Industrial Statistics with Minitab
John Wiley & Sons Inc (Verlag)
978-0-470-97275-5 (ISBN)
Industrial Statistics with MINITAB:
Provides comprehensive coverage of user-friendly practical guidance to the essential statistical methods applied in industry.
Explores statistical techniques and how they can be used effectively with the help of MINITAB 16.
Contains extensive illustrative examples and case studies throughout and assumes no previous statistical knowledge.
Emphasises data graphics and visualization, and the most used industrial statistical tools, such as Statistical Process Control and Design of Experiments.
Is supported by an accompanying website featuring case studies and the corresponding datasets.
Six Sigma Green Belts and Black Belts will find explanations and examples of the most relevant techniques in DMAIC projects. The book can also be used as quick reference enabling the reader to be confident enough to explore other MINITAB capabilities.
Pere Grima, Professor, Department of the Technical University of Catalonia UPC, Barcelona, Spain. Lluis Marco, Assistant Professor, Department of the Technical University of Catalonia UPC, Barcelona, Spain. Xavier Tort-Martorell, Department Director Statistics Department,the Technical University of Catalonia UPC, Barcelona, Spain. Xavier Tort-Martorell is president elect of ENBIS The authors possess a wide experience both in training and consulting. They have designed and delivered courses in various universities and at all levels, from undergraduate to postgraduate and professional training. Their activities have always been strongly linked to the business and industrial world. Most recently with a high focus on Six Sigma training and consulting they have certified more than 450 Black Belts in Spain and Latin America- . They have also advised many companies from different sectors on the implementation of quality improvement programs and the proper use of statistical methods. Among others: Hewlett-Packard, Samsung electronics, Alstom Transport, Siemens VDO, BBVA, Procter & Gamble and ITP.
Preface xiii
Part One Introduction and Graphical Techniques 1
1 A First Look 3
1.1 Initial Screen 3
1.2 Entering Data 4
1.3 Saving Data: Worksheets and Projects 5
1.4 Data Operations: An Introduction 5
1.5 Deleting and Inserting Columns and Rows 7
1.6 First Statistical Analyses 8
1.7 Getting Help 10
1.8 Personal Configuration 12
1.9 Assistant 13
1.10 Any Difficulties? 14
2 Graphics for Univariate Data 15
2.1 File ‘PULSE’ 15
2.2 Histograms 16
2.3 Changing the Appearance of Histograms 17
2.4 Histograms for Various Data Sets 21
2.5 Dotplots 23
2.6 Boxplots 24
2.7 Bar Diagrams 25
2.8 Pie Charts 27
2.9 Updating Graphs Automatically 28
2.10 Adding Text or Figures to a Graph 29
3 Pareto Charts and Cause–Effect Diagrams 31
3.1 File ‘DETERGENT’ 31
3.2 Pareto Charts 32
3.4 Cause-and-Effect Diagrams 35
4 Scatterplots 37
4.1 File ‘pulse’ 37
4.2 Stratification 38
4.3 Identifying Points on a Graph 39
4.4 Using the ‘Crosshairs’ Option 45
4.5 Scatterplots with Panels 46
4.6 Scatterplots with Marginal Graphs 48
4.7 Creating an Array of Scatterplots 50
5 Three Dimensional Plots 52
5.1 3D Scatterplots 52
5.2 3D Surface Plots 55
5.3 Contour Plots 58
6 Part One: Case Studies – Introduction and Graphical Techniques 62
6.1 Cork 62
6.2 Copper 68
6.3 Bread 73
6.4 Humidity 76
Part Two Hypothesis Testing. Comparison of Treatments 79
