Instructor

Syllabus

Office Hours -- Fall 2016

Class Meeting Time

Current Textbooks

Courses that may serve as a prerequisite: Any of the following: PSYC 228 or 709; EDRM 710; STAT 509, 515, 700, or 704; MGSC 291, 391 or 692; BIOS 700.
(If you have had a course that may be equivalent to one of these, please contact me about it.)

Course Description

Purpose: To introduce students with a variety of statistical backgrounds to the basic ideas in multivariate statistics. It will cover the assumptions, limitations, and uses of basic techniques such as cluster analysis, principal components analysis, and factor analysis as well as how to implement these methods in R. Instead of theoretical development, the focus will be on the intuitive understanding and applications of these methods to real data sets by the students.

Available Computing Resources

Course Notes

• Class Slides for Chapter 1

• Class Slides for Chapter 2

• Class Slides for Chapter 3

• Class Slides for Chapter 4

• Chapter 4 Slides with Variance and Covariance Derivations

• Class Slides for Chapter 4, Part 2

• Class Slides for Chapter 5

• Class Slides for Chapter 6

• Figure 6.4 from Everitt textbook

• Class Slides for Chapter 6, Part 2

• Table of Possible Covariance Structures for Model-based Clustering

• Class Slides for Chapter 7 (Classification)

• Class Slides for Chapter 8 (and parts of Chaps. 2 and 4) of ISL textbook (K-nearest neighbors, Regression Trees, Classification Trees, and Random Forests)

• Class Slides for Chapter 9 of ISL textbook (Support Vector Classifier and Support Vector Machine)

• Class Slides for Chapter 7 (Grouped Multivariate Data: Hotelling's T^2 inference and MANOVA)

• Class Slides for Chapter 8

Downloading Instructions for R

• Instructions for Downloading R

Computing Tips: Some Review

• Introduction to SAS which should be a good instruction for the software

• A link to Dr. Habing's SPSS example code for multivariate analysis

Computer Code for Class Examples

Example R Code

• Chapter 1 example R code (Mean vector, Covariance matrix, Correlation matrix, Distances, Q-Q plot, Chi-square plot)

• A simple example of a Box-Cox transformation for multivariate data

• Chapter 2 example R code (Enhanced scatterplots, Convex hull, Chi-plot, Bivariate boxplot, Bivariate density estimator, Bubble plot, Scatterplot matrix, 3-D scatterplot, Star plot, Chernoff faces)

• Some extra examples of plots for multivariate observations (Profile plots, Profiles with bars, Andrews Plots)

• Chapter 3 example R code (Principal Components Analysis, including scree plots, plots of PC scores, and CIs for variances of the population PCs)

• Chapter 4 example R code (Factor Analysis, including rotations, plotting factor scores, and model diagnostics)

• Chapter 5 example R code (Multidimensional Scaling and Correspondence Analysis)

• Chapter 6 example R code (Cluster Analysis)

• Chapter 7 example R code (Discriminant Analysis, Classification using Logistic Regression)

• K-Nearest Neighbors, Regression Tree, Classification Tree, and Random Forest R code

• Support Vector Classifier and Support Vector Machine example R code

• More Chapter 7 example R code (Hotelling T^2 inference, MANOVA)

• Chapter 8 example R code (Canonical Correlation Analysis, Multivariate Regression)

• Multiple Imputation example R code

Example SAS Code

• Chapter 1 example SAS code (Mean vector, Correlation matrix, Q-Q plot, Chi-square plot)

• Chapter 3 example SAS code (Principal Components Analysis, including plots of PC scores)

• Chapter 4 example SAS code (Factor Analysis, including rotations and model diagnostics)

• Chapter 5 example SAS code (Multidimensional Scaling)

• Chapter 6 example SAS code (Cluster Analysis)

• Chapter 7 example SAS code (Discriminant Analysis and MANOVA)

• Chapter 8 example SAS code (Canonical Correlation Analysis, Multivariate Regression)

Data Sets

• Fisher's Iris Data

• Air Pollution Data (from Chapter 2)

• U.S. Air Pollution Data (from Chapter 3)

• Foodstuffs Content Data (Table 3.6 of text)

• Bumpus's Bird Data

• Life Expectancy Data

• School Subjects Correlation Matrix, Pain Correlation Matrix and Legal Offenses Dissimilarity Matrix (from Problems 4.6, 4.7 and Table 5.12 of text)

• Hair/Eye Color Data (Table 5.13 of text)

• Pottery Data (Table 6.3 of text)

• Tennis Racquets Data

• ACT items Data

• Body Measurements Data

• SAT scores Data

• SAT scores with graduation indicator

• Egyptian skulls data (from Table 5.8)

• SIDS data (from Table 7.5)

• Test score data

• Blood Glucose data (from Table 8.6 -- slightly corrected from book's printing which had some typos)

• Computer data

• hsb data from the 'faraway' package in R

• 2015 baseball batting data

• 2015 baseball pitching data

Homework

• Homework 1, Fall 2016 (due Friday, Sept. 2 by 3:00 p.m.)

• Homework 2, Fall 2016 (due Thursday, Sept. 15 by 3:00 p.m.)

• Homework 3, Fall 2016 (due Tuesday, Sept. 27 by 3:00 p.m.)

• Homework 4, Fall 2016 (due Thursday, Oct. 6 by 3:00 p.m.)

• Homework 5, Fall 2016 (due Wednesday, November 2 by 4:00 p.m.)

• Homework 6, Fall 2016 (due Tuesday, November 22, by 3:00 p.m.)

• Homework 7, Fall 2016 (EXTRA CREDIT) (due Thursday, December 1, by 3:00 p.m.)

Some Homework Solution Code and General Comments

• Homework 1 example solutions (Word document) or Homework 1 example solutions (pdf)

• Homework 2 example solutions (Word document) or Homework 2 example solutions (pdf)

• Example R code for Homework 2 solutions, Fall 2016

• Homework 3 example solutions (Word document) or Homework 3 example solutions (pdf)

• Example R code for Homework 3 solutions, Fall 2016

• Homework 4 example solutions (Word document) or Homework 4 example solutions (pdf)

• Example R code for Homework 4 solutions, Fall 2016

• Homework 5 example solutions (Word document) or Homework 5 example solutions (pdf)

• Example R code for Homework 5 solutions, Fall 2016

• Homework 6 example solutions (Word document) or Homework 6 example solutions (pdf)

• Example R code for Homework 6 solutions, Fall 2016

• Example R code for Homework 7 solutions, Fall 2016

Midterm Exam Information

• Midterm: (Word document) or (pdf document) (due on or before Thursday, Oct. 20 by 4:00 p.m.)

• Countries data with names (data set 1 for midterm exam)

• NBA Players Data with names (data set 2 for midterm exam)

Final Exam Information

• Final Exam: (Word document) or (pdf document) (due on or before Friday, Dec. 9 by 3:00 p.m.)

• Cereal data (with names) (data set 1 for final exam)

• Mammals Data (with names) (data set 2 for final exam)