Statistical modeling for management:
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Los Angeles [u.a.]
Sage
2008
|
| Edition: | 1. publ. |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Angekündigt als: Marketing measurement and analysis |
| Physical Description: | XVIII, 235 S. |
| ISBN: | 0761970118 9780761970125 9780761970118 |
Staff View
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| 100 | 1 | |a Hutcheson, Graeme |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Statistical modeling for management |c Graeme D. Hutcheson ; Luiz Moutinho |
| 246 | 1 | 3 | |a Marketing measurement and analysis |
| 250 | |a 1. publ. | ||
| 264 | 1 | |a Los Angeles [u.a.] |b Sage |c 2008 | |
| 300 | |a XVIII, 235 S. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Angekündigt als: Marketing measurement and analysis | ||
| 650 | 4 | |a Statistische Methode / Ökonometrisches Modell / Betriebsanalyse / Performance Measurement / Marketing-Informationssystem / Management-Informationssystem / Data-Warehouse-System | |
| 650 | 4 | |a Mathematisches Modell | |
| 650 | 4 | |a Marketing |x Mathematical models | |
| 650 | 4 | |a Marketing |x Statistical methods | |
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Record in the Search Index
| _version_ | 1817681153887305728 |
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| adam_text |
Contents
List
of Tables
. ix
List of Figures
. xv
Preface
.xvii
1
Measurement Scales
1
1.1
Continuous Data
. 2
1.1.
1 The underlying distribution
. 2
1.1.2
Recording the data
. 3
1.1.3
Applying mathematical operations
. 5
1.2
Ordered Categorical Data
. 6
1.2.1
The underlying distribution
. 6
1.2.2
Recording the data
. 7
1.2.3
Applying mathematical operations
. 11
1.3
Unordered Categorical Data
. 13
1.3.1
The underlying distribution
. 13
1.3.2
Recording the data
. 13
1.3.3
Applying mathematical operations
. 14
1.4
Conclusion
. 15
2
Modeling Continuous Data
17
2.1
The Generalized Linear Model
. 17
2.2
The Ordinary Least-Squares Model
. 19
2.2.1
Simple OLS regression
. 19
Computing and interpreting model parameters
. 21
Predicting the response variable
. 22
Goodness-of-fit statistics
. 23
2.2.2
Multiple OLS regression
. 29
Computing and interpreting model parameters
. 29
Predicting the response variable
. 31
Goodness-of-fit statistics
. 31
2.3
Categorical Explanatory Variables
. 35
2.4
Analyzing Simple Experimental Designs for Continuous Data
. 36
2.4.1
Unrelated groups design
. 36
Comparing two groups using OLS regression
. 36
vi
CONTENTS
Comparing more than two groups using OLS regression
. 42
Comparing two groups using a t-test
. 46
Comparing more than two groups using ANOVA
. 47
2.4.2
Related groups designs
. 47
Comparing two groups using OLS regression
. 48
Comparing more than two groups using OLS regression
. 53
Comparing two groups using a t-test
. 54
Comparing more than two groups using ANOVA
. 55
2.5
Conclusion
. 55
Modeling Dichotomous Data
57
3.1
The Generalized Linear Model
. 57
3.2
The Logistic Regression Model
. 62
3.2.1
Simple logistic regression
. 62
Computing and Interpreting Model Parameters
. 63
Predicted probabilities
. 65
Goodness-of-fit statistics
. 66
3.2.2
Multiple Logistic Regression
. 69
Computing and interpreting model parameters
. 71
Predicted probabilities
. 73
Goodness-of-fit statistics
. 73
3.2.3
Categorical explanatory variables
. 76
Computing and interpreting model parameters
. 77
Predicted probabilities
. 78
Goodness-of-fit statistics
. 80
3.3
Analyzing Simple Experimental Designs for Dichotomous Data
. 82
3.4
Conclusion
. 82
Modeling Ordered Data
83
4.1
The Generalized Linear Model
. 83
4.2
The Proportional Odds Model
. 83
4.2.1
Simple proportional odds
. 85
Checking the proportional odds assumption
. 86
Computing and interpreting model parameters
. 88