7 Random Numbers and Numbers Following a Pattern 81
7.1 Introducing Values Following a Pattern 81
7.2 Sampling Random Data from a Column 83
7.3 Random Number Generation 83
7.4 Example: Solving a Problem Using Random Numbers 85
8 Computing Probabilities 87
8.1 Probability Distributions 87
8.2 Option ‘Probability Density’ or ‘Probability’ 88
8.3 Option ‘Cumulative Probability’ 89
8.4 Option ‘Inverse Cumulative Probability’ 89
8.5 Viewing the Shape of the Distributions 92
8.6 Equivalence between Sigmas of the Process and Defects per Million Parts Using ‘Cumulative Probability’ 92
9 Hypothesis Testing for Means and Proportions. Normality Test 95
9.1 Hypothesis Testing for One Mean 95
9.2 Hypothesis Testing and Confidence Interval for a Proportion 99
9.3 Normality Test 100
10 Comparison of Two Means, Two Variances or Two Proportions 103
10.1 Comparison of Two Means 103
10.2 Comparison of Two Variances 107
10.3 Comparison of Two Proportions 109
11 Comparison of More than Two Means: Analysis of Variance 110
11.1 ANOVA (Analysis of Variance) 110
11.2 ANOVA with a Single Factor 110
11.3 ANOVA with Two Factors 114
11.4 Test for Homogeneity of Variances 119
12 Part Two: Case Studies – Hypothesis Testing. Comparison of Treatments 120
12.1 Welding 120
12.2 Rivets 124
12.3 Almonds 126
12.4 Arrow 127
12.5 U Piece 131
12.6 Pores 133
Part Three Measurement Systems Studies and Capability Studies 137
13 Measurement System Study 139
13.1 Crossed Designs and Nested Designs 139
13.2 File ‘RR_CROSSED’ 140
13.3 Graphical Analysis 140
13.4 R&R Study for the Data in File ‘RR_CROSSED’ 141
13.5 File ‘RR_NESTED’ 147
13.6 Gage R&R Study for the Data in File ‘RR_NESTED’ 147
13.7 File ‘GAGELIN’ 148
13.8 Calibration and Linearity Study of the Measurement System 148
14 Capability Studies 151
14.1 Capability Analysis: Available Options 151
14.2 File ‘VITA_C’ 152
14.3 Capability Analysis (Normal Distribution) 152
14.4 Interpreting the Obtained Information 152
14.5 Customizing the Study 154
14.6 ‘Within’ Variability and ‘Overall’ Variability 155
14.7 Capability Study when the Sample Size is Equal to One 158
14.8 A More Detailed Data Analysis (Capability Sixpack) 161
15 Capability Studies for Attributes 163
15.1 File ‘BANK’ 163
15.2 Capability Study for Variables that Follow a Binomial Distribution 163
15.3 File ‘OVEN_PAINTED’ 166
15.4 Capability Study for Variables that Follow a Poisson Distribution 166
16 Part Three: Case Studies – R&R Studies and Capability Studies 168
16.1 Diameter_measure 168
16.2 Diameter_capability_1 173
16.3 Diameter_capability_2 174
16.4 Web_visits 176
Part Four Multi-Vari Charts and Statistical Process Control 181
17 Multi-Vari Charts 183
17.1 File ‘MUFFIN’ 183
17.2 Multi-Vari Chart with Three Sources of Variation 184
17.3 Multi-Vari Chart with Four Sources of Variation 186
18 Control Charts I: Individual Observations 188
18.1 File ‘CHLORINE’ 188
18.2 Graph of Individual Observations 188
18.3 Customizing the Graph 191
18.4 I Chart Options 192
18.5 Graphs of Moving Ranges 196
18.6 Graph of Individual Observations – Moving Ranges 197
19 Control Charts II: Means and Ranges 198
19.1 File ‘VITA_C’ 198
19.2 Means Chart 199
19.3 Graphs of Ranges and Standard Deviations 200
19.4 Graphs of Means-Ranges 201
19.5 Some Ideas on How to Use Minitab as a Simulator of Processes for Didactic Reasons 201
20 Control Charts for Attributes 204
20.1 File ‘MOTORS’ 204
