Unique Numbers: 57285/64025 Time: MWF 10-11 Room: RLM 6.118
Class Web Page: http://www.ma.utexas.edu/users/mks/384E09/M384Esp09home.html
Instructor: Smith, RLM 10.136, 471-6142, mks[at]math.utexas.edu, http://www.ma.utexas.edu/users/mks/index.html
Office hours: Office hours are posted on my home page. I will need to cancel office hours now and then to accommodate meetings, oral exams, etc. I will try to give you several days advance notice when this happens. If it is impossible for you to make office hours, I will try to arrange individual appointments at other times.
Text: Design and Analysis of Experiments, Dean and Voss,
Please Note: The UT library has an e-book copy of the textbook. If you use it, please use "view" rather than "check out". If you use "check out", then no one else will be able to use the e-book for 24 hours. Also, be sure to "close" after viewing the book.
Parts of Text to be Covered: With some omissions, we will cover Chapters 1 — 7, 10, 17, 18, and 19. As time permits, we may also cover parts of Chapter 11. Occasional short additional topics may be added.
Focus of the Course: This is an introductory course in Design and Analysis of Variance. The course will be a mix of theory and application. Some results will be derived, so that you get some feel for why things are as they are. Most of what you will be expected to do is apply the theory carefully and with understanding. This means I will expect you to think about what you do. Do not expect rules that you can use in a mechanical manner.
Prerequisites: An upper division undergraduate course in statistics such as M378K, or a similar graduate course. Since introductory statistics courses vary widely in their content and emphasis, it is inevitable that most students will be fuzzy on some of the prerequisite material. Therefore, I will give very brief reviews of most undergraduate concepts we use and expect you to consult appropriate references (e.g., the texts by Ross or Wackerly listed below) if needed.
Assignments and grading: Course grades will be based on five problem sets, a take-home midterm exam, and a take-home final exam. The problem sets will be due Fridays February 6, February 20, April 3, April 17, and May 1. The midterm will be due Friday, March 13 (the day before spring break). The final exam will be due Thursday, May 14 at 5 pm (the end of the final exam date and time listed in the course schedule for this course). One homework grade will be dropped, to allow for a normal amount of illness, emergencies, bad weeks, and a learning curve. The midterm and final exams will each count equally with each of the remaining homework assignments in determining the course grade, but the exam grades will not be dropped.
I will expect you to write up your homework solutions carefully. Do not hand in a rough draft. In particular:
1. Write in complete sentences.
2. Organize your presentation. In particular, put computer output and graphs as close as possible to the place where you discuss or refer to them. (Please do not put them at the end of each problem.) This often requires cutting and pasting (either by hand or computer). In some cases, writing on your computer output will work.
3. Do not hand in computer output that you have not referred to in your discussion. Again, this may require cutting and pasting. But be sure to include computer output that you have referred to in your discussion.
4. Explain your reasoning clearly. The quality of your reasoning will be an important consideration in your grade, especially as the semester progresses and you have more options available to consider. Do not expect full credit if you do not give reasons for your answers or if you do not interpret your output in the context of the problem.
5. Write legibly.
6. In any applied problem, state your conclusions carefully and in the context of the problem. For example, "We reject the null" is not an acceptable final conclusion. "Based on the p-value of .02, we conclude that the data do not support the null hypothesis that the mean weight of beans of type 1 is 3 and reject it in favor of the alternate hypothesis that this mean weight is > 3," would be much better (assuming the details fit the context).
I will also sometimes assign questions for discussion. -> Be sure to think about these before we discuss them in class. Their purpose is to help you understand (and avoid misunderstanding!) some of the subtleties involved in the concepts and their application.
I will also try to give you reading assignments so you can preview material if you choose.
