Instructor: Michael Minnotte
Office: Lund 201-C
Phone: 797-2844 (office)
E-mail: Mike dot Minnotte at usu dot edu
Office Hours: TR 9:30 - 10:20, W 10:30 - 11:20, or by appointment.

Please note: The syllabus has the wrong time and date for the final exam. The final will be held at 11:30 on Tuesday, May 2. Sorry for the confusion.

R will not be formally available in the labs until the next time they update software; probably over Spring Break. After that, it should be available in the SciTech and AgSci labs. In the meantime, we have permission to download and install R on an individual basis. Just be aware that it will vanish when you log off, so you'll have to reinstall each time, even on the same machine. Be sure to save your work!
Update: Apparently, R is now available in the labs.

On reconsideration, I have decided to cancel the exams for this course. Homework will count for 75% of your grade, and there will be a final project requiring use of your own data for the remaining 25%. More details will be made available later.

I'll put announcements here. Please check regularly, especially if you have to miss class for any reason.

• Homework 1 (pdf) - Radiology data
• Homework 2 (pdf) - Educational testing data - Due Feb. 2!
• Homework 3 (pdf) - Leslie Salt Property data, Radiology data, Bank Salary data
• Homework 4 (pdf) - Track and Field Record data, "Need for Cognition" Survey data, Vocational Interest Correlation, Teen Drug Use Correlation
  • R function to import triangular correlation matrices (read.tri)
• Homework 5 (pdf) - MBA Students' Car Attitudes Survey data, Nine Psychological/Intelligence Tests' Correlation, "Need for Cognition" Survey data
• Homework 6 (pdf) - British Town Distances, Nation Similarities Data, Grocery Purchase Similarities Data
• Homework 7 (pdf) - International Food Usage Data
• Homework 8 (pdf) - Brother Head Size Data, Women's Health and Attitudes Data, Workplace Characteristic and Worker Satisfaction Data
• Homework 9 (pdf) - Attitude - Intention - Behavior Data, Math Attitudes Data
• Homework 10 (pdf) - Depression Questionnaire Data

• Final Project (pdf)

• Introduction to R
• Data Import
• R Graphics
  • 3D Scatterplot Function
• Vector and Matrix Geometry
• Regression Basics
  • 3D Scatterplot + Regression Plane Function
• Regression Diagnostics and Predictions - Note: anova(reg5, reg3) will do the same F-test I did, more easily and without additional packages. Bounty to Darl Flake!
• Principal Component Analysis
• Exploratory Factor Analysis
• Confirmatory Factor Analysis
• Multidimensional Scaling
• Hierarchical Clustering
• Distance Measures and k-Means Clustering
  • kth Nearest Neighbor Distance Function
  • k-Means Clustering Pseudo-F Statistic Function
  • k-Means Clustering Prediction Function
• Canonical Correlation Analysis
• Structural Equation Modeling
• Discriminant Analysis
  • Prediction Plot (2-D case)
• Discriminant Analysis Visualization and Classification Trees
  • 3-d Visualization of 2-d LDA/QDA
  • Some basic material on classification (and regression) trees
• Random Forests
  • Some material on random forests
• ANOVA and MANOVA
• ANCOVA and MANCOVA

Data Sets

• Iris Data:
  • Text File
  • Text File (with variable names)
  • SAS Xport File
  • SPSS File
  • S-Plus Object
  • Excel File
  • Excel Comma Separated Value File
• Women's Heights and Weights
• Leslie Salt Property Values
• Trivariate data for Principal Components Analysis Example
• Gross State Product Data (Raw)
• Planetary Data
• Pain Reliever Perceptions Data
• Luxury Car Perceptions Data (Lower Correlation Matrix)
• Expert Ratings of Wine-Growing Areas' Status Data
• European City Distance Data
• Car Dissimilarity Data
• Teen Drug Use Correlation Data
• Belly Dance Example Data
• Grocery Marketing Data
• Self Esteem/Job Satisfaction Correlation Matrix
• Respondent and Friend Aspirations Correlation Matrix
• South African Heart Disease Data
• Finland Alcohol Study
• New Food Item Marketing Study
• Ad Copy Likability/Intent Data
• Social Skills Study Data