20.2 Plotting the Proportion of Defective Units (P) 204
20.3 File ‘CATHETER’ 205
20.4 Plotting the Number of Defective Units (NP) 206
20.5 Plotting the Number of Defects per Constant Unit of Measurement (C) 208
20.6 File ‘FABRIC’ 210
20.7 Plotting the Number of Defects per Variable Unit of Measurement (U) 210
21 Part Four: Case Studies – Multi-Vari Charts and Statistical Process Control 212
21.1 Bottles 212
21.2 Mattresses (1st Part) 217
21.3 Mattresses (2nd Part) 221
21.4 Plastic (1st Part) 223
21.5 Plastic (2nd Part) 224
Part Five Regression and Multivariate Analysis 231
22 Correlation and Simple Regression 235
22.1 Correlation Coefficient 235
22.2 Simple Regression 238
22.3 Simple Regression with ‘Fitted Line Plot’ 239
22.4 Simple Regression with ‘Regression’ 244
23 Multiple Regression 247
23.1 File ‘CARS2’ 247
23.2 Exploratory Analysis 247
23.3 Multiple Regression 249
23.4 Option Buttons 250
23.5 Selection of the Best Equation: Best Subsets 252
23.6 Selection of the Best Equation: Stepwise 254
24 Multivariate Analysis 256
24.1 File ‘LATIN_AMERICA’ 256
24.2 Principal Components 257
24.3 Cluster Analysis for Observations 263
24.4 Cluster Analysis for Variables 266
24.5 Discriminant Analysis 267
25 Part Five: Case Studies – Regression and Multivariate Analysis 272
25.1 Tree 272
25.2 Power Plant 278
25.3 Wear 285
25.4 TV Failure 290
Part Six Experimental Design and Reliability 293
26 Factorial Designs: Creation 295
26.1 Creation of the Design Matrix 295
26.2 Design Matrix with Data Already in the Worksheet 301
27 Factorial Designs: Analysis 303
27.1 Calculating the Effects and Determining the Significant Ones 303
27.2 Interpretation of Results 308
27.3 A Recap with a Fractional Factorial Design 310
28 Response Surface Methodology 313
28.1 Matrix Design Creation and Data Collection 313
28.2 Analysis of the Results 317
28.3 Contour Plots and Response Surface Plots 322
29 Reliability 325
29.1 File 325
29.2 Nonparametric Analysis 326
29.3 Identification of the Best Model for the Data 329
29.4 Parametric Analysis 330
29.5 General Graphical Display of Reliability Data 333
30 Part Six: Case Studies – Design of Experiments and Reliability 335
30.1 Cardigan 335
30.2 Steering wheel – 1 340
30.3 Steering Wheel – 2 343
30.4 Paper Helicopters 345
30.5 Microorganisms 349
30.6 Jam 359
30.7 Photocopies 365
Appendices 371
A1 Appendix 1: Answers to Questions that Arise at the Beginning 373
A2 Appendix 2: Managing Data 377
A2.1 Copy Columns with Restrictions (File: ‘PULSE’) 377
A2.2 Selection of Data when Plotting a Graph 381
A2.3 Stacking and Unstacking of Columns (File ‘BREAD’) 382
A2.4 Coding and Sorting Data 386
A3 Appendix 3: Customization of Minitab 390
A3.1 Configuration Options 390
A3.2 Use of Toolbars 392
A3.3 Add Elements to an Existing Toolbar 392
A3.4 Create Custom Toolbars 393
Index 397
| Verlagsort | New York |
|---|---|
| Sprache | englisch |
| Maße | 158 x 236 mm |
| Gewicht | 653 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Computerprogramme / Computeralgebra |
| Mathematik / Informatik ► Mathematik ► Finanz- / Wirtschaftsmathematik | |
| Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik | |
| Technik ► Elektrotechnik / Energietechnik | |
| ISBN-10 | 0-470-97275-0 / 0470972750 |
| ISBN-13 | 978-0-470-97275-5 / 9780470972755 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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