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
*
Read MV Graphics Articles on E-Reserve

2.2        The scatterplot

2.3        Scatterplot Matrix

2.4        Enhanced Scatterplots

2.5        Coplots and Trellis Graphics

2.6        Probability Plots

2.7        Other Plots

3.    Principal Components Analysis

 

3.1        Introduction and Motivation

* PCA Notes
* Fieller PCA Notes

* R Examples for Principal Components Analysis (PCA)

* SAS Examples for Principal Components Analysis (PCA)
*
Handout Examples for Principal Components Analysis (PCA)

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

* Read Africa CA Article on E-Reserve
* Correspondence Analysis Notes

* Correspondence Analysis Notes
  Forrest Young Notes
* Correspondence Analysis Notes
North Car St. Notes
* Correspondence Analysis Notes

* Categorical Data: Part 5: Correspondence analysis  (M Friendly)
*
R CA Examples
* SAS CA Examples

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

* Proc MDS Documentation in SAS

* Proc MDS Overview

* Proc MDS Example in SAS

* SAS MDS Examples

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

* Logistic Regression Lecture Notes
* PA 765: Logistic Regression Notes
* UCLA Logistic SAS Seminar
*
Indiana Notes
* Logistic Links

* SAS Logistic Regression Programs
*
Proc Logistic Notes

7.2        Non-linear Models

7.3        Link Functions, Error Distributions

8.      Regression and MANOVA

 

8.1        Introduction and Motivation

* SAS MANOVA Programs
* MANOVA Notes

* MANOVA Resource Site

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

* SAS Discriminant Analysis Programs
* CART Examples in R

* SAS CHAID Analysis Programs

* Neural Network and other Example Data

* Weka Website

* Data Mining Notes

* Tiberius Software Download Site (and information)

* Neural Net Notes

* Archaeology Neural Net Example

11.2      Example

11.3      Allocation Rules

11.4      FisherŐs 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

11.11   Pattern Recognition, Neural Networks

12.    Exploratory Factor Analysis

 

12.1  Introduction to Factor Analysis

 

* SAS Exploratory FA Programs           
* 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 * SAS SEM Programs
* SEM Notes

* Proc Calis Information

* Proc Calis Examples
* Proc Calis Information

* UCLA Proc Calis Examples
*
LSU FA and SEM Notes
*
Proc Calis 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

* S Rathbun Lin Algebra Notes
* N Fieller Introductory Notes

A.2 Basic Linear Algebra

* Linear Algebra Notes.
* MV parameters and sample estimates.

* 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

* R Examples for Linear Algebra and Eigenvectors

A.4 Distance

* Distance and Geometry Notes.
* Data Dimensionality Notes

 

 


Engin A. Sungur, Spring 2005