Revised: March 7, 2016
Stevens Institute of Technology
Howe School of Technology Management
Syllabus
MGT 718
Multivariate Analysis
Fall, 2014
Yasuaki Sakamoto
Babbio 632
Tel: 201-216-8198
Fax: 201-216-5385
ysakamot@stevens.edu
Wednesdays, 6:15 pm
Babbio 641
Office Hours:
Wednesday 5:30 and 9:00 pm
By appointment
Course & Web Address:
BC 641 http://www.stevens.edu/moodle
Overview
This course introduces basic methods underlying multivariate analysis through computer
applications using R, which is used by many data scientists and is an attractive
environment for learning multivariate analysis. Students will master multivariate analysis
techniques, including principal components analysis, factor analysis, structural equation
modeling, multidimensional scaling, correspondence analysis, cluster analysis,
multivariate analysis of variance, discriminant function analysis, logistic regression, as
well as other methods used for dimension reduction, pattern recognition, classification,
and forecasting. Students will build expertise in applying these techniques to real data
through class exercises and a project, and learn how to visualize data and present results.
This proficiency will prepare students to conduct their own independent research.
In this course, students will:
- master various techniques used in multivariate analysis
- learn how to apply multivariate analysis methods to real data
- improve their ability to think critically about data analysis and interpretation
- develop skills for visualizing and communicating results
Learning Goals
After taking this course, students will be able to:
- use R to analyze multivariate data
- visualize multivariate data and communicate results
- recognize pattern, classify information, and forecast events
- think critically about data and research findings
Additional learning objectives include the development of:
Written and oral communications skills - the written project report will be used to assess
written communication skills and the oral presentations of the project will be used to
assess oral communication skills.
1
Pedagogy
The course incorporates demonstration, discussion, and in-class R exercise. Students are
expected to complete a final project using their own data. The overall goal is to establish
an active, comfortable, and creative learning environment.
Readings
Recommended textbooks
T. W. Anderson (2003). An Introduction to Multivariate Statistical Analysis, Third
Edition, Wiley.
Abdelmonem A. Afifi, Virginia Clark, Susanne May (2004). Computer-Aided
Multivariate Analysis, Fourth Edition, CRC Press.
Additional tutorials
Intro to R by GoogleDevelopers, Quick-R, inside-R, An Introduction to R, A short list of
the most useful R commands, R reference card
Supplementary materials
Probability and statistics 1.151 or 18.05 from MIT OpenCourseWare
(http://ocw.mit.edu/index.htm)
Assignments
Take-home midterm exam assigned on week 7: Materials up to week 7
Take-home final exam assigned on week 13: Comprehensive
Final paper: The method and results sections in a journal manucript format
Grading
Assignment
Midterm exam
Final exam
Final paper
Total
Grade Percent
30%
30%
40%
100%
Ethical Conduct
The following statement is printed in the Stevens Graduate Catalog and applies to all
students taking Stevens courses, on and off campus.
“Cheating during in-class tests or take-home examinations or homework is, of course,
illegal and immoral. A Graduate Academic Evaluation Board exists to investigate
academic improprieties, conduct hearings, and determine any necessary actions. The
term ‘academic impropriety’ is meant to include, but is not limited to, cheating on
homework, during in-class or take home examinations and plagiarism.”
2
Consequences of academic impropriety are severe, ranging from receiving an “F” in a
course, to a warning from the Dean of the Graduate School, which becomes a part of the
permanent student record, to expulsion.
Reference: The Graduate Student Handbook, Academic Year 2003-2004 Stevens
Institute of Technology, page 10.
Consistent with the above statements, all homework exercises, tests and exams that are
designated as individual assignments MUST contain the following signed statement
before they can be accepted for grading.
____________________________________________________________________
I pledge on my honor that I have not given or received any unauthorized assistance on this
assignment/examination. I further pledge that I have not copied any material from a book,
article, the Internet or any other source except where I have expressly cited the source.
Signature _________________________
Date: _____________
Please note that assignments in this class may be submitted to www.turnitin.com, a webbased anti-plagiarism system, for an evaluation of their originality.
Course/Teacher Evaluation
Continuous improvement can only occur with feedback based on comprehensive and
appropriate surveys. Your feedback is an important contributor to decisions to modify
course content/pedagogy which is why we strive for 100% class participation in the
survey.
All course teacher evaluations are conducted on-line. You will receive an e-mail one
week prior to the end of the course informing you that the survey site
(https://www.stevens.edu/assess) is open along with instructions for accessing the site.
Login using your campus username and password. All responses are strictly anonymous.
We especially encourage you to clarify your position on any of the questions and give
explicit feedbacks on your overall evaluations in the section at the end of the formal
survey which allows for written comments. We ask that you submit your survey prior to
the close of the examination period.
Course Schedule
Topic
Reading
Exercise
Week 1
Overview and goals of
multivariate analysis
Week 2
Statistical computing
using the R
environment, review of
descriptive statistics
- The 2013 KDnuggets Software Poll
- Why use R? and Follow up (pdfs)
- An introduction to R (pp 7-17)
- Using R for introductory statistics (pp 1-32)
Install R, try R code,
and submit output
Week 3
Getting used to R,
review of probabilities
and inferential
statistics
Looking at
- Basic statistics (a pdf note)
- An introduction to R (pp 18-39)
- Using R for introductory statistics (pp 41-77)
Think about data for
project in your
research area.
- Basic statistics
Graph and interpret
Week 4
3
Week 5
Week 6
Week 7
Week 8
Week 9
Week 10
multivariate data,
visualization methods,
preparing for data
analysis, selecting
appropriate methods
Simple regression,
multiple regression,
and correlation
PCA, matrix
manipulation,
eigenvector and
eigenvalue
Exploratory and
confirmatory factor
analysis
Path diagram and
structural equation
modeling
Multidimensional
scaling and
correspondence
analysis
Clustering
Week 13
Discriminant function
analysis, MANOVA,
Bayes net, neural net
Logistic regression,
binomially distributed
data, maximum
likelihood
Forecasting
Week 14
Presenting results
Week 11
Week 12
- An introduction to R (pp 62-75)
- Using R for introductory statistics (pp 32-41)
the data
- Basic statistics
- An introduction to R (pp 50-61)
- Using R for introductory statistics (pp 77-89)
Computer-Aided Multivariate Analysis: PCA
(pdf)
Detect relationship
between variables
Computer-Aided Multivariate Analysis: Factor
Analysis (pdf)
Find underlying
dimensions in the data
Using Multivariate Statistics: SEM (pdf)
Detect structure in the
data
An R and S-PLUS Companion to Multivariate
Analysis: MDS and correspondence analysis (pdf)
Measure distance and
find spatial
relationship
An R and S-PLUS Companion to Multivariate
Analysis: Cluster Analysis (pdf)
Using Multivariate Statistics: Discriminant
function analysis, MANOVA (pdf)
Measure distance and
partition data points
Classify event
Using Multivariate Statistics: Logistic regression
(pdf)
Predict event
Using Multivariate Statistics: Time-series analysis
(pdf)
Analyze longitudinal
data
Writing method and
results sections in a
journal manuscript
format
Reduce the number of
dimensions in the data
4