Instructor:	Dr. Brian Habing, Associate Professor office: 203 LeConte e-mail: habing@stat.sc.edu MSN messenger: brian@habing.com AOL or Yahoo messenger: DrStatpsy FAX: (803)777-4048 office phone: (803)777-3578 home phone: (803)739-2686 (9am to 10pm only) Office Hours: whenever the door is open, by appointment, and Monday/Wednesday/Friday 9:30-11:30 Website: http://www.stat.sc.edu/~habing/courses/530F08.html
Bulletin Description:	STAT 530 —Applied Multivariate Statistics. (3) (Prereq: STAT 515 or PSYC 228 or MGSC or equivalent) Introduction to fundamental ideas in multivariate statistics using case studies. Descriptive, exploratory, and graphical techniques; introduction to cluster analysis, principal components, factor analysis, discriminant analysis, Hotelling's T2 and other methods.
Note:	The deadline for dropping the course without failing is Thursday, October 2nd Thursday, October 9th; Tuesday, November 4th; and Thursday, November 27th are holidays
Learning Outcomes:	This course covers the background, implementation, and interpretation of basic graphical displays, principal components analysis, factor analysis, multidimensional scaling, cluster analysis, MANOVA, discriminant analysis, and canonical correlation analysis. Upon successful completion of this course, students should be able to: Determine which multivariate methods are appropriate for a given situation Understand the basic logic behind each method's construction Verify whether the assumptions needed to implement the methods are satisfied Analyze a data set using the methods in the software package of their choice Interpret the output for each of the methods
Expectations:	All students are expected to: Attend/view class regularly, asking questions when clarification is needed and participating in any in-class activities. Copies of the power-point slides will be posted on the course page by noon the day of the class, and it may be helpful to print them out for use during class. Read the pages covered in each class before the following class period. The pages will be listed on the course page. Attempt all of the assigned homework problems and turn them in before the start of the class in which they are due Use the resource of their fellow students and their instructor to seek answers to questions that arise in class, in the readings, and on the homework
Required Text:	An R and S-PLUS Companion to Multivariate Analysis by Brian Everitt, Springer 2005 The University Bookstore can be linked to at http://sc.bkstore.com/ .
Computers:	Use of a computer is required for the analysis of multivariate data. The examples in class will be done using R, SAS, and SPSS. Most problems will be able to be analyzed using your choice of those three packages, but everyone is encouraged to download a free copy of R (see the course page for downloading instructions). The university has discounted rates for SAS and SPSS (see the link on the course page). NO PREVIOUS KNOWLEDGE OF R, SAS, or SPSS IS ASSUMED.
Exams and Topics Covered:	There will be two take-home exams. The topics covered in the exams will generally follow the chapters of the text listed below. However, the exams may also cover material which was solely presented in class, and that is not contained in the text. You are to receive help from no-one except me on the exams. The exams may be turned in to me personally, left in my mailbox in 216 LeConte, faxed to (803)777-4048, or sent by e-mail. The first exam will be posted on Tuesday, October 7th and due before class on Tuesday, October 21st. It will focus on materials related to Chapters 1-4, including Graphical Displays, Principal Components, and Factor Analysis. The second (final) exam will be posted on Tuesday, November 25th and due at 11:00am Tuesday, December 9th. It will focus on materials related to Chapters 5-8, including Multidimensional Scaling, Cluster Analysis, Discriminant Analysis, MANOVA, Logistic Regression, and Canonical Correlation Analysis. Incidence of cheating and academic dishonesty will be punished to the full extent allowed by university regulations.
Homework:	Homework is due at the beginning of the class period it was assigned for. Late homework is not accepted. The homework may be turned in to me personally, left in my mailbox in 216 LeConte, faxed to (803)777-4048, or sent by e-mail in a pre-approved format. Homework will be assigned at least one week in advance in class, and will also be posted on the class website. Any handwriting on the homework must be legible, the work used to obtain the answers must be shown and correct, and the final answers must be clearly indicated in order to receive full credit. Extra points may be deducted for violating any of the following: Write on one side of the paper only. Multiple pages must be stapled together. No clips. Copies of the computer code or menu options used must be included with any homework requiring R, SAS, or SPSS. Extraneous pages of SAS/R output should not be turned in. Your name should be on the first page of the assignment. You MAY consult with other students on the homework assignments (e.g. you can ask each other for advice and may work on the big picture together, but you should write up the details yourself). The lowest two homework grades will be discarded.
Grades:	The grade is determined with each exam and the homework being worth 1/3rd of the final grade. The grading scale may be adjusted as needed, but will not be made more difficult than: Letter Minimum Grade Percent A 90 B+ 87 B 80 C+ 77 C 70 D+ 67 D 60 F 0 There is no "extra credit". Any deviations from the above grading scheme will be to the benefit of the students.
Graduate Credit:	Students taking the course for graduate credit will be assigned extra questions on the take home exams.
Complaints and Comments:	While there are end of semester evaluation forms, they come far too late to resolve any difficulties experienced in the class. All complaints should be raised by either speaking with me directly, or by anonymously leaving a message in my mailbox in 216 LeConte.