Predicted probabilities
. 89
Goodness-of-fit statistics
. 90
4.2.2
Multiple proportional odds
. 93
Checking the proportional odds assumption
. 94
Computing and interpreting model parameters
. 94
Predicted probabilities
. 96
Goodness-of-fit statistics
. 97
4.2.3
Categorical explanatory variables
. 100
4.3
Analyzing Simple Experimental Designs for Ordered Data
. 106
4.3.1
Unrelated groups design
. 107
4.3.2
Related groups design
. 113
4.4
Conclusion
. 119
CONTENTS
vii
5
Modeling Unordered Data
121
5.1
The Generalized Linear Model
. 121
5.2
The Multi-nomial Logistic Regression Model
. 122
5.2.1
Simple multi-nomial logistic regression
. 123
Computing and interpreting model parameters
. 123
Predicted probabilities
. 126
Goodness-of-fit statistics
. 127
5.2.2
Multiple multi-nomial logistic regression including
categorical variables
. 130
Computing and interpreting model parameters
. 131
Predicted probabilities
. 133
Goodness-of-fit statistics
. 134
5.3
Analyzing Simple Experimental Designs for Unordered Data
. 137
5.3.1
Unrelated groups design
. 137
5.3.2
Related groups design
. 145
5.4
Conclusion
. 152
6
Neural Networks
153
6.1
Cognitive Theory
-
Nodes and Links
-
Mental Manipulation of Data
. . 153
6.1.1
Roots: A parallel model of the brain
.153
6.1.2
Neural networks embody a process of learning
.154
6.1.3
Implementation of NNs
.157
6.1.4
The backpropagation algorithm (BP)
.158
Details of the algorithm
.161
6.1.5
Basic properties of the
SOM
.164
6.1.6
Potential benefits of the approach
.167
6.1.7
Business applications
.167
6.2
Example-Applications
.168
6.2.1
The research model
.168
6.2.2
Analysis of the data
.170
6.2.3
Labeling of hidden nodes: male buyers
.171
6.2.4
Labeling of hidden nodes: female buyers
.172
6.2.5
Findings: male car buyers
.172
6.2.6
Findings: female car buyers
.174
6.2.7
Conclusions and implications
.175
7
Approximate Algorithms for Management Problems
177
7.1
Genetic Algorithms
. 177
7.1.1
Site location analysis using genetic algorithms
. 179
7.1.2
Chromosome representation
. 180
7.1.3
Fitness function
. 180
7.1.4
Genetic operators
. 181
7.1.5
Simple illustration
. 181
7.2
Tabu Search
. 182
7.2.1
Example
. 183
viii CONTENTS
7.2.2
Application
of tabu search to
segmentation
. 183
7.3
Simulated Annealing
. 184
7.3.1
Sales territory design using simulated annealing
. 185
7.3.2
Information about a single
SCU
. 186
7.3.3
The model
. 187
7.3.4
Contiguity
. 188
7.3.5
Equal workload
. 188
7.3.6
Equality of sales potential
. 188
7.3.7
Profit
. 189
7.3.8
Application of simulated annealing
. 190
8
Other Statistical, Mathematical and Co-pattern Modeling Techniques
191
8.1
Discriminant Analysis
.191
8.2
Automatic Interaction Detection (AID)
.191
8.3
Logical Type Discriminant Models: The C5 Algorithm
.192
8.4
Multidimensional Scaling
.198
8.5
Conjoint Analysis
.199
8.6
Correspondence Analysis
.200
8.7
Latent Analysis
.201
8.8
Fuzzy Sets
.203
8.9
Fuzzy Decision Trees
.205
8.10
Artificial Intelligence
.206
8.11
Expertsystems
.207
8.11.1
Method based on marketing main advantages main
limitations applications
.207
8.12
Fuzzy Logic and Fuzzy Expert Systems
.208
8.13
Rough Set Theory
.208
8.14
Variable Precision Rough Sets (VPRS)
.210
8.14.1
An overview of VPRS
.210
8.15
Dempster-Shafer Theory
.212
8.16
Chaos Theory
.215
8.17
Data Mining
.217
8.17.1
Mining and refining data
.217
8.17.2
Siftware
.218
8.17.3
Invasion of the data snatchers
.218
8.17.4
Mining with query tools
.219
8.18
Data Mining
.220
8.18.1
A computing perspective on data mining
.221
8.18.2
Data mining tools
.221
8.18.3
Try new data-mining techniques
-
they can overcome,
augment traditional
stat
analysis
.221
References
223
Index
231 |
| adam_txt |
Contents
List
of Tables
. ix
List of Figures
. xv
Preface
.xvii
1
Measurement Scales
1
1.1
Continuous Data
. 2
1.1.
