Multivariate analysis of quality: an introduction
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Chichester [u.a.]
Wiley
2001
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| Schlagworte: | |
| Online-Zugang: | Publisher description Table of Contents Inhaltsverzeichnis |
| Beschreibung: | XX, 445 S. Ill., graph. Darst. |
| ISBN: | 0471974285 |
Internformat
MARC
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| 100 | 1 | |a Martens, Harald |d 1946- |e Verfasser |0 (DE-588)14320789X |4 aut | |
| 245 | 1 | 0 | |a Multivariate analysis of quality |b an introduction |c Harald Martens ; Magni Martens |
| 264 | 1 | |a Chichester [u.a.] |b Wiley |c 2001 | |
| 300 | |a XX, 445 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 7 | |a Analyse multivariée |2 ram | |
| 650 | 7 | |a Kwaliteitscontrole |2 gtt | |
| 650 | 7 | |a Multivariate analyse |2 gtt | |
| 650 | 7 | |a Toepassingen |2 gtt | |
| 650 | 4 | |a Multivariate analysis | |
| 650 | 0 | 7 | |a Multivariate Analyse |0 (DE-588)4040708-1 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Qualitätsmanagement |0 (DE-588)4219057-5 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Multivariate Analyse |0 (DE-588)4040708-1 |D s |
| 689 | 0 | 1 | |a Qualitätsmanagement |0 (DE-588)4219057-5 |D s |
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| 700 | 1 | |a Martens, Magni |d 1948- |e Verfasser |0 (DE-588)1070185299 |4 aut | |
| 856 | 4 | |u http://www.loc.gov/catdir/description/wiley036/00043342.html |3 Publisher description | |
| 856 | 4 | |u http://www.loc.gov/catdir/toc/onix07/00043342.html |3 Table of Contents | |
| 856 | 4 | 2 | |m HBZ Datenaustausch |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009546983&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-009546983 | ||
Datensatz im Suchindex
| _version_ | 1804128798022565888 |
|---|---|
| adam_text | Contents
Preface v
Acknowledgements vii
PART ONE: OVERVIEW (Chapters 1 3) 1
1 Why Multivariate Data Analysis? 3
1.1 Purpose of the Book 3
1.1.1 Multivariate Analysis of a Multivariate World 4
1.1.2 Why this Book? 4
1.1.3 One Approach to Multivariate Data Analysis 5
1.1.4 The Examples 7
1.1.5 Data Analysis is a Cognitive Discipline,
Not a Mathematical Exercise 8
1.1.6 Target Reader Group 9
1.2 Information and Quality 10
1.3 The Role of Multivariate Data Analysis in Research 11
1.3.1 Who Should be the Data Analyst? 12
1.3.2 Mathematical Modelling But Just a Little 12
1.4 A Small Example of Multivariate Data Analysis 13
1.4.1 Causality and Prediction 16
1.4.2 The Selectivity Problem and its Solution 17
1.4.3 Multivariate Data Analysis: Overview Maps 19
1.5 Guidelines for Using the Book 21
1.6 Test Questions 22
1.7 Answers 22
2 Qualimetrics for Determining Quality 25
2.1 A World Under Indirect Observation 25
2.1.1 Looking for the Golden Bits of Information 26
2.1.2 It is Human to Err, Even for Researchers 26
2.2 The Quality of Information 27
2.2.1 Validity 27
x Multivariate Analysis of Quality
2.2.2 Reliability 27
2.2.3 Relevance 29
2.3 A Study of the Quality Concept 30
2.4 Four Definitions of the Word Quality 37
2.4.1 Overview of Various Quality Definitions 37
2.4.2 Relationships Between the Four Definitions 42
2.5 Information About Quality 45
2.5.1 Qualimetrics 45
2.5.2 Communicating Quality Grade and Confidence 46
2.5.3 The Word Quality as Carrier of Information
