Debbie - Stevens Institute of Technology



Revised: October 25, 2014

Stevens Institute of Technology

Howe School of Technology Management

Syllabus

MGT 718

Multivariate Analysis

|Fall, 2014 |Wednesdays, 6:15 pm |

| |Babbio 641 |

|Yasuaki Sakamoto |Office Hours: |

|Babbio 632 |Wednesday 5:30 and 9:00 pm |

|Tel: 201-216-8198 |By appointment |

|Fax: 201-216-5385 | |

|ysakamot@stevens.edu | |

| |Course & Web Address: |

| |BC 641 |

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. |

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 () |

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 |Grade Percent |

|Midterm exam |30% |

|Final exam |30% |

|Final paper |40% |

|Total |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.” |

| |

|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 , a web-based 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 () 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 2013 KDnuggets Software Poll |Install R, try R code, and |

| |the R environment, review of |- Why use R? and Follow up (pdfs) |submit output |

| |descriptive statistics |- An introduction to R (pp 7-17) | |

| | |- Using R for introductory statistics (pp 1-32) | |

|Week 3 |Getting used to R, review of |- Basic statistics (a pdf note) |Think about data for project |

| |probabilities and inferential |- An introduction to R (pp 18-39) |in your research area. |

| |statistics |- Using R for introductory statistics (pp 41-77) | |

|Week 4 |Looking at multivariate data, |- Basic statistics |Graph and interpret the data |

| |visualization methods, |- An introduction to R (pp 62-75) | |

| |preparing for data analysis, |- Using R for introductory statistics (pp 32-41) | |

| |selecting appropriate methods | | |

|Week 5 |Simple regression, multiple |- Basic statistics |Detect relationship between |

| |regression, and correlation |- An introduction to R (pp 50-61) |variables |

| | |- Using R for introductory statistics (pp 77-89) | |

|Week 6 |PCA, matrix manipulation, |Computer-Aided Multivariate Analysis: PCA (pdf) |Reduce the number of |

| |eigenvector and eigenvalue | |dimensions in the data |

|Week 7 |Exploratory and confirmatory |Computer-Aided Multivariate Analysis: Factor Analysis (pdf) |Find underlying dimensions in |

| |factor analysis | |the data |

|Week 8 |Path diagram and structural |Using Multivariate Statistics: SEM (pdf) |Detect structure in the data |

| |equation modeling | | |

|Week 9 |Multidimensional scaling and |An R and S-PLUS Companion to Multivariate Analysis: MDS and |Measure distance and find |

| |correspondence analysis |correspondence analysis (pdf) |spatial relationship |

|Week 10 |Clustering |An R and S-PLUS Companion to Multivariate Analysis: Cluster |Measure distance and partition|

| | |Analysis (pdf) |data points |

|Week 11 |Discriminant function |Using Multivariate Statistics: Discriminant function analysis,|Classify event |

| |analysis, MANOVA, Bayes net, |MANOVA (pdf) | |

| |neural net | | |

|Week 12 |Logistic regression, |Using Multivariate Statistics: Logistic regression (pdf) |Predict event |

| |binomially distributed data, | | |

| |maximum likelihood | | |

|Week 13 |Forecasting |Using Multivariate Statistics: Time-series analysis (pdf) |Analyze longitudinal data |

|Week 14 |Presenting results | |Writing method and results |

| | | |sections in a journal |

| | | |manuscript format |

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