1 The underlying distribution
. 2
1.1.2
Recording the data
. 3
1.1.3
Applying mathematical operations
. 5
1.2
Ordered Categorical Data
. 6
1.2.1
The underlying distribution
. 6
1.2.2
Recording the data
. 7
1.2.3
Applying mathematical operations
. 11
1.3
Unordered Categorical Data
. 13
1.3.1
The underlying distribution
. 13
1.3.2
Recording the data
. 13
1.3.3
Applying mathematical operations
. 14
1.4
Conclusion
. 15
2
Modeling Continuous Data
17
2.1
The Generalized Linear Model
. 17
2.2
The Ordinary Least-Squares Model
. 19
2.2.1
Simple OLS regression
. 19
Computing and interpreting model parameters
. 21
Predicting the response variable
. 22
Goodness-of-fit statistics
. 23
2.2.2
Multiple OLS regression
. 29
Computing and interpreting model parameters
. 29
Predicting the response variable
. 31
Goodness-of-fit statistics
. 31
2.3
Categorical Explanatory Variables
. 35
2.4
Analyzing Simple Experimental Designs for Continuous Data
. 36
2.4.1
Unrelated groups design
. 36
Comparing two groups using OLS regression
. 36
vi
CONTENTS
Comparing more than two groups using OLS regression
. 42
Comparing two groups using a t-test
. 46
Comparing more than two groups using ANOVA
. 47
2.4.2
Related groups designs
. 47
Comparing two groups using OLS regression
. 48
Comparing more than two groups using OLS regression
. 53
Comparing two groups using a t-test
. 54
Comparing more than two groups using ANOVA
. 55
2.5
Conclusion
. 55
Modeling Dichotomous Data
57
3.1
The Generalized Linear Model
. 57
3.2
The Logistic Regression Model
. 62
3.2.1
Simple logistic regression
. 62
Computing and Interpreting Model Parameters
. 63
Predicted probabilities
. 65
Goodness-of-fit statistics
. 66
3.2.2
Multiple Logistic Regression
. 69
Computing and interpreting model parameters
. 71
Predicted probabilities
. 73
Goodness-of-fit statistics
. 73
3.2.3
Categorical explanatory variables
. 76
Computing and interpreting model parameters
. 77
Predicted probabilities
. 78
Goodness-of-fit statistics
. 80
3.3
Analyzing Simple Experimental Designs for Dichotomous Data
. 82
3.4
Conclusion
. 82
Modeling Ordered Data
83
4.1
The Generalized Linear Model
. 83
4.2
The Proportional Odds Model
. 83
4.2.1
Simple proportional odds
. 85
Checking the proportional odds assumption
. 86
Computing and interpreting model parameters
. 88
Predicted probabilities
. 89
Goodness-of-fit statistics
. 90
4.2.2
Multiple proportional odds
. 93
Checking the proportional odds assumption
. 94
Computing and interpreting model parameters
. 94
Predicted probabilities
. 96
Goodness-of-fit statistics
. 97
4.2.3
Categorical explanatory variables
. 100
4.3
Analyzing Simple Experimental Designs for Ordered Data
. 106
4.3.1
Unrelated groups design
. 107
4.3.2
Related groups design
. 113
4.4
Conclusion
. 119
CONTENTS
vii
5
Modeling Unordered Data
121
5.1
The Generalized Linear Model
. 121
5.2
The Multi-nomial Logistic Regression Model
. 122
5.2.1
Simple multi-nomial logistic regression
. 123
Computing and interpreting model parameters
. 123
Predicted probabilities
. 126
Goodness-of-fit statistics
. 127
5.2.2
Multiple multi-nomial logistic regression including
categorical variables
. 130
Computing and interpreting model parameters
. 131
Predicted probabilities
. 133
Goodness-of-fit statistics
. 134
5.3
Analyzing Simple Experimental Designs for Unordered Data
. 137
5.3.1
Unrelated groups design
. 137
5.3.2
Related groups design
. 145
5.4
Conclusion
. 152
6
Neural Networks
153
6.1
Cognitive Theory
-
Nodes and Links
-
Mental Manipulation of Data
. . 153
6.1.1
Roots: A parallel model of the brain
.153
6.1.2
Neural networks embody a process of learning
.154