Between People 47
2.5.4 Cognitive Aspects of Multivariate Data Analysis 47
2.5.5 Quality and Qualimetrics Bridging Technology
and the Humanities 49
2.6 Test Questions 50
2.7 Answers 50
3 A Layman s Guide to Multivariate Data Analysis 51
3.1 Reliable and Relevant Information as a Goal 51
3.1.1 Examples in this book 51
3.2 Qualimetrics as Means 52
3.2.1 From Measurements to Data 52
3.2.2 One Research Project Cycle: Steps I VI 57
3.3 Soft Bi Linear Modelling (BLM) as a Tool 59
3.3.1 Choosing the BLM Method: a Versatile Engine 59
3.3.2 What is X and What is Y? 65
3.4 Some Useful Data Analytical Validation Tools 66
3.5 Some Useful Plots 67
3.5.1 The General Types of Plots 67
3.5.2 Example of BLM Plots: a Small Consumer Study 69
3.6 Getting Started With Multivariate Data Analysis 73
3.7 Test Questions 74
3.8 Answers 74
PART TWO: METHODOLOGY (Chapters 4 11) 77
4 Some Estimation Concepts 79
4.1 Basic Notation And Terminology 79
4.1.1 Some Reserved Letters 79
4.1.2 Matrix Notation 79
4.1.3 Other Symbols 80
4.1.4 The Only Matrix Algebra Really Needed in this Book 81
4.1.5 Names of Samples and Variables 81
4.1.6 A Matrix (Table) of Data 81
Contents xi
4.2 Solving the Selectivity Problem: the LITMUS Data Set 83
4.2.1 Background, LITMUS Data Set 83
4.2.2 The Selectivity Problem: One, Two and Three
Causal Phenomena 85
4.2.3 Three Challenges in the LITMUS Data Set 87
4.2.4 A Law is Not THE Law 88
4.3 Linear Least Squares Regression 89
4.4 Test Questions 92
4.5 Answers 92
5 Analysis of One Data Table X: Principal Component Analysis 93
5.1 What is a Principal Component? 93
5.1.1 A PC Shows the Systematic Pattern of Variation in a
Data Set 93
5.1.2 PCA: Bi linear Modelling of One Single Data
Matrix X 94
5.1.3 The Physical Meaning of a PC 94
5.1.4 A Simple Model For Simple Data: Different
Concentrations of Blue Litmus 94
5.1.5 The Same Bi linear Model With Unknown
Parameters: PCA 96
5.2 How are Principal Components Obtained? 97
5.2.1 The Mean centred Multi component Bi linear
Model of PCA 97
5.2.2 The Full Bi linear Model of X 101
5.3 How PCs Give Meaningful Maps of the Data 101
5.3.1 Too Many Raw Data Plots 101
5.3.2 Compact Overview Plots From PCA 103
5.3.3 Reconstructions From the Score Plot 106
5.4 When and How 107
5.4.1 When to Use PCA 107
5.4.2 How to Use PCA 108
5.5 Test Questions 109
5.6 Answers 109
6 Analysis of Two Data Tables X and Y: Partial Least Squares
Regression (PLSR) 111
6.1 Modelling Y from the Essence of X 111
6.1.1 Seeing the Same Data From Three Different Angles 112
6.2 BLM: From PCA to PLSR 112
6.2.1 Score Plot From PCA and PLSR 114
6.2.2 Loading Plot From PLSR 115
6.3 Smooth Curves are Nice, But Not Necessary 115
xii Multivariate Analysis of Quality
6.4 Calibration and Prediction Models 116
6.4.1 The Calibration Model 116
6.4.2 The Prediction Model 118
6.5 A Mini example: Learning How to Predict Two Unknowns 119
6.5.1 Calibration Phase 119
6.5.2 Prediction Phase 122
6.6 When and How 123
6.6.1 When to Use PLSR 123
6.6.2 How to Use PLSR 124
6.7 Test Questions 124
6.8 Answers 125
7 Example of a Multivariate Calibration Project 127
7.1 Predictive Modelling 127
7.2 Calibration 128
7.2.1 Principal Properties Design 128
7.2.2 Bi linear Regression Modelling by PLSR 130
7.2.3 Inspecting the Bi linear Model 130
7.2.4 Summary of the Calibration Model: Regression
Coefficients b 131
7.3 Prediction 132
7.3.1 Predicted Scores 131