Often I will post class lecture notes on the class home page the
night before lectures. I will email the class from Blackboard when
notes are posted. Be sure to check your email. (Please Note: If the University
has the wrong email address for you in Blackboard, then you will not
receive these or any other notifications of misprints, changes in due
Policy on late work: I am willing to accept one slightly late homework assignment from each student. "Slightly late" means after class on the day the assignment is due, but before the grader picks up homework. Late exams may be subject to a late penalty. I am always willing to accept assignments early. They may be slipped under my door if I am not available. Extenuating circumstances will be handled on a case-by-case basis. In particular, according to Section 51.911 of the Texas Education Code, a student who misses an examination, work assignment, or other project due to the observance of a religious holy day must be given an opportunity to complete the work missed within a reasonable time after the absence, provided that he or she has properly notified each instructor. It is the policy of The University of Texas at Austin that the student must notify each instructor at least fourteen days prior to the classes scheduled on dates he or she will be absent to observe a religious holy day. For religious holidays that fall within the first two weeks of the semester, the notice should be given on the first day of the semester. Alternate arrangements will be made as soon as possible after notification.
Computer software: Minitab is the default software for this course. I will use it and will give handouts on using it. I will accept use of other software on assignments provided:
1. You don't ask me for help with the software. (In particular, it is your responsibility to put the data into a format appropriate for the software, if needed.)
2. It can do what is needed.
3. You don't use it to replace doing your part (in particular, thinking) on homework.
4. You interpret output assuming I am unfamiliar with the software.
In particular, students who have access to SAS may wish to use that software;
the textbook provides instructions in doing so. However, the math department
does not support SAS, although it currently has JMP, which is related to SAS.
Statistical ethics: Statistics consists of a collection of tools which, like any tools, can be used either for good or for ill. It is your responsibility as a citizen of the world to be sure not to misuse these tools. I encourage you to read the Ethical Guidelines for Statistical Practice developed by the American Statistical Association, available on the web at http://www.amstat.org/about/ethicalguidelines.cfm.
Academic Integrity: Any act designed to avoid partipating honestly in the academic process is considered academic dishonesty by this University. Examples of academic dishonesty include providing misleading information to recieve an extension on an assignment, plagiarism, and unauthorized collaboration. Instructors vary in what kinds of collaboration are authorized. Here are the details that apply to this course:
The following type of collaboration is authorized on homework, but not on exams: Working on homework with someone who is at roughly the same stage of progress as you, provided both parties contribute in roughly equal quantity and quality (in particular, thinking) to whatever problem or problem parts they collaborate on.
The following types of collaboration are not authorized:
1. Working together with one person the doer and one the follower.
2. Any type of copying; this includes splitting up a problem so that different people do different parts or obtaining solutions from students who took the course previously.
3. Any type of collaboration on exams.
The penalty for unauthorized
collaboration will be a grade of zero on the assignment or exam. Cases
of unauthorized collaboration or other acedemic dishonesty will be
reported to the Dean of Students office. See http://deanofstudents.utexas.edu/sjs/ for more information on academic information and its potential consequenceLecture
Academic dishonesty aside, asking anyone, "How do I do this problem?" (as opposed to questions like, "How do I carry out this detail of this technique?" or, "I'm not sure whether to proceed this way or this way; here is my thinking about each possibility; am I missing something?"or "Can you read this over and critique it for me?") is cheating -- cheating yourself and your future employer, since it avoids the most important part of statistics: thinking.
Students with Disabilities: Please notify me as soon as possible of any modification/adaptation you may require to accommodate a disability-related need. You will be requested to provide documentation to the Dean of Students' Office, in order that the most appropriate accommodations can be determined. Specialized services are available on campus through Services for Students with Disabilities. For more information, contact the Office of the Den of Students at 471-6259, 471-4641 TTY.
Additional references: Here are some suggestions if you need to consult another text. However, do not try to find solutions to homework problems in another textbook. I expect you to think in doing homework problems. If you look up the solution, you have largely defeated the purpose of the problem.
Other books on Analysis of Variance and Design of Experiments:
Cobb, George W., Introduction to Design and Analysis of Experiments,
Key College/ Springer, 1998. This book is slightly lower level than this course.
It does not go into derivations of the statistical procedures, but has a
lot of detail on different experimental designs and gives many intuitive descriptions.
Montgomery, Douglas C. Design and Analysis of Experiments, Wiley,
2001. Another book that has been used as a text for this course.
Books that may be useful for prerequisite material:
Ross, Sheldon M., Introduction to Probability and Statistics for
Engineers and Scientists, New York, N.Y., Wiley, 1987, TA 340 R67 1987
Engineering Library. Another textbook on introductory statistics that
might be useful for review or reference.
Wackerly, Mendenhall, and Schaeffer, Mathematical Statistics with Applications,