6.1.3
Implementation of NNs
.157
6.1.4
The backpropagation algorithm (BP)
.158
Details of the algorithm
.161
6.1.5
Basic properties of the
SOM
.164
6.1.6
Potential benefits of the approach
.167
6.1.7
Business applications
.167
6.2
Example-Applications
.168
6.2.1
The research model
.168
6.2.2
Analysis of the data
.170
6.2.3
Labeling of hidden nodes: male buyers
.171
6.2.4
Labeling of hidden nodes: female buyers
.172
6.2.5
Findings: male car buyers
.172
6.2.6
Findings: female car buyers
.174
6.2.7
Conclusions and implications
.175
7
Approximate Algorithms for Management Problems
177
7.1
Genetic Algorithms
. 177
7.1.1
Site location analysis using genetic algorithms
. 179
7.1.2
Chromosome representation
. 180
7.1.3
Fitness function
. 180
7.1.4
Genetic operators
. 181
7.1.5
Simple illustration
. 181
7.2
Tabu Search
. 182
7.2.1
Example
. 183
viii CONTENTS
7.2.2
Application
of tabu search to
segmentation
. 183
7.3
Simulated Annealing
. 184
7.3.1
Sales territory design using simulated annealing
. 185
7.3.2
Information about a single
SCU
. 186
7.3.3
The model
. 187
7.3.4
Contiguity
. 188
7.3.5
Equal workload
. 188
7.3.6
Equality of sales potential
. 188
7.3.7
Profit
. 189
7.3.8
Application of simulated annealing
. 190
8
Other Statistical, Mathematical and Co-pattern Modeling Techniques
191
8.1
Discriminant Analysis
.191
8.2
Automatic Interaction Detection (AID)
.191
8.3
Logical Type Discriminant Models: The C5 Algorithm
.192
8.4
Multidimensional Scaling
.198
8.5
Conjoint Analysis
.199
8.6
Correspondence Analysis
.200
8.7
Latent Analysis
.201
8.8
Fuzzy Sets
.203
8.9
Fuzzy Decision Trees
.205
8.10
Artificial Intelligence
.206
8.11
Expertsystems
.207
8.11.1
Method based on marketing main advantages main
limitations applications
.207
8.12
Fuzzy Logic and Fuzzy Expert Systems
.208
8.13
Rough Set Theory
.208
8.14
Variable Precision Rough Sets (VPRS)
.210
8.14.1
An overview of VPRS
.210
8.15
Dempster-Shafer Theory
.212
8.16
Chaos Theory
.215
8.17
Data Mining
.217
8.17.1
Mining and refining data
.217
8.17.2
Siftware
.218
8.17.3
Invasion of the data snatchers
.218
8.17.4
Mining with query tools
.219
8.18
Data Mining
.220
8.18.1
A computing perspective on data mining
.221
8.18.2
Data mining tools
.221
8.18.3
Try new data-mining techniques
-
they can overcome,
augment traditional
stat
analysis
.221
References
223
Index
231 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Hutcheson, Graeme Moutinho, Luiz 1949- |
| author_GND | (DE-588)131450204 |
| author_facet | Hutcheson, Graeme Moutinho, Luiz 1949- |
| author_role | aut aut |
| author_sort | Hutcheson, Graeme |
| author_variant | g h gh l m lm |
| building | Verbundindex |
| bvnumber | BV021818751 |
| callnumber-first | H - Social Science |
| callnumber-label | HF5415 |
| callnumber-raw | HF5415 |
| callnumber-search | HF5415 |
| callnumber-sort | HF 45415 |
| callnumber-subject | HF - Commerce |
| classification_rvk | QP 300 QP 327 |
| ctrlnum | (OCoLC)255616173 (DE-599)BVBBV021818751 |
| dewey-full | 658.800151 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.800151 |
| dewey-search | 658.800151 |
| dewey-sort | 3658.800151 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| edition | 1. publ. |
| format | Book |
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| id | DE-604.BV021818751 |
| illustrated | Not Illustrated |
| index_date | 2024-07-02T15:53:23Z |
| indexdate | 2024-12-06T09:03:51Z |
| institution | BVB |
| isbn | 0761970118 9780761970125 9780761970118 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-015030929 |
| oclc_num | 255616173 |
| open_access_boolean | |
| owner | DE-703 DE-355 DE-BY-UBR DE-945 DE-11 DE-188 |
