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Author: Admin | 2025-04-28
STAT 530 (Applied Multivariate Statistics and Data Mining) Fall 2024 InstructorDavid Hitchcock, associate professor of statisticsSyllabusSyllabus: (pdf document)Office Hours -- Fall 2024Monday, Tuesday, Wednesday, Thursday 9:00-9:50 a.m. or by appointmentOffice: 202 LeConte CollegePhone: 777-5346E-mail: hitchcock@stat.sc.eduClass Meeting TimeTues-Thurs 10:05-11:20 a.m., LeConte College Room 224 Current TextbooksAn Introduction to Multivariate Analysis with R (2011), by Brian Everitt and Tolsten Holthorn. (available as a free (possibly only via USC computers) download at the textbook site). An Introduction to Statistical Learning with Applications in R (2013), by James, Witten, Hastie, and Tibshirani (available as free download at the ISL textbook site).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 Description530—Applied Multivariate Statistics and Data Mining (3) (Prereq: A grade of C or higher in STAT 515, STAT 205, STAT 509, STAT 512, ECON 436, MGSC 391, PSYC 228, or equivalent ) Introduction to fundamentals of multivariate statistics and data mining. Principal components and factor analysis; multidimensional scaling and cluster analysis; MANOVA and discriminant analysis; decision trees; and support vector machines. Use of appropriate software.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 ResourcesR is available as a free download (from the CRAN home page). These packages are also available on the computers inthe labs in LeConte College (and a few other buildings).Help in using R can be found on the CRAN home page. Course NotesClass 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 2Table 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 RInstructions for Downloading RStats with
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