Instructor

Syllabus

Office Hours -- Fall 2022

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 (this is Chapter 5 in the new Everitt/Hothorn textbook)

• Chapter 4 Slides with Variance and Covariance Derivations

• Class Slides for Chapter 4, Part 2 (this is Chapter 5 in the new Everitt/Hothorn textbook)

• Class Slides for Chapter 5 (this is Chapter 4 in the new Everitt/Hothorn textbook)

• 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) (this is not part of the new Everitt/Hothorn book)

• 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

• Some Brief Notes for Chapter 9

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, Pirate plots)

• 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 (this is Chapter 5 in the new Everitt/Hothorn textbook) (Factor Analysis, including rotations, plotting factor scores, and model diagnostics)

• Chapter 5 example R code (this is Chapter 4 in the new Everitt/Hothorn textbook) (Multidimensional Scaling and Correspondence Analysis)

• Chapter 6 example R code (Cluster Analysis)

• Chapter 7 example R code (Discriminant Analysis, Classification using Logistic Regression) (not part of new Everitt/Hothorn book)

• 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

• Timber data example R code (A simple repeated measures analysis)

• Plasma data example R code (Another repeated measures analysis)

• Spotify (subset) data example R code (Another repeated measures analysis)

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)

• Spotify dataset (.csv file) (from 'bayesrules' package)

• 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)

• Caravan Data

• 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 2022 (due Wednesday, Aug. 31 by 11:59 p.m.)
(Instructions for uploading via Blackboard) (NOTE: Please use Chrome or Firefox when uploading assignments in Blackboard, not Safari!)

• Homework 2, Fall 2022 (due Wednesday, Sept. 14 by 11:59 p.m.)
(Instructions for uploading via Blackboard) (NOTE: Please use Chrome or Firefox when uploading assignments in Blackboard, not Safari!)

• Homework 3, Fall 2022 (due Monday, Sept. 26 by 11:59 p.m.)
(Instructions for uploading via Blackboard) (NOTE: Please use Chrome or Firefox when uploading assignments in Blackboard, not Safari!)

• Homework 4, Fall 2022 (due Wednesday, Oct. 5 by 11:59 p.m.)
(Instructions for uploading via Blackboard) (NOTE: Please use Chrome or Firefox when uploading assignments in Blackboard, not Safari!)

• Homework 5, Fall 2022 (due Wednesday, November 2 by 11:59 p.m.)
(Instructions for uploading via Blackboard) (NOTE: Please use Chrome or Firefox when uploading assignments in Blackboard, not Safari!)

• Homework 6, Fall 2022 (due Wednesday, November 16 by 11:59 p.m.)
(Instructions for uploading via Blackboard) (NOTE: Please use Chrome or Firefox when uploading assignments in Blackboard, not Safari!)

• Homework 7, Fall 2022 (due Wednesday, Nov. 30 by 11:59 p.m.)
(Instructions for uploading via Blackboard) (NOTE: Please use Chrome or Firefox when uploading assignments in Blackboard, not Safari!)

Some Homework Solution Code and General Comments

• Homework 1 example solutions (Word document)

• Example R code for Homework 1 solutions

• Homework 2 example solutions (Word document)

• Example R code for Homework 2 solutions

• Homework 3 example solutions (Word document)

• Example R code for Homework 3 solutions

• Homework 4 example solutions (Word document)

• Example R code for Homework 4 solutions

• Homework 5 example solutions (Word document)

• Example R code for Homework 5 solutions, Fall 2022

• Homework 6 example solutions (Word document)

• Example R code for Homework 6 solutions, Fall 2022

• Homework 7 example solutions (Word document)

• Example R code for Homework 7 solutions, Fall 2022

Project Information

• Project Information, Fall 2022

Midterm Exam Information

• Midterm: (pdf document, 5 pages) (due on or before Tuesday, Oct. 11 by 11:59 p.m.)

• R code to read in data for Midterm Exam (Use this!)

• Bulls data (data set 1 for midterm exam; the R code above will read this into R)

• NBA Players Data 2022 (data set 2 for midterm exam; the R code above will read this into R)

Final Exam Information

• Final Exam:(pdf document, 4 pages) (due on or before Thursday, Dec. 8 by 4:00 p.m.)

• R code to read in data for Final Exam (Use this!)

• Mammals Data (data set 1 for final exam; the R code above will read this into R)

• Forest Fire Data with no IDs or Forest Fire Data with a column of ID numbers (data set 2 for final exam; the R code above will read this into R)