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雷达卡
一本挺好的书，感兴趣的，识货的请看看吧。格式是DjVu的，实在是懒得转格式了，不好意思了。
Statistical Analysis of Management Data
Gatignon, Hubert
KLUWER ACADEMIC PUBLISHER, 2003
ISBN: 1-4020-7315-1
Statistical Analysis of Management Data is especially designed to provide doctoral students with a theoretical knowledge of the basic concepts underlying the most important multivariate techniques and with an overview of actual applications in various fields. The content herein addresses both the underlying mathematics and problems of application. As such, a reasonable level of competence in both statistics and mathematics is needed. This book is not intended as a first introduction to statistics and statistical analysis. Instead it assumes that the student is familiar with basic statistical techniques. The techniques are presented in a fundamental way but in a format accessible to students in a doctoral program, to practicing academicians, and to data analysts.
Contents
Preface
1. Introduction
1.1 Overview
1.2 Objectives
1.2.1 Develop the Students Knowledge of the Technical Details of Various Techniques for Analyzing Data
1.2.2 Expose the Students to Applications and "Hand-on" Use of Various Computer Programs for Carrying Out Statistical Analyses of Data
1.3 Types of Scales
1.3.1 Definition of Different Types of Scales
1.3.2 The Impact of the Type of Scale on Statistical Analysis
1.4 Topics Covered
1.5 Pedagogy
References
2. Multivariate Normal Distribution
2.1 Univariate Normal Distribution
2.2 Bivariate Normal Distribution
2.3 Generalization to Multivariate Case
2.4 Tests About Means
2.4.1 Sampling Distribution of Sample Centroids
2.4.2 Significance Test: One-sample Problem
2.4.3 Significance Test: Two-sample Problem
2.4.4 Significance Test: K-sample Problem
2.5 Examples
2.5.1 Test of the Difference Between Two Mean Vectors - One- Sample Problem
2.5.2 Test of the Difference Between Several Mean Vectors - K-sample Problem
2.6 Assignment
References
Basic Technical Readings
Application Readings
3. Measurement Theory: Reliability and Factor Analysis
3.1 Notions of Measurement Theory
3.1.1 Definition of a Measure
3.1.2 Parallel Measurements
3.1.3 Reliability
3.1.4 Composite Scales
3.2 Factor Analysis
3.2.1 Axis Rotation
3.2.2 Variance Maximizing Rotations (Eigenvalues/vectors)
3.2.3 Principal Component Analysis
3.2.4 Factor Analysis
3.3 Conclusion - Procedure for Scale Construction
3.3.1 Exploratory Factor Analysis
3.3.2 Confirmatory Factor Analysis
3.3.3 Reliability-Coefficient
3.4 Application Examples
3.5 Assignment
References
Basic Technical Readings
Application Readings
4. Multiple Regression with a Single Dependent Variable
4.1 Statistical Inference: Least Squares and Maximum Likelihood
4.1.1 The Linear Statistical Model
4.1.2 Point Estimation
4.1.3 Maximum Likelihood Estimation
4.1.4 Properties of Estimator
4.2 Pooling Issues
4.2.1 Linear Restrictions
4.2.2 Pooling Tests and Dummy Variable Models
4.2.3 Strategy for Pooling Tests
4.3 Examples of Linear Model Estimation with SAS
4.4 Assignment
References
Basic Technical Readings
Application Readings
5. System of Equations
5.1 Seemingly Unrelated Regression (SUR)
5.1.1 Set of Equations with Contemporaneously Correlated Disturbances
5.1.2 Estimation
5.1.3 Special Cases
5.2 A System of Simultaneous Equations
5.2.1 The Problem