7.3.2 Predicted Y values with Outlier sensitive
Uncertainty Estimate 133
7.3.3 Inspecting the Detected Outliers 133
7.3.4 Updating the Model 136
7.4 Summary of all the LITMUS Data Set Illustrations 136
7.4.1 Increasing complexity 136
7.4.2 So, How was the Selectivity Enhancement in
Figure 4.1 Attained? 137
7.5 Test Questions 137
7.6 Answers 137
8 Interpretation of Many Types of Data X = Y: Exploring
Relationships in Interdisciplinary Data Sets 139
8.1 Sensory and Chemical Data 139
8.1.1 Finding Quality Criteria 139
8.1.2 Approaching Interdisciplinary Data 141
8.2 Analysing Many Types of X and Y variables 142
8.2.1 Preliminary Inspection of the Raw Data 143
8.2.2 Checking A Priori Expectations 144
8.2.3 Standardising the Input Variables to the Same Initial
Variability 145
Contents xiii
8.2.4 One Matrix BLM: PCA as a Preliminary Inspection
Tool 146
8.2.5 Two Matrices BLM: PLS Regression 146
8.2.6 Predicting Chemistry and Physics (Y) From
People s Perception (X) 148
8.2.7 Swapping X and Y 152
8.2.8 Non Linearities 153
8.2.9 Quality Criteria 154
8.3 Test Questions 156
8.4 Answers 156
9 Classification and Discrimination X,, X2, X,...: Handling
Heterogeneous Sample Sets 157
9.1 Heterogeneous Sample Set 157
9.2 SIMCA and DPLSR 157
9.2.1 Initial Analysis: Discovering Classes of Samples 158
9.2.2 Classification by Separate Modelling of Each
Sample Group: SIMCA 163
9.2.3 Classification by DPLSR 169
9.3 Outlier Detection 173
9.3.1 The Importance of Outliers 173
9.3.2 Techniques for Detecting Outliers 174
9.4 Test Questions 175
9.5 Answers 175
10 Validation X? Y?? 177
10.1 The Validity of Information From BLM 177
10.1.1 The Need for Validation Tools 177
10.1.2 Reliability of BLM Results 177
10.1.3 A Visual Approach to Validation 178
10.1.4 How to Summarise the X and Y residuals 179
10.1.5 Cross validation (CV) 179
10.2 Cross validation Principles 180
10.2.1 Enough Calibration Samples to Guard Against
Input Errors? 180
10.2.2 Every Phenomenon to be Modelled, Must be Seen
at Least Twice 183
10.2.3 For Readers Who Want a Short cut 186
10.3 Cross validation, Step by Step 188
10.3.1 A Small Data Set With One y , Many X variables 189
10.4 The Stability of Bi linear Models 195
10.4.1 Model Reliability at a Glance 195
10.4.2 Summarising the Validity Assessment of this Model 199
xiv Multivariate Analysis of Quality
10.5 Different Levels of Validity 199
10.5.1 What Kind of Validity? 199
10.5.2 Including the Replicates 200
10.5.3 Three Different Ways to Segment the Replicated
Samples in the Cross validation 201
10.6 When and How 204
10.6.1 When to Validate 204
10.6.2 How to Validate 204
10.7 Test Questions 205
10.8 Answers 205
11 Experimental Planning Y? X! 207
11.1 Informative Data Needed, But How Can We Get It? 207
11.1.1 Good Data is the Basis for Good Results 207
11.1.2 Experimental Design 207
11.1.3 What is a Sufficiently Informative Data Set? 208
11.2 Different Approaches to Experimental Planning 208
11.2.1 Explorative Design: Stratified Random Sampling 210
11.2.2 Design of Controlled Experiments 211
11.2.3 Principal Properties (PP) Design 212
11.2.4 Good Experimental Practice 213
11.2.5 Design Approaches Used in this Book 213
11.3 Factorial Designs 215
11.3.1 Quality Criteria for Factorial Designs 215
11.3.2 What Can the Factorial Designs Reveal? 215
11.3.3 Some Alternative Factorial Designs for