| owner_facet | DE-703 DE-355 DE-BY-UBR DE-945 DE-11 DE-188 |
| physical | XVIII, 235 S. |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Sage |
| record_format | marc |
| spelling | Hutcheson, Graeme Verfasser aut Statistical modeling for management Graeme D. Hutcheson ; Luiz Moutinho Marketing measurement and analysis 1. publ. Los Angeles [u.a.] Sage 2008 XVIII, 235 S. txt rdacontent n rdamedia nc rdacarrier Angekündigt als: Marketing measurement and analysis Statistische Methode / Ökonometrisches Modell / Betriebsanalyse / Performance Measurement / Marketing-Informationssystem / Management-Informationssystem / Data-Warehouse-System Mathematisches Modell Marketing Mathematical models Marketing Statistical methods Management (DE-588)4037278-9 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Statistische Analyse (DE-588)4116599-8 gnd rswk-swf Statistisches Modell (DE-588)4121722-6 gnd rswk-swf Datenerhebung (DE-588)4155272-6 gnd rswk-swf Management (DE-588)4037278-9 s Datenerhebung (DE-588)4155272-6 s Statistische Analyse (DE-588)4116599-8 s Statistisches Modell (DE-588)4121722-6 s DE-604 Datenanalyse (DE-588)4123037-1 s DE-188 Moutinho, Luiz 1949- Verfasser (DE-588)131450204 aut Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015030929&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Hutcheson, Graeme Moutinho, Luiz 1949- Statistical modeling for management Statistische Methode / Ökonometrisches Modell / Betriebsanalyse / Performance Measurement / Marketing-Informationssystem / Management-Informationssystem / Data-Warehouse-System Mathematisches Modell Marketing Mathematical models Marketing Statistical methods Management (DE-588)4037278-9 gnd Datenanalyse (DE-588)4123037-1 gnd Statistische Analyse (DE-588)4116599-8 gnd Statistisches Modell (DE-588)4121722-6 gnd Datenerhebung (DE-588)4155272-6 gnd |
| subject_GND | (DE-588)4037278-9 (DE-588)4123037-1 (DE-588)4116599-8 (DE-588)4121722-6 (DE-588)4155272-6 |
| title | Statistical modeling for management |
| title_alt | Marketing measurement and analysis |
| title_auth | Statistical modeling for management |
| title_exact_search | Statistical modeling for management |
| title_exact_search_txtP | Statistical modeling for management |
| title_full | Statistical modeling for management Graeme D. Hutcheson ; Luiz Moutinho |
| title_fullStr | Statistical modeling for management Graeme D. Hutcheson ; Luiz Moutinho |
| title_full_unstemmed | Statistical modeling for management Graeme D. Hutcheson ; Luiz Moutinho |
| title_short | Statistical modeling for management |
| title_sort | statistical modeling for management |
| topic | Statistische Methode / Ökonometrisches Modell / Betriebsanalyse / Performance Measurement / Marketing-Informationssystem / Management-Informationssystem / Data-Warehouse-System Mathematisches Modell Marketing Mathematical models Marketing Statistical methods Management (DE-588)4037278-9 gnd Datenanalyse (DE-588)4123037-1 gnd Statistische Analyse (DE-588)4116599-8 gnd Statistisches Modell (DE-588)4121722-6 gnd Datenerhebung (DE-588)4155272-6 gnd |
| topic_facet | Statistische Methode / Ökonometrisches Modell / Betriebsanalyse / Performance Measurement / Marketing-Informationssystem / Management-Informationssystem / Data-Warehouse-System Mathematisches Modell Marketing Mathematical models Marketing Statistical methods Management Datenanalyse Statistische Analyse Statistisches Modell Datenerhebung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015030929&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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