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Gatignon H. — Statistical analysis of management data
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Название: Statistical analysis of management data
Автор: Gatignon H.
Аннотация: This book covers multivariate statistical analyses that are important for researchers in all fields of management whether finance, production, accounting, marketing, strategy, technology or human resources management. Although multivariate statistical techniques such as those described in this book play key roles in fundamental disciplines of the social sciences (e.g., economics and econometrics or psychology and psychometrics), the methodologies particularly relevant and typically used in management research are the center of focus of this study. 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.
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Рубрика: Математика /Вероятность /Статистика и приложения /
Статус предметного указателя: Готов указатель с номерами страниц
ed2k: ed2k stats
Год издания: 2002
Количество страниц: 335
Добавлена в каталог: 03.06.2005
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Предметный указатель
Performance information 324
Point estimation 57—59
Pooling tests and dummy variable models 67—69
Pooling tests, strategy for 69—71
Population Discrepancy Function Value (F0) 231 245
Preference data, analysis of 253 260—261
PREFMAP 261 306
PREFMAP, example of 292—306
Price sensitivity 316
Price-quantity map 88
Principal component analysis 6 37—38
Principal Component Analysis and Factor Analysis difference between 38
Principal Component Analysis, component loadings for 42
Probability density function of error vector 57
Probit models 112
Problem definition 255
PROC SYSLIN 93
Product characteristics 315
Profit see Property Fitting
Property Fitting (PROFIT) 259
Property Fitting (PROFIT), analysis 266
Property Fitting (PROFIT), example of 275—285
Property Fitting (PROFIT), procedures 258
Proximity data 306
Proximity data, analysis of 5
Proximity matrices 253
Proximity matrices, alternative 254—255
Proximity, derived measures of 254
Psychographics 318
Purchase behavior 320
Purchase, decision process 320
Purchase, intentions 165
Quadratic effect coding 141
Quantal choice models 112—121
Quantal choice models, fit measures 120
Range constraint problem 115
Rank order 253
Rank order, data 6 137
Rank-ordered dependent variables 144
Rank-ordered dissimilarities 256
Rank-ordered measures of dissimilarity 262
Rao's R 18 24
Ratio scale 3—4
Reflective indicators 38
Reliability 29—30 53
Reliability of a two-component scale 31—33
Reliability, coefficient a 44
Reliability, measurement of 29
Restricted residual sum of squares (RRSS) 66
Reversed regression 161—162
Rotation to terminal solution 42
Sales and advertising expenditures for brand as function of price model 76
Sales and advertising expenditures or market share 104
Sales and advertising expenditures, marketing example with 89
Sample centroids, multivariate distribution 13
Sample centroids, sampling distribution of 12—13
Sample centroids, univariate distribution 12
Sample distribution theory 168
SAS 2 19 21 42 44—45 49 52—53 103 144 236
SAS and 2SLS 100
SAS and SUR estimation 94 97—99
SAS for analysis of variance 52
SAS for computation of means and coefficients 46
SAS for reliability-coefficient a 48 50
SAS for scale construction 51—52
SAS in regression analysis 74
SAS, examples using 93—103
SAS, GLM procedure 143
SAS, input to perform the test of a mean vector 21
SAS, procedure REG 71
SAS, procedure SYSLIN 93 95—96
SAS, working data set 19
Scale construction, procedure for 43
Scales of measurement and their properties 4
Scales, types of 3—5
Scaling factor 256
SCAN.DAT 325 327
Scanner data 3 325
SCANNER.PAS 326—327
Seemingly Unrelated Regression (SUR) 79—83
Seemingly Unrelated Regression (SUR), estimator 99
Seemingly Unrelated Regression (SUR), example 93
Segmentation 53
Shepard diagram 262
Significance test, one-sample problem 13
Significance test, two-sample problem 15—17
Significance test, univariate test 13
Significance test, К-sample problem 17—18
Similarity data in management research 253
Similarity data, analysis of 6
Similarity data, problem definition 255
Similarity judgments, individual differences in 260
Simultaneity and identification 88—91
Simultaneous equations, system of 83—88
Single equation econometrics 6
Social sciences research in 162
Sonites 315—317
Space geometry 33
Specialty retail stores 316
Standard regression model with categorical dependent variables 112
Statistical analysis software 42
Statistical independence, model of 169
Statistical inference, least squares and maximum likelihood 55—65
Statistical tables 310—313
Statistics of fit 120
Stimuli, minimum number of 257
Stress as an index of fit 256
Structural model with measurement models, example of 221—250
Structural relationship parameters 171
Subject preferences and stimuli 306
Subsets of data, pooling issues 65—71
Sum of squares cross products (SSCP) matrix 14 16—17
Supply and demand curves 88
Supply and demand inter-relationships 88
SURVEY data 154 250
Survey, coding of variables 322—323
Survey, data 3
Survey, questionnaire and scale type 318—321
SURVEY.ASC Data File 53
Symmetric (half) matrix-missing diagonal 254
SYSLIN 101
SYSLIN, procedure and 2SLS 100—101
System of equations econometrics 6
Three Stage Least Squares (3SLS) 87—88 92—93
Three Stage Least Squares (3SLS), example 101—103
Total SSCP matrices 18
Transformational logit 113—117
Two Stage Least Squares (2SLS) 87 92—93
Two Stage Least Squares (2SLS), example 99—103
Two-groups discriminant analysis 128
Unbiasedness 61
Underlying perceptual dimensions 306
Unidimensionality 53
Unidimensionality, verification of 29
Unique components 40
Univariate normal distribution 9
Unordered model 147
Unrestricted residual sum of squares (URSS) 66
Variance maximizing rotations 34—37
Varimax rotation method 42
Vector and matrix differentiation 309
Vector, model of preference 260—261 293
Wilk, lambda of 18
Wilk, likelihood-ratio criterion of 17
Windows 130
Windows Explorer 148
Wold, non-linear iterative least squares procedure of 260
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