COURSE OUTLINE & SUPPLEMENTAL LEARNING MATERIALS:

PART I. EXPLORATORY MULTIVARIATE TECHNIQUES: "DATA MINING"
CONTENT		LEARNING MATERIAL
1.    Multivariate Data And Multivariate Statistics
	1.1        Introduction
	1.2        Types of Data
	1.3        Basic Multivariate Statistics
	1.4        The Aims of Multivariate Analysis
2.    Exploring Multivariate Data Graphically
	2.1        Introduction	* Introductory Notes and Graphics Notes. * R Graphics Examples Directory. Includes code for faces, stars, and coplots * SAS Face and Star Plot Directory  * The Parallel Coordinate Explorer A Java Applet allowing direct manipulation of data (on automobile models) shown in the form of a parallel coordinate plot.  Exercise: Starting with the default display, select first the Japanese cars, then the European, then the American ones. How do these groups differ on the other variables? Select the 8-cylinder cars. Where are they produced?            * Data/Statistical Graphics Gallery Some examples of the Best and Worst of Statistical Graphics. * Notes for beginning Linear Algebra and Graphics * MVA Notes * Notes on Graphical Displays
	2.2        The scatterplot
	2.3        Scatterplot Matrix
	2.4        Enhanced Scatterplots Chiplot and Bivariate Boxplot R Functions for chiplot, bivariate boxplot, and bivariate density plots
	2.5        Coplots and Trellis Graphics
	2.6        Probability Plots
	2.7        Other Plot
3.    Principal Components Analysis
	3.1        Introduction and Motivation	* PCA Notes * Fieller PCA Notes
	3.2        Presentation of Method
	3.3        Extensions
	3.4        Graphical Methods
	3.5        Applications and Examples
4.      Correspondence  Analysis
	4.1        Introduction and Motivation	* Correspondence Analysis Notes * Correspondence Analysis Notes  Forrest Young Notes * Correspondence Analysis Notes North Carolina State. Notes * Correspondence Analysis Notes * Categorical Data: Part 5: Correspondence analysis  (M Friendly)
	4.2        A Simple Example
	4.3        Two-Dimensional Tables
	4.4        Applications
	4.5        Multiple Correspondence Analysis
5.      Multidimensional Scaling (MDS)
	5.1        Introduction and Motivation	* F Young MDS Notes * Cornell MDS Notes * Statsoft Notes
	5.2        Proximity Matrices
	5.3        Classical MDS
	5.4        Metric LS MDS
	5.5        Non-metric MDS
	5.6        Non-Euclidean metrics
	5.7        Three-way MDS
	5.8        Inference in MDS
6.    Cluster Analysis
	6.1        Introduction and Motivation	* StatSoft Text Notes on Cluster Analysis * Clustering PDF Notes * SAS Cluster Analysis Examples * R Cluster Analysis Examples * PRIZM Marketing Segmentation Cluster Analysis Results
	6.2        Agglomerative Hierarchical Clustering  Techniques
	6.3        Optimization Methods
	6.4        Finite Mixture Models for Cluster Analysis	mixture distributions
PART II. CONFIRMATORY MULTIVARIATE TECHNIQUES[1]: "DATA CRAFTING"
7.      The Generalized Linear Models (GLM)
	7.1        Linear Models	* PA 765: Logistic Regression Notes * UCLA Logistic SAS Seminar * Indiana Notes * Logistic Links * Proc Logistic Notes
	7.2        Non-linear Models
	7.3        Link Functions, Error Distributions
8.      Regression and MANOVA
	8.1        Introduction and Motivation
	8.2        LS Estimation and ANOVA models
	8.3        Direct and Indirect Effects
9.      Log-Linear and Logistic Models
	9.1        Introduction and Motivation
	9.2        MLE
	9.3        Transition Models
10.    Multivariate Response Models
	10.1      Introduction and Motivation
	10.2      Repeated Measures
	10.3      Multivariate Tests
	10.4      Random Effects
	10.5      Logistic Models
	10.6      Marginal Models for Binary Response
	10.7      Marginal Modelling
	10.8      Generalized Random Effects
11.    Discrimination, Classification, and Pattern Recognition
	11.1      Introduction and Motivation	* Weka Website * Data Mining Notes * Tiberius Software Download Site (and information) * Neural Net Notes * Archaeology Neural Net Example Classification and Regression Trees in R
	11.2      Example
	11.3      Allocation Rules
	11.4      Fisher's Discriminant Function
	11.5      Assessing Discriminant Function
	11.6      Quadratic Discriminant Function
	11.7      More than Two Groups
	11.8      Logistic Discrimination
	11.9      Variable Selection
	11.10   Other Methods: CART
	11.11   Pattern Recognition, Neural Networks
12.    Exploratory Factor Analysis
	12.1  Introduction to Factor Analysis	* FA and SEM Notes             *SEM FAQ
	12.2  Basic Factor Analysis Model
	12.3  Estimation of the FA Model
	12.4  Rotation of Factors
	12.5  Estimating Factor Scores
	12.6  Factor Analysis vs. PCA
13.  Confirmatory Factor Analysis and Structural Equations Models
	13.1  Introduction	* SEM Notes * Proc Calis Information * Proc Calis Examples * Proc Calis Information * UCLA Proc Calis Examples * LSU FA and SEM Notes
	13.2  Path Analysis & Path Diagrams
	13.3  Structural Equations Models (SEM's)
	13.4  Assessment of  Fit
A.  Linear Algebra
	A.1 Matrix Algebra	* N Fieller Introductory Notes
	A.2 Basic Linear Algebra	* More Linear Algebra Notes. * Linear Algebra Notes.
	A.3 Eigenvalues & Eigen vectors	* Eigenvalue / Eigenvector * Demonstration Modules * Notes about SAS and Eigenvalues and Eigenvectors * SAS code, program for matrix algebra demonstration
	A.4 Distance	* Distance and Geometry Notes.

 

 

Engin A. Sungur, Spring 2005