Statistical Analysis of Management Data
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 fie
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Statistical Analysis of Management Data
Hubert Gatignon The Claude Janssen Chaired Professor of Business Administration and Professor of Marketing INSEAD
KLUWER ACADEMIC PUBLISHERS NEW YORK, BOSTON, DORDRECHT, LONDON, MOSCOW
eBook ISBN: Print ISBN:
0-306-48165-0 1-4020-7315-1
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To my daughters, Aline and Valérie
Contents
Preface 1 Introduction 1.1 Overview 1.2 Objectives 1.2.1 Develop the Student’s 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
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Contents
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
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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
