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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]
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)
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.
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
Assignments and Grading:
Midterm 1
20 %
October 18
Midterm 2
November 15
Final Exam
During Finals Week
Homework (Best 14 of 15)
Quizzes (Best 5 of 6)
Unannounced in Class
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.
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
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.
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:
Most standard statistic procedures for multivariate data analysis can be found under
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.
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):