• The Comprehensive R Archive Network
• Windows R Setup Executable Download - click on rwXXXX.exe, where XXXX gives the version number
• The Omega Hat Project (software connecting R with other packages)
• R Commands (Web site organized by topic)
• R Frequently Asked Questions (FAQ) List
• Understanding R (Web tutorial)
• R for Beginners (72 page pdf file)
• An Introduction to R (100 page pdf file)
• Data Analysis and Graphics Using R -- An Introduction (112 page pdf file)

Note: If you are using R on campus, you will probably need to execute the following commands to get through the firewall to install additional packages:

Sys.putenv("http_proxy"="http://proxy.usu.edu:80/")
Sys.getenv("http_proxy")

You will probably need to run these commands (on the R console) at the beginning of your session before trying to install packages.

Course Objectives: The objective of this class is to give you an introductory working knowledge of multivariate data analysis so that you can understand the literature and be able to appropriately analyze many types of multivariate data. The emphasis will be on developing a sound understanding of the methods and of when they should and should not be employed. Verbal and geometrical explanations will be stressed and only a minimal amount of mathematical notation will be used.

Prerequisites: Stat 3000 and Stat 5100. In exceptional cases, the prerequisites may be waived, but you are expected to have a good background in general statistics, regression, and statistical computing. If you are concerned about your preparation, please come see me.

Homework: I will assign homework every 1-2 weeks, usually from the text book. Please make things easy on me and yourself; make your homeworks easy to read and grade. Use one side of the paper, write or type neatly, and leave plenty of space. I will not grade a paper which I can't read. Also, show your work. Full credit will not, in general, be given for just the answer. If your answer is wrong, you will probably receive partial credit if you show your work, but not otherwise.

Many of the problems will involve computer work (see below). For the computational portions of such problems, I will want to see the commands and output of the R computer package. Cut-and-paste from the R console window into a text editor or word processor, or use the save to file command to save the entire console so that you may work with your results later. Feel free to delete mistakes or unnecessary commands and output.

Finally, you may help each other with your homeworks, but I expect what you turn in to be your own work. Helping does not mean simply copying what someone else has put down.

Late Homeworks: All homework will be due in class on the due date. The grade for the homework will be reduced by 10% if it is turned in late on the due date, and another 10% for every working day it is late after that, to a minimum of 30% of the original grade.

Once during the semester, I will, on request, waive the late penalty for a paper turned in by the class one week after the due date (or start the clock then for a still later turn-in). Simply note the request at the top of your homework when turning it in. Additional requests for extension without penalty will not be granted, so save this for a time you really need it.

Computer Use: We will use the R computer package. R is a Gnu-license (freeware) clone of the S-Plus package, and is available for free download (Windows and Unix) from
http://cran.us.r-project.org/ . I will spend some time in class going over the use of R.

Let me know if you need access to a computer with R, and I will arrange to have it made available in one or more of the labs on campus.

Tests: There will be a midterm in class on March 9, and the final will be Tuesday, May 2, from 11:30 to 1:20. More information will be provided about the exams as they approach.

Grades: For each person I will compute an overall score according to the formula

and will assign grades accordingly. There is no fixed grade profile for this class: if everyone does well, everyone can get an A.

Disability Policy: If a student has a disability that will likely require some accomodation by the instructor, the student must contact the instructor and document the disability through the Disability Resource Center, preferably during the first week of the course. Any requests for special considerations relating to attendance, pedagogy, taking of examinations, etc. must be discussed with and approved by the instructor. In cooperation with the Disability Resource Center, course materials can be provided in alternative formats - large print, audio, diskette or Braille.

Late Adds: The last day to add this class is January 30. Attending this class beyond that date without being officially registered will not be approved by the Dean's Office.

The above schedule and procedures in this course are subject to change in the event of extenuating circumstances.