{"product_id":"industrial-statistics-with-minitab-isbn-9780470972755","title":"Industrial Statistics with Minitab","description":"\u003cp\u003e\u003ci\u003eIndustrial Statistics with MINITAB\u003c\/i\u003e demonstrates the use of MINITAB as a tool for performing statistical analysis in an industrial context. This book covers introductory industrial statistics, exploring the most commonly used techniques alongside those that serve to give an overview of more complex issues. A plethora of examples in MINITAB are featured along with case studies for each of the statistical techniques presented.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eIndustrial Statistics with MINITAB\u003c\/i\u003e:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eProvides comprehensive coverage of user-friendly practical guidance to the essential statistical methods applied in industry.\u003c\/li\u003e \u003cli\u003eExplores statistical techniques and how they can be used effectively with the help of MINITAB 16.\u003c\/li\u003e \u003cli\u003eContains extensive illustrative examples and case studies throughout and assumes no previous statistical knowledge.\u003c\/li\u003e \u003cli\u003eEmphasises data graphics and visualization, and the most used industrial statistical tools, such as Statistical Process Control and Design of Experiments. \u003c\/li\u003e \u003cli\u003eIs supported by an accompanying website featuring case studies and the corresponding datasets.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eSix 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.\u003c\/p\u003e \u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart One Introduction and Graphical Techniques 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 A First Look 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Initial Screen 3\u003c\/p\u003e \u003cp\u003e1.2 Entering Data 4\u003c\/p\u003e \u003cp\u003e1.3 Saving Data: Worksheets and Projects 5\u003c\/p\u003e \u003cp\u003e1.4 Data Operations: An Introduction 5\u003c\/p\u003e \u003cp\u003e1.5 Deleting and Inserting Columns and Rows 7\u003c\/p\u003e \u003cp\u003e1.6 First Statistical Analyses 8\u003c\/p\u003e \u003cp\u003e1.7 Getting Help 10\u003c\/p\u003e \u003cp\u003e1.8 Personal Configuration 12\u003c\/p\u003e \u003cp\u003e1.9 Assistant 13\u003c\/p\u003e \u003cp\u003e1.10 Any Difficulties? 14\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Graphics for Univariate Data 15\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 File ‘PULSE’ 15\u003c\/p\u003e \u003cp\u003e2.2 Histograms 16\u003c\/p\u003e \u003cp\u003e2.3 Changing the Appearance of Histograms 17\u003c\/p\u003e \u003cp\u003e2.4 Histograms for Various Data Sets 21\u003c\/p\u003e \u003cp\u003e2.5 Dotplots 23\u003c\/p\u003e \u003cp\u003e2.6 Boxplots 24\u003c\/p\u003e \u003cp\u003e2.7 Bar Diagrams 25\u003c\/p\u003e \u003cp\u003e2.8 Pie Charts 27\u003c\/p\u003e \u003cp\u003e2.9 Updating Graphs Automatically 28\u003c\/p\u003e \u003cp\u003e2.10 Adding Text or Figures to a Graph 29\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Pareto Charts and Cause–Effect Diagrams 31\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 File ‘DETERGENT’ 31\u003c\/p\u003e \u003cp\u003e3.2 Pareto Charts 32\u003c\/p\u003e \u003cp\u003e3.4 Cause-and-Effect Diagrams 35\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Scatterplots 37\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 File ‘pulse’ 37\u003c\/p\u003e \u003cp\u003e4.2 Stratification 38\u003c\/p\u003e \u003cp\u003e4.3 Identifying Points on a Graph 39\u003c\/p\u003e \u003cp\u003e4.4 Using the ‘Crosshairs’ Option 45\u003c\/p\u003e \u003cp\u003e4.5 Scatterplots with Panels 46\u003c\/p\u003e \u003cp\u003e4.6 Scatterplots with Marginal Graphs 48\u003c\/p\u003e \u003cp\u003e4.7 Creating an Array of Scatterplots 50\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Three Dimensional Plots 52\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 3D Scatterplots 52\u003c\/p\u003e \u003cp\u003e5.2 3D Surface Plots 55\u003c\/p\u003e \u003cp\u003e5.3 