Controlled Experiments 216
11.4 Which Design is Good Enough? Power Estimation 220
11.4.1 Cost benefit Comparison 220
11.4.2 Planning to Deal with Uncertainty 221
11.4.3 Example of Optimising a Design Prior to the
Experiment 221
11.4.4 Assumptions to be Made Prior to Power Estimation 224
11.4.5 Power Estimation in the COCOA Example 225
11.4.6 Choosing the Best Design from the Power Estimation 227
11.4.7 Results from the Actual Experiment, Based on
the Chosen Design 227
11.5 When and How 229
11.5.1 When to Plan 229
11.5.2 How to Plan 229
11.6 Test Questions 230
11.7 Answers 230
Contents xv
PART THREE: APPLICATIONS (Chapters 12 16) 233
12 Multivariate Calibration: Quality Determination of Wheat
From High speed NIR Spectra 235
12.1 Introduction 235
12.1.1 Purpose of the Project 235
12.1.2 Purpose of the Data Analysis 237
12.2 Calibration 237
12.2.1 Selection of Calibration Samples 238
12.2.2 Selection of Variables 238
12.2.3 Making Data Tables 238
12.2.4 Data Pre treatment 239
12.2.5 Graphical Inspection of Calibration Data 239
12.2.6 Weighting of the Variables 239
12.2.7 Regression Modelling 239
12.2.8 Prediction Error in y 241
12.2.9 Graphical Interpretation of the Bi linear Model 243
12.2.10 Bi linear Modelling Used as a
Hypothesis generating Tool 246
12.2.11 Attempts at Improving the Calibration Modelling 248
12.3 Prediction 249
12.3.1 Checking the Model 251
12.4 Conclusions 253
12.5 Test Questions 254
12.5.1 Questions About the Calibration Phase 254
12.5.2 Answers 252
12.5.3 Questions About the Prediction Phase 255
12.5.4 Answers 255
12.5.5 General Question 255
12.5.6 Answers 256
13 Analysis of Questionnaire Data: What Determines Quality
of the Working Environment? 257
13.1 Introduction 257
13.1.1 Purpose of the Project 257
13.1.2 Purpose of the Data Analysis 258
13.2 Working Environment Studied on Averaged Data 258
13.2.1 Selection of Samples 259
13.2.2 Selection of Variables 259
13.2.3 Making Data Tables 259
13.2.4 Inspection of Input Data 259
13.2.5 Modelling JOBSATISFACTION (y) from
26 Detailed Questions (X) 261
13.2.6 The Bi linear Model of the 34 Departments 261
xvi Multivariate Analysis of Quality
13.2.7 Prediction of Job Satisfaction 264
13.2.8 Possible Model Optimisation Steps 266
13.2.9 Tracing the Conclusions Back to the Raw Data 267
13.3 Working Environment Studied on Individuals Data 268
13.3.1 Making the Data Table 268
13.3.2 Data Analysis of all the Individuals 268
13.3.3 Segmentation of People into Groups 269
13.4 Conclusions 272
13.5 Test Questions 272
13.6 Answers 273
14 Analysis of a Heterogeneous Sample Set: Predicting Toxicity
From Quantum Chemistry 275
14.1 Introduction 275
14.1.1 Purpose of the Project 275
14.1.2 Purpose of the Data Analysis 276
14.2 Calibration and Prediction of the Whole Data Set 277
14.2.1 Selection of Calibration Samples 277
14.2.2 Selection of Variables 277
14.2.3 Molecular Descriptors 279
14.2.4 Toxicity 279
14.2.5 Transforming the Input Variables into Comparable
Units 280
14.2.6 Making Data Tables 281
14.2.7 Graphical Inspection of Input Data 281
14.2.8 Weighting of the Variables 281
14.2.9 Modelling With Only Linear Descriptors in X 283
14.2.10 Modelling With Linear and Squared Descriptors in X 283
14.2.11 Removal of a Gross Outlier Sample 284
14.2.12 Removal of Useless or Detrimental X variables 284
14.2.13 The Final Joint Calibration Model 286
