STA 5707 Applied Multivariate Analysis

GENERAL INFORMATION (1/6/03) STA 4702/STA5707 Applied Multivariate Analysis Spring 2003 LECTURER: TEACHING ASSISTANT Pi-Erh Lin Mr. Gang Ye 208 OSB 644-6697 Office Hours: Office Hours: 1:30 p.m. - 2:20 p.m. TBA or by appointment or by appointment GENERAL POLICY: a. There will be no midterm examination and no final examination for STA 4207/STA 5707 in the Spring of 2003. Instead, there will be either three or four class projects for data analysis. Thee projects will be accounted for 80% of the total course grade. The remaining 20% will be homework assignments and your class participation. Usually you will have one week time to complete an assigned project. b. For each data analysis project report, the following weights of importance will be assigned: Technical Competence 50% Thoroughness 30% Clarity and Neatness 15% Innovation 5% The report must be typed in double-space on single side of a standard sized paper with portions of computer printouts copy-and-paste in the text. COURSE GRADE ASSIGNMENT: A = 90% and up B = 80% and up C = 70% and up ORIGINAL WORK: Students in this course may assist each other, or consult with the TA, with performing computer runs. They may discuss with each other on what graphs, plots, or analyses they have produced. All interpretations, write-ups, discussions, etc. of their project reports MUST be their own work. IMPORTANT DATE TO NOTE: The last day to drop the course without receiving a grade from FSU is Friday, January 31, 2003.

Course Objectives and Tentative Syllabus (1/6/03) STA 4702/5707 APPLIED MULTIVARIATE ANALYSIS Course Information: Instructor: Pi-Erh Lin 208 OSB/FSU 644-6697 Office Hours: 1:30 p.m. to 2:20 p.m. M-W-F Prerequisite: STA 4203/5207 Applied Regression Methods Textbooks: Computer-Aided Multivariate Analysis (3rd ed.). 1997. A.A. Afifi and V. Clark. Publisher: Chapman & Hall/CRC.
SPSS for Windows Step by Step. A simple guide and reference. 11.0 update. (4th ed.). 2003. D. George and P. Mallery. Publisher: Allyn and Bacon.
References: Dillon and Goldstein (1984): Multivariate Analysis. Johnson and Wichern (1998): Applied Multivariate Statistical Analysis.

Course objectives:
To gain an understanding of basic multivariate statistical analysis with emphasis in applications.
To be able to formulate from a complicated problem ready for multivariate statistical analysis;
To be able to recommend a multivariate technique appropriate for a given situation;
To be able to use SPSS or Minitab computer programs for statistical analysis; and
To be able to describe and interpret statistical results from computer outputs in layman's terms.

Tentative Syllabus:

(1) Principal Components Analysis
(2) Factor Analysis
(3) Cluster Analysis
(4) Canonical Correlation Analysis
(5) Discriminant Analysis
(6) Multivariate Analysis of Variance, and
(7) Multidimensional contingency table, if time permits.