections
10 & 11, website3,
Cohen reading
Websites4,5
Power & Sample Size
16.24, 16.29, 16.33 a & b, plus additional problem on
slides **This homework is due 15 MAR
Survey Design
Survey Case Study, Survey Design (see slides), Case
Study 2 **This homework is due 22 MAR
27
15
20
22
Regression, cont.
ANOVA & Single Factor Studies
1.27, 1.43 & 2.62, 2.1, 2.17, 2.18, 2.28 a & c, 2.29 a, d,
e., 2.47. See addenda on slides.
6.3, 6.7, 7.12, 7.20, 7.24 a & b, 7.37 a. See addenda on
slides.
16.5, 16.8 & 10 (substitute box plots for aligned dot
plots), 16.14, 16.16, 16.43
17.9, 17.11, 17.16, 17.21, 17.36 (see slides for
additions)
18.15 a-d, 18.16, 18.38, 18.39 - see additions on slides.
Quiz #1
CH 19
Two factor studies, part 1.
27
SPRING BREAK
29
SPRING BREAK
APR 3
Test Review/Finish CH 19, Two Factor Studies
5
CH 21
Randomized complete block designs
10
CH 28.3 – 28.8
Latin Squares (Guest Lecturer: Dr. Natapoff)
12
pp. 87-89, Section
18.7, Schaum’s
addendum
Non-parametric statistics
1 page project proposal (due 3 APR)
19.4, 19.6, 19.7, 19.9, 19.11 (by hand/spreadsheet), &
19.38, 19.15 (with software), 19.32 a-d, 19.40, 19.51 &
52 (see notes on slides) **Homework due 5 APR
21.5 (a – c), 21.6, 21.9 (a – b), 21.10, 21.11, 21.14,
21.18, plus supplementary problem on slides.
Case Study 3
17.26, 17.34, 17.51, 17.69, 12.39, Supplementary
problem, and see additional notes on slides. **This
homework due 19 APR
3
http://www.cemcentre.org/renderpage.asp?linkID=30325016
http://www.utexas.edu/academic/diia/assessment/iar/how_to/methods/survey.php
5
http://www2.chass.ncsu.edu/garson/pa765/survey.htm
4
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MIT
Department of Aeronautics and Astronautics
PATRIOT’S DAY
17
19
CH 22
Analysis of Covariance
22.10, 22.16, 22.32 – see notes on slides
24
CH 24
Multifactor Studies
26
CH 25
Random & Mixed effects models
May 1
CH 26
Nested designs
3
CH 27
Repeated measures design
24.9 & 10, and 24.22 & 23 (see notes on slides), Case
Study 46
25.3, 25.12, 25.14, 25.15 (except d), 25.16 (except g)
25.24 (see notes on slides)
Suggested: 26.3, 26.4, 26.5 (for b, use either a boxplot
or a bar graph), 26.10, 26.11
Suggested: 27.1, 27.2, 27.14, 27.15 (columns are sales,
store, display, time), 27.19a-d, Case Study 5 (testable)
8
Quiz #2
10
Final Presentation
15
Final Presentation
17
Final Presentation


6
All written projects due
Homework Assignments: Homework will be due every Tuesday following the date of assignment. At the beginning of each class on Tuesday, a
student (randomly selected) will present the homework solutions to the rest of the class. This presentation will count towards the participation grade.
 Unless otherwise noted, assignments are individual efforts.
 Case Studies are papers that you are to analyze from a statistics viewpoint. The analysis is not a formal research report, bullets are fine, but
should address whether or not the general DOE was appropriate, were the appropriate statistical tests used (and if not, what should have been
used), were the results presented in a clear and concise manner, and what was the overall contribution of the statistical analysis to the main
theme of the paper?
Software: While statistical software will not be a requirement in this class, it is strongly advised that each student become familiar with the package of
their choice. Recommendations include:
 SPSS
 S+
 Minitab
 SAS (you need to have experience with this to be comfortable)
 Matlab (good but the user interface is not great)
 Ask about any other programs
www.dodccrp.org/events/2004_CCRTS/CD/papers/029.pdf
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MIT
Department of Aeronautics and Astronautics
Final Project: For the final project, students will develop a hypothesis, design of experiments, conduct the experiment, analyze the results, and communicate the
conclusions through both a written report in research paper format as well as in a presentation at the conclusion of the semester. Students are not required to
focus on human-computer interaction, and are encouraged to relate this assignment to a current research project. Students will submit a proposal for the project
mid-semester. Students can work in pairs on the project, and are encouraged to do so for the sake of data gathering, but each will turn in individual reports (and
the data analysis and conclusions will be individual efforts.)
Grade Basis:
Final Project: Written
Final Project: Oral
Quiz 1
Quiz 2
Homework:
Case Studies
Participation
Total
15%
10%
15%
20%7
15%
15%
10%
100%
Academic Honesty Policy:
It is expected that within this course, the highest standards of academic integrity will be maintained, in keeping with MIT’s stated policy: "Fundamental to the
principle of independent learning and professional growth is the requirement of honesty and integrity in conduct of one's academic and nonacademic
life….Cheating, plagiarism, unauthorized collaboration, and other forms of academic dishonesty are considered serious offenses for which disciplinary penalties
can be imposed.” Specifically in this class, collaboration is allowed for the projects but case summaries and graduate student projects are individual efforts. All
referenced work should be appropriately cited (APA format), to include websites, as well as figures and graphs in presentations. If there are any questions
whatsoever, feel free to contact the course instructors about any possible gray areas.
7
Whichever of the two quiz scores is higher will count 20% and the other will count 15%.
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