**Unique Numbers**: 58950/58825 **Time**:
MWF 10-11 **Room**: RLM 6.116

**Class Web Page**:http://www.ma.utexas.edu/users/mks/384G04/384G04home.html

**Instructor**: Smith, RLM 10.136, 471-6142, mks@math.utexas.edu,
http://www.ma.utexas.edu/users/mks/index.html

**Office hours**: Office hours for the first three
class days are:

Office hours for the second week of classes and regular office hours will be announced in class and on my web page when they are set. 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. However, I am

- Wednesday, August 25, 2:30 - 3:30 pm
- Thursday, August 26, 11 - 12 am
- Friday, August 27, 11 - 12 am

**Text**: *Applied Regression Including Computing
and Graphics*, Cook and Weisberg, Wiley, 1999

**Topics covered**: With some omissions, we will cover
Chapters 1 — 17 (Parts I and II) of the text, supplemented by derivations
of some results (mainly in Chapter 6) and occasional short additional
topics.

**Focus of the Course**: 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 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 M358K or M378K, or a similar graduate course.
Students enrolled for the undergraduate course (M374G) also need permission
of the instructor. 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 the appendices
of the references listed below or other suitable sources as needed to fill
in any gaps in your particular background. The references by Mendenhall and
Sincich, Neter et al, Rice, and Ross listed below are especially appropriate
for this purpose.

Also, some acquaintance with linear algebra (e.g., multiplying matrices, understanding matrix inverses, linear dependence and independence) will be assumed eventually. If your linear algebra background is weak, see Section 7.9.1 of the text and/or Chapter 5 of the reference by Neter et al listed below now to get up to speed before we start using matrix notation.

**Assignments and grading**: Course grades will be
based mainly on problem sets to be turned in approximately every two weeks.
There will also be a take-home midterm and a take-home final. The final exam
will be officially due Thursday, December 9, 5 p.m. (the end of the final
exam date and time listed in the course schedule for this course).
Two homework grades 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*.

Assignments and exams for M 384G and M 374G will have substantial overlap, but some differences. In some cases they will be the same, but usually the M 374G assignment will have either one (typically more challenging) problem deleted, or parts (usually the more challenging parts) of some M 384G problems deleted, or will have a less challenging problem substituted for one on the M 384G assignment.

*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.

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. Be sure to check.

**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**: I will expect you to learn how
to use Arc regression software. The textbook integrates instructions on
using Arc into the text, and includes an Appendix that serves as an Arc
user's manual. Arc is especially designed for regression and includes some
features not available in other software packages. I will accept use of
other software on assignments when the special features of Arc are not
needed, 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.)

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.

*Arc availability*: Arc is available free for Windows,
Macintosh, and Unix platforms at http://www.stat.umn.edu/arc/.(Note:
This website is more direct than the one given on p. 545 of the textbook.)
If you have your own computer, you may want to download your own copy.
Arc has been installed on the Math Department computer system for your use
in math department student labs. If you do not have a math department computer
account, I can give you a class account for use this semester. It might
also be possible to use Arc on other University computers by downloading
it onto floppy or Zip disks.

*Cautions Regarding Arc*:

1. There has been a bug in the lisp-stat program in which arc is written that messes up histograms when the window is resized. So be cautious in resizing windows for histograms.

2. In the past, at least one student had problems with arc loaded after upgrading to Windows 2000 Professional. This was reported to the developers of arc, but they had had no additional reports of the problem. If you use Windows 2000 Professional, please be alert for possible problems and check the arc website above for possible updates.

*Copying and Printing from Arc*: Arc does not support
printing directly. However, text from Arc can be copied and pasted to
a word processing program, then edited and printed. The Windows and Mac
versions of Arc also support copying and pasting of graphics. The Unix
version (which is the version available on math department computers)
does not support copying graphics. However, graphics may be saved in PostScript
format (using "Save to file"), then converted to another format, then imported
into the Star Office word processor available on the math department computers.
See "Using Arc and Star Office on Math Department Computers" at http://www.ma.utexas.edu/users/mks/384G04/arcstoffice.html for more information.

*Data*: Data needed for problems in the textbook
comes with Arc. If I assign other data problems, I will put the data on the
math department computer system and on the web for students using other computers.
More details will be given as the need arises.

**Ethical matters**:

*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/profession/index.cfm?fuseaction=ethicalstatistics.

*Authorized collaboration*: Since the University
defines collaboration that is not specifically authorized as academic dishonesty,
I need to tell you what collaboration is authorized in this class.

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.

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?") 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**: Although I believe that
our textbook is the best regression textbook available, I realize that no
textbook is just right for everyone at all times. 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.

(I have not put any of these on reserve; please let me know if you think I need to do so.)

Chatterjee S, Price B, Regression Analysis By Example, 2nd ed, 1991. New York: John Wiley & Sons, QA 278.2 C5 1991. Has less of an emphasis on linear algebra than our textbook.

Cook, R. D., Regression Graphics: Ideas for Studying Regression through Graphics, 1998, New York, Wiley, QA 278.2 C6647 1998 PMA. Gives the theory behind the graphical techniques used in our textbook.

Draper and Smith, Applied regression analysis,3rd ed., New York, Wiley 1998 QA 276 D68 1998 Physics-Math-Astronomy Library (Also available as an e-book through UTNetCAT.) An earlier edition of this book has been used as a text for this course in the past. There is a third edition, but the library does not have it.

Graybill, Franklin A. and Hariharan K. Iyer, Regression analysis: concepts and applications, Duxbury, Belmont, Calif., 1994 QA 278.2 G73 1994. Slightly less advanced than our textbook. Weak point: Examples tend to be made-up rather than real. Possible strong point (depending on how you plan to use regression): Emphasizes the use of regression for prediction.

Mendenhall, William and Terry Sincich, A Second Course in Statistics: Regression Analysis, 5th edition, Prentice Hall, 1996, HF 1017 M46 1996. Slightly less advanced than this course. The first chapter has a review of basic statistics.

Montgomery, Douglas C. and Elizabeth Peck, Introduction to Linear Regression Analysis, 2nd ed., New York, Wiley, 1992 QA 278.2 M65 1992. Another book that has been used as a text for this course.

Neter, John, Michael Kutner, Christopher Nachsteim and William Wasserman, Applied Linear Regression Models, 3rd edition, Chicago, Irwin, 1996, QA 278.2 A65 1996. A third book which has been used as a text for this course.

Rice, John A. Mathematical Statistics and Data Analysis, 2nd ed. Duxbury, 1994, QA 276.12 R53 1994. A textbook on introductory mathematical statistics that would be suitable if you need review of 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.

Ryan, Thomas P., Modern Regression Methods, Wiley, 1997, QA 278.2 R93 1997. Often terse, but has summaries of recent developments.

Sen, Ashish and Srivastava, M. Regression analysis : theory, methods and applications, Springer, 1990 QA 278.2 S46 1990

Stapleton, James H., Linear Statistical Models, Wiley,
1995, QA 279 S695 1995. Regression and analysis of variance from a linear
algebra and geometric point of view.