14.2.14 Prediction of y From X With Different Cross validation
Schemes 286
14.2.15 Comparing the Originally Published Calibration
and Test Sets 289
14.2.16 Too Much Heterogeneity for Joint Modelling? 289
14.3 Three Classes of Samples Modelled Separately 289
14.3.1 Class 1: Nitro aromatic Compounds 291
14.3.2 Class 2: Poly cyclic Aromatic Hydrocarbon
Compounds 291
14.3.3 Class 3: Aromatic Amines 294
14.4 Conclusions 294
14.5 Test Questions 295
14.6 Answers 295
Contents xvii
15 Multivariate Statistical Process Control: Quality Monitoring
of a Sugar Production Process 297
15.1 Introduction 297
15.1.1 Purpose of the Project 297
15.1.2 Purpose of the Data Analysis 299
15.2 Calibration Model for the First Time Period 300
15.2.1 Selection of Calibration Samples 300
15.2.2 Selection of Variables 300
15.2.3 Process Analysis 300
15.2.4 Product Quality Analysis 301
15.2.5 Making Data Tables 301
15.2.6 Initial Data Reduction 301
15.2.7 Graphical Inspection of Calibration Input Data 301
15.2.8 Weighting of the Variables 302
15.2.9 Calibration Modelling of y vs. X 304
15.2.10 Main Model Overviews 304
15.2.11 One way Loading Plots 305
15.2.12 The Prediction Model 307
15.3 MSPC and Updating 307
15.3.1 Predicted y, With Outlier sensitive Uncertainty Estimate 310
15.3.2 Scores as Time Series 311
15.3.3 Leverage and X residual: Two Complementary
Summaries of X 312
15.3.4 Checking the Process Data 313
15.3.5 Updating the Calibration Model 315
15.4 Conclusions 318
15.4.1 Rhythm and Blues in Research 319
15.5 Test Questions 319
15.6 Answers 320
16 Design and Analysis of Controlled Experiments: Reducing
Loss of Quality in Stored Food 323
16.1 Introduction 323
16.1.1 Purpose of the Project 323
16.1.2 Purpose of the Data Analysis 324
16.2 Screening Experiment 324
16.2.1 Selection of Variables 324
16.2.2 Selection of Samples 325
16.2.3 Model and Estimation Method to be Used 329
16.2.4 How Many Replicates Needed? 329
16.2.5 Preliminary Inspection of Input Data 333
16.2.6 Modelling the Response Data 333
16.2.7 Elimination of Useless Variables 336
16.2.8 Checking the Model Interpretation in the Raw Data 337
xviii Multivariate Analysis of Quality
16.3 Main Experiment: Response Surface Analysis 340
16.3.1 Selection of Variables 340
16.3.2 Selection of Samples 341
16.3.3 Model and Estimation Method to be Used 343
16.3.4 Does the Extended Design Have the Necessary and
Sufficient Power? 343
16.3.5 Preliminary Inspection of Input Data 344
16.3.6 Modelling the Response Data 345
16.3.7 Comparison to the Previous Screening Experiment 349
16.3.8 Checking the Model Interpretation in the Raw Data 349
16.4 Conclusions 352
16.5 Test Questions 352
16.6 Answers 353
PART FOUR: APPENDICES (A1 A16) 355
Appendix Al How the Present Book Relates to Some
Mathematical Modelling Traditions in Science 357
Al.l Causal Modelling 358
A1.2 Classical Statistics 358
A 1.3 Dynamic Process Modelling 360
A 1.4 Modern Informatics Modelling 360
A 1.5 Econometrics Path Modelling 360
A1.6 Psychometrics 361
A 1.7 Chemometrics 361
A1.8 Sensometrics 361
Appendix A2 Sensory Science 362
Appendix A3.1 Bi linear Modelling Has Many Applications 364
Appendix A3.2 Common Problems and Pitfalls in Soft Modelling 366
A3.2.1 Experimental Design 366
A3.2.2 Scaling of Input Variables 367
A3.2.3 Outlier Objects 367
A3.2.4 Heterogeneous Sample Sets 368
A3.2.5 Problems in the Internal Data Analytical
