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Statistical Analysis of Management Data

:	Hubert Gatignon, INSEAD, Fontainebleau, France
:	Statistical Analysis of Management Data
:	Kluwer Academic Publishers
:	9780306481659
:	1
:	CHF 87.00
:

:	Sonstiges
:	English

:	280
:	DRM
:	PC/MAC/eReader/Tablet
:	PDF

Statis ical 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.

Writ en for:
Researchers, scientists

	Contents	7
	Preface	12
	1. Introduction	13
	1.1 Overview	13
	1.2 Objectives	14
	1.2.1 Develop the Student’s Knowledge of the Technical Details of Various Techniques for Analyzing Data	14
	1.2.2 Expose the Students to Applications and “Hand-on” Use of Various Computer Programs for Carrying Out Statistical Analyses of Data	14
	1.3 Types of Scales	15
	1.3.1 Definition of Different Types of Scales	16
	1.3.2 The Impact of the Type of Scale on Statistical Analysis	16
	1.4 Topics Covered	17
	1.5 Pedagogy	18
	References	20
	2. Multivariate Normal Distribution	21
	2.1 Univariate Normal Distribution	21
	2.2 Bivariate Normal Distribution	21
	2.3 Generalization to Multivariate Case	23
	2.4 Tests About Means	24
	2.4.1 Sampling Distribution of Sample Centroids	24
	2.4.2 Significance Test: One-sample Problem	25
	2.4.3 Significance Test: Two-sample Problem	27
	2.4.4 Significance Test: K-sample Problem	29
	2.5 Examples	31
	2.5.1 Test of the Difference Between Two Mean Vectors – One- Sample Problem	31
	2.5.2 Test of the Difference Between Several Mean Vectors – K-sample Problem	33
	2.6 Assignment	37
	References	39
	Basic Technical Readings	39
	Application Readings	39
	3. Measurement Theory: Reliability and Factor Analysis	41
	3.1 Notions of Measurement Theory	41
	3.1.1 Definition of a Measure	41
	3.1.2 Parallel Measurements	41
	3.1.3 Reliability	41
	3.1.4 Composite Scales	42
	3.2 Factor Analysis	45
	3.2.1 Axis Rotation	45
	3.2.2 Variance Maximizing Rotations (Eigenvalues/vectors)	46
	3.2.3 Principal Component Analysis	49
	3.2.4 Factor Analysis	50
	3.3 Conclusion - Procedure for Scale Construction	55
	3.3.1 Exploratory Factor Analysis	55
	3.3.2 Confirmatory Factor Analysis	56
	3.3.3 Reliability-Coefficient	56
	3.4 Application Examples	56
	3.5 Assignment	65
	References	66
	Basic Technical Readings	66
	Application Readings	66
	4. Multiple Regression with a Single Dependent Variable	67
	4.1 Statistical Inference: Least Squares and Maximum Likelihood	67
	4.1.1 The Linear Statistical Model	67
	4.1.2 Point Estimation	69
	4.1.3 Maximum Likelihood Estimation	71
	4.1.4 Properties of Estimator	73
	4.2 Pooling Issues	77
	4.2.1 Linear Restrictions	77
	4.2.2 Pooling Tests and Dummy Variable Models	79
	4.2.3 Strategy for Pooling Tests	81
	4.3 Examples of Linear Model Estimation with SAS	83
	4.4 Assignment	87
	References	88
	Basic Technical Readings	88
	Application Readings	89
	5. System of Equations	91
	5.1 Seemingly Unrelated Regression (SUR)	91
	5.1.1 Set of Equations with Contemporaneously Correlated Disturbances	91
	5.1.2 Estimation	93
	5.1.3 Special Cases	94
	5.2 A System of Simultaneous Equations	95
	5.2.1 The Problem	95
	5.2.2 Two Stage Least Squares: 2SLS	99
	5.2.3 Three Stage Least Squares: 3SLS	99
	5.3 Simultaneity and Identification	100
	5.3.1 The Problem	100
	5.3.2 Order and Rank Conditions	101
	5.4 Summary	103
	5.4.1 Structure of Matrix	103
	5.4.2 Structure of Matrix	104
	5.4.3 Test of Covariance Matrix	104
	5.4.4 3SLS versus 2SLS	105
	5.5 Examples Using SAS	105
	5.5.1 Seemingly Unrelated Regression Example	105
	5.5.2 Two Stage Least Squares Example	111
	5.5.3 Three Stage Least Squares Example	113
	5.6 Assignment	115
	References	116
	Basic Technical Readings	116
	Application Readings	116
	6. Categorical Dependent Variables	117
	6.1 Discriminant Analysis	117
	6.1.1 The Discriminant Criterion	117
	6.1.2 Discriminant Function	120
	6.1.3 Classification and Fit	121
	6.2 Quantal Choice Models	124
	6.2.1 The Difficulties of the Standard Regression Model with Categorical Dependent Variables	124
	6.2.2 Transformational Logit	125
	6.2.3 Conditional Logit Model	129
	6.2.4 Fit Measures	132
	6.3 Examples	133
	6.3.1 Example of Discriminant Analysis Using SAS	133
	6.3.2 Example of Multinomial Logit – Case 1 Analysis Using LIMDEP	140
	6.3.3 Example of Multinomial Logit – Case 2 Analysis Using LOGIT. EXE	142
	6.3.4 Example of Multinomial Logit – Case 2 Analysis Using LIMDEP	145
	6.4 Assignment	147
	References	148
	Basic Technical Readings	148
	Application Readings	148
	7. Rank Ordered Data	149
	7.1 Conjoint Analysis – MONANOVA	149
	7.1.1 Effect Coding Versus Dummy Variable Coding	149
	7.1.2 Design Programs	154
	7.1.3 Estimation of Part-worth Coefficients	155
	7.2 Ordered Probit	156
	7.3 Examples	159
	7.3.1