5.2.2 Two Stage Least Squares: 2SLS
5.2.3 Three Stage Least Squares: 3SLS
5.3 Simultaneity and Identification
5.3.1 The Problem
5.3.2 Order and Rank Conditions
5.4 Summary
5.4.1 Structure of Matrix
5.4.2 Structure of Matrix
5.4.3 Test of Covariance Matrix
5.4.4 3SLS versus 2SLS
5.5 Examples Using SAS
5.5.1 Seemingly Unrelated Regression Example
5.5.2 Two Stage Least Squares Example
5.5.3 Three Stage Least Squares Example
5.6 Assignment
References
Basic Technical Readings
Application Readings
6. Categorical Dependent Variables
6.1 Discriminant Analysis
6.1.1 The Discriminant Criterion
6.1.2 Discriminant Function
6.1.3 Classification and Fit
6.2 Quantal Choice Models
6.2.1 The Difficulties of the Standard Regression Model with Categorical Dependent Variables
6.2.2 Transformational Logit
6.2.3 Conditional Logit Model
6.2.4 Fit Measures
6.3 Examples
6.3.1 Example of Discriminant Analysis Using SAS
6.3.2 Example of Multinomial Logit - Case 1 Analysis Using LIMDEP
6.3.3 Example of Multinomial Logit - Case 2 Analysis Using LOGIT. EXE
6.3.4 Example of Multinomial Logit - Case 2 Analysis Using LIMDEP
6.4 Assignment
References
Basic Technical Readings
Application Readings
7. Rank Ordered Data
7.1 Conjoint Analysis - MONANOVA
7.1.1 Effect Coding Versus Dummy Variable Coding
7.1.2 Design Programs
7.1.3 Estimation of Part-worth Coefficients
7.2 Ordered Probit
7.3 Examples
7.3.1 Example of MONANOVA Using PC-MDS
7.3.2 Example of Conjoint Analysis Using SAS
7.3.3 Example of Ordered Probit Analysis Using LIMDEP
7.4 Assignment
References
Basic Technical Readings
Application Readings
8. Error in Variables - Analysis of Covariance Structure
8.1 The Impact of Imperfect Measures
8.1.1 Effect of Errors-in-variables
8.1.2 Reversed Regression
8.1.3 Case with Multiple Independent Variables
8.2 Analysis of Covariance Structures
8.2.1 Description of Model
8.2.2 Estimation
8.2.3 Model Fit
8.2.4 Test of Significance of Model Parameters
8.2.5 Simultaneous Estimation of Measurement Model Parameters with Structural Relationship Parameters Versus Sequential Estimation
8.2.6 Identification
8.3 Examples
8.3.1 Example of Confirmatory Factor Analysis
8.3.2 Example of Model to Test Discriminant Validity Between Two Constructs
8.3.3 Example of Structural Model with Measurement Models
8.4 Assignment
References
Basic Technical Readings
Application Readings
9. Analysis of Similarity and Preference Data
9.1 Proximity Matrices
9.1.1 Metric versus Non-metric Data
9.1.2 Unconditional versus Conditional Data
9.1.3 Derived Measures of Proximity
9.1.4 Alternative Proximity Matrices
9.2 Problem Definition
9.2.1 Objective Function
9.2.2 Stress as an Index of Fit
9.2.3 Metric
9.2.4 Minimum Number of Stimuli
9.2.5 Dimensionality
9.2.6 Interpretation of MDS Solution
9.2.7 The KYST Algorithm
9.3 Individual Differences in Similarity Judgments
9.4 Analysis of Preference Data
9.4.1 Vector Model of preferences
9.4.2 Ideal Point Model of Preference
9.5 Examples
9.5.1 Example of KYST
9.5.2 Example of INDSCAL
9.5.3 Example of PROFIT (Property Fitting) Analysis
9.5.4 Example of MDPREF
9.5.5 Example of PREFMAP
9.6 Assignment
References
Basic Technical Readings
Application Readings
Appendices
Appendix A: Rules in Matrix Algebra
Appendix B: Statistical Tables
Appendix C: Description of Data Sets
Index
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