Validation 368
A3.2.6 Problems in the External, Interpretational
Validation 368
A3.2.7 Missing Values 369
Contents xix
Appendix A4 Mathematical Details 370
A4.1 Some More Vector Algebra 370
A4.2 Some Useful Statistical Expressions 371
A4.3 Other Statistical Summaries: Median, Quartiles,
Percentiles 373
A4.4 More on Linear Least Squares Regression 373
A4.5 Effect Correction 374
A4.6 Three Different Regression Methods 374
Appendix A5 PCA Details 377
A5.1 Two Ways to do Univariate Regression 377
A5.2 The NIPALS Algorithm for PCA 377
A5.3 Equivalent PCA Representations 378
A5.4 Scaling and Rotation of Bi linear Models 379
A5.5 Correlation Loadings for X 380
A5.6 Missing Values 381
Appendix A6 PLS Regression Details 382
A6.1 BLMbyPLSR 382
A6.2 Loading Weights: wa 382
A6.3 Scores: ta 383
A6.4 Y loadings: qa 383
A6.5 X loadings: pa 383
A6.6 Y residuals: Fa 383
A6.7 X residuals: Ea 383
A6.8 Comments on the PLSR Algorithm 384
A6.9 Estimates of the Regression Coefficients BA and
offset bo,A 384
A6.10 PLSR Draws on Two Sources of Structure 385
A6.11 Correlation Loadings for Y 385
A6.12 Loading Weights vs. Loadings 385
A6.13 Disagreement Between X and Y 386
A6.14 Special Versions of PLSR 386
Appendix A7 Modelling the Unknown 388
Appendix A8 Non linearity and Weighting 389
A8.1 Non linear Relationships Handled by Polynomial
BLM 389
A8.2 A Priori Weighting: the Use of Prior Knowledge
to Scale the Input Variables for BLM 390
A8.3 Graphical Consequences of the Weighting 391
xx Multivariate Analysis of Quality
A8.4 Bi linear Modelling by Ordinary Least Squares
(OLS), Weighted Least Squares (WLS) and
Generalised Least Squares (GLS) 392
Appendix A9 Classification and Outlier Detection 394
A9.1 SIMCA Classification 394
A9.2 Discriminant PLS Regression (DPLSR) 394
A9.3 Outlier Analysis 395
A9.4 Statistics for One Sample 396
A9.5 Statistics for One Model (Class) 397
A9.6 Statistics for One Variable 397
Appendix A10 Cross validation Details 399
A 10.1 Different Ways to Define Cross validation Segments 399
A 10.2 Passive Variables 401
A 10.3 Cross validation with Jack knifing in BLM 402
Appendix All Power Estimation Details 410
Al 1.1 Monte Carlo Assessment of the Power of an
Experimental Design 410
Appendix A12 What Makes NIR Data So Information rich? 415
Appendix A13 Consequences of the Working Environment Survey 418
Appendix A14 Details of the Molecule Class Models 419
Appendix A15 Forecasting the Future 420
Appendix A16 Significance Testing with Cross validation
vs. ANOVA 421
References 427
Index 435
|
| any_adam_object | 1 |
| author | Martens, Harald 1946- Martens, Magni 1948- |
| author_GND | (DE-588)14320789X (DE-588)1070185299 |
| author_facet | Martens, Harald 1946- Martens, Magni 1948- |
| author_role | aut aut |
| author_sort | Martens, Harald 1946- |
| author_variant | h m hm m m mm |
| building | Verbundindex |
| bvnumber | BV013950737 |
| callnumber-first | Q - Science |
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| callnumber-raw | QA278.M373 2001 |
| callnumber-search | QA278.M373 2001 |
| callnumber-sort | QA 3278 M373 42001 |
| callnumber-subject | QA - Mathematics |
| classification_rvk | QH 234 SK 830 VC 6050 |
| classification_tum | MAT 627f |
| ctrlnum | (OCoLC)44425421 (DE-599)BVBBV013950737 |
| dewey-full | 519.5/3521 519.5/35 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/35 21 519.5/35 |
