Office: 215C LeConte College

Phone: 777-5346

E-mail: hitchcock@stat.sc.edu

__ 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.)

__ 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.

These packages are also available on the computers in the labs in LeConte College (and a few other buildings). Help in using R can be found on the CRAN home page.

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

- 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)

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

**Example R Code**

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

- 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)

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

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

- 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)

- 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)

- 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)

- Egyptian skulls data (from Table 5.8)

- SIDS data (from Table 7.5)

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

- 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!)

- Homework 1 example solutions (Word document)

- Homework 2 example solutions (Word document)

- Homework 3 example solutions (Word document)

- Homework 4 example solutions (Word document)

- Homework 5 example solutions (Word document)

- Homework 6 example solutions (Word document)

- Homework 7 example solutions (Word document)

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

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

- 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)