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STAT 505: Applied Multivariate Analysis Instructor: Andrew Wiesner Office: 416 Thomas Building Office Hours: T 2:30 – 3:30 and by appt. Phone: 863 – 5653 Email: [email protected] Teaching Assistant: Yi-Ju Chen Office: 316 Thomas Building Office Hours: M 1:00 – 3:00 Phone: 863 – 3238 Email: [email protected] Text: Johnson, R.A., and Wichern, D.W. (1998). Applied Multivariate Statistical Analysis. 5th ed. Prentice Hall, New York. (On reserve, Physical and Math Science Library (PAMS) at 201 Davey Lab) Supplemental SAS Text: Cody, R. and Smith, J.K. Applied Statistics and the SAS Programming Language, Fourth Edition. Prentice Hall, New York. (On reserve, Physical and Math Science Library (PAMS) at 201 Davey Lab) Objectives: Students completing this course should be able to: select appropriate methods of multivariate data analysis, given multivariate data and study objectives; write SAS programs to carry out multivariate data analyses; interpret results of multivariate data analyses. Prerequisites: 6 Hours Credit in Statistics; Matrix Algebra. All Penn State and Eberly College of Science policies regarding academic integrity apply to this course. For more information see http://www.science.psu.edu/academic/Integrity/index.html Assignments and Grading: Midterm 1 20 % October 18 Midterm 2 20% November 15 Final Exam 30% During Finals Week Homework (Best 14 of 15) 20% Weekly Quizzes (Best 5 of 6) 10% Unannounced in Class Homework Most homework assignment will involve the use of a statistical package. For this course the required software is SAS. The homework is designed to provide you the opportunity to display an understanding of the course material, including the multivariate functions of SAS and proper interpretation(s) of the output. Collaborative work is allowed and encouraged. However, each student is responsible for submitting their solutions to each assignment. For all assignments requiring the use of the computer, the SAS code used in solving the problem should be included. This code should precede the problem for which the code was used. All homework assignments are to be turned in at the beginning of the class period on the date on which they are due. Homework data sets will be available on the course web site on ANGEL. A failure to make an honest attempt at each problem will result in a zero (0) for that assignment. The grading of the homeworks will be straightforward: if you attempt each problem honestly (i.e. your answer shows some reasonableness), then you will be awarded full credit for that problem. Solutions to the homework will be available following the due date. Since solutions will be available and the lowest score will be dropped, late homework submissions will NOT be accepted. Quizzes The quizzes will be unannounced. The purpose of these quizzes is to assist you in keeping pace with the material. They will consist of 5 to 7 multiple choice questions. Since your lowest score will be dropped, make-up of missed quizzes will NOT be permitted. Exams Exams are open book and open notes from this semester only!! No notes or exams from prior semesters will be permitted. Use of such materials will result in an automatic zero for that exam. Students MUST work individually on the exams. Hand calculators are required for each exam. Computing: SAS shall be used for all in-class demonstrations of statistical analyses, homework assignments, and exams. Help regarding the syntax of SAS commands, and SAS manuals are available at: www.work.psu.edu/sas/onlinedoc/saspdf/common/mainpdf.htm Most standard statistic procedures for multivariate data analysis can be found under SAS/STAT. Course Outline: 0. Introduction 1. Multivariate Data a. Matrix Review. b. Graphical Display of Multivariate Data. c. Probability Theory. d. Measures of Central Tendency, Dispersion, and Association. e. Linear Combinations of Random Variables. f. Multivariate Normal Distribution. g. Properties of the Sample Mean and Correlation. h. Partial Correlations. 2. Inferences about Multivariate Means a. Hotelling's T-Square. (i) One-Sample Hotelling's T-Square. (ii) Paired Hotelling's T-Square. (iii) Two-Sample Hotelling's T-Square. b. Multivariate Analysis of Variance (MANOVA): (i) One-Way MANOVA. (ii) Two-Way MANOVA. c. Repeated Measures Experiments and Growth Curves: d. Discriminant Analysis: (1) Linear discriminant analysis (2) Quadratic discriminant analysis 3. Data Reduction a. Principal Components Analysis: b. Factor Analysis: c. Canonical Correlation Analysis: d. Cluster Analysis: 4. Structural Equation Modeling (time permitting):