| dewey-search | 519.5/35 21 519.5/35 |
| dewey-sort | 3519.5 235 221 |
| dewey-tens | 510 - Mathematics |
| discipline | Chemie / Pharmazie Mathematik Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV013950737 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T18:54:58Z |
| institution | BVB |
| isbn | 0471974285 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-009546983 |
| oclc_num | 44425421 |
| open_access_boolean | |
| owner | DE-20 DE-703 DE-355 DE-BY-UBR DE-521 DE-634 DE-83 DE-11 DE-91 DE-BY-TUM |
| owner_facet | DE-20 DE-703 DE-355 DE-BY-UBR DE-521 DE-634 DE-83 DE-11 DE-91 DE-BY-TUM |
| physical | XX, 445 S. Ill., graph. Darst. |
| publishDate | 2001 |
| publishDateSearch | 2001 |
| publishDateSort | 2001 |
| publisher | Wiley |
| record_format | marc |
| spelling | Martens, Harald 1946- Verfasser (DE-588)14320789X aut Multivariate analysis of quality an introduction Harald Martens ; Magni Martens Chichester [u.a.] Wiley 2001 XX, 445 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Analyse multivariée ram Kwaliteitscontrole gtt Multivariate analyse gtt Toepassingen gtt Multivariate analysis Multivariate Analyse (DE-588)4040708-1 gnd rswk-swf Qualitätsmanagement (DE-588)4219057-5 gnd rswk-swf Multivariate Analyse (DE-588)4040708-1 s Qualitätsmanagement (DE-588)4219057-5 s DE-604 Martens, Magni 1948- Verfasser (DE-588)1070185299 aut http://www.loc.gov/catdir/description/wiley036/00043342.html Publisher description http://www.loc.gov/catdir/toc/onix07/00043342.html Table of Contents HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009546983&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Martens, Harald 1946- Martens, Magni 1948- Multivariate analysis of quality an introduction Analyse multivariée ram Kwaliteitscontrole gtt Multivariate analyse gtt Toepassingen gtt Multivariate analysis Multivariate Analyse (DE-588)4040708-1 gnd Qualitätsmanagement (DE-588)4219057-5 gnd |
| subject_GND | (DE-588)4040708-1 (DE-588)4219057-5 |
| title | Multivariate analysis of quality an introduction |
| title_auth | Multivariate analysis of quality an introduction |
| title_exact_search | Multivariate analysis of quality an introduction |
| title_full | Multivariate analysis of quality an introduction Harald Martens ; Magni Martens |
| title_fullStr | Multivariate analysis of quality an introduction Harald Martens ; Magni Martens |
| title_full_unstemmed | Multivariate analysis of quality an introduction Harald Martens ; Magni Martens |
| title_short | Multivariate analysis of quality |
| title_sort | multivariate analysis of quality an introduction |
| title_sub | an introduction |
| topic | Analyse multivariée ram Kwaliteitscontrole gtt Multivariate analyse gtt Toepassingen gtt Multivariate analysis Multivariate Analyse (DE-588)4040708-1 gnd Qualitätsmanagement (DE-588)4219057-5 gnd |
| topic_facet | Analyse multivariée Kwaliteitscontrole Multivariate analyse Toepassingen Multivariate analysis Multivariate Analyse Qualitätsmanagement |
| url | http://www.loc.gov/catdir/description/wiley036/00043342.html http://www.loc.gov/catdir/toc/onix07/00043342.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009546983&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT martensharald multivariateanalysisofqualityanintroduction AT martensmagni multivariateanalysisofqualityanintroduction |