Contour Plots 58\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Part One: Case Studies – Introduction and Graphical Techniques 62\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Cork 62\u003c\/p\u003e \u003cp\u003e6.2 Copper 68\u003c\/p\u003e \u003cp\u003e6.3 Bread 73\u003c\/p\u003e \u003cp\u003e6.4 Humidity 76\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Two Hypothesis Testing. Comparison of Treatments 79\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Random Numbers and Numbers Following a Pattern 81\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introducing Values Following a Pattern 81\u003c\/p\u003e \u003cp\u003e7.2 Sampling Random Data from a Column 83\u003c\/p\u003e \u003cp\u003e7.3 Random Number Generation 83\u003c\/p\u003e \u003cp\u003e7.4 Example: Solving a Problem Using Random Numbers 85\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Computing Probabilities 87\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Probability Distributions 87\u003c\/p\u003e \u003cp\u003e8.2 Option ‘Probability Density’ or ‘Probability’ 88\u003c\/p\u003e \u003cp\u003e8.3 Option ‘Cumulative Probability’ 89\u003c\/p\u003e \u003cp\u003e8.4 Option ‘Inverse Cumulative Probability’ 89\u003c\/p\u003e \u003cp\u003e8.5 Viewing the Shape of the Distributions 92\u003c\/p\u003e \u003cp\u003e8.6 Equivalence between Sigmas of the Process and Defects per Million Parts Using \u003ci\u003e‘Cumulative Probability’ \u003c\/i\u003e92\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Hypothesis Testing for Means and Proportions. Normality Test 95\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Hypothesis Testing for One Mean 95\u003c\/p\u003e \u003cp\u003e9.2 Hypothesis Testing and Confidence Interval for a Proportion 99\u003c\/p\u003e \u003cp\u003e9.3 Normality Test 100\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Comparison of Two Means, Two Variances or Two Proportions 103\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Comparison of Two Means 103\u003c\/p\u003e \u003cp\u003e10.2 Comparison of Two Variances 107\u003c\/p\u003e \u003cp\u003e10.3 Comparison of Two Proportions 109\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Comparison of More than Two Means: Analysis of Variance 110\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 ANOVA (Analysis of Variance) 110\u003c\/p\u003e \u003cp\u003e11.2 ANOVA with a Single Factor 110\u003c\/p\u003e \u003cp\u003e11.3 ANOVA with Two Factors 114\u003c\/p\u003e \u003cp\u003e11.4 Test for Homogeneity of Variances 119\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Part Two: Case Studies – Hypothesis Testing. Comparison of Treatments 120\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Welding 120\u003c\/p\u003e \u003cp\u003e12.2 Rivets 124\u003c\/p\u003e \u003cp\u003e12.3 Almonds 126\u003c\/p\u003e \u003cp\u003e12.4 Arrow 127\u003c\/p\u003e \u003cp\u003e12.5 U Piece 131\u003c\/p\u003e \u003cp\u003e12.6 Pores 133\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Three Measurement Systems Studies and Capability Studies 137\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Measurement System Study 139\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Crossed Designs and Nested Designs 139\u003c\/p\u003e \u003cp\u003e13.2 File ‘RR_CROSSED’ 140\u003c\/p\u003e \u003cp\u003e13.3 Graphical Analysis 140\u003c\/p\u003e \u003cp\u003e13.4 R\u0026amp;R Study for the Data in File ‘RR_CROSSED’ 141\u003c\/p\u003e \u003cp\u003e13.5 File ‘RR_NESTED’ 147\u003c\/p\u003e \u003cp\u003e13.6 Gage R\u0026amp;R Study for the Data in File ‘RR_NESTED’ 147\u003c\/p\u003e \u003cp\u003e13.7 File ‘GAGELIN’ 148\u003c\/p\u003e \u003cp\u003e13.8 Calibration and Linearity Study of the Measurement System 148\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Capability Studies 151\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Capability Analysis: Available Options 151\u003c\/p\u003e \u003cp\u003e14.2 File ‘VITA_C’ 152\u003c\/p\u003e \u003cp\u003e14.3 Capability Analysis (Normal Distribution) 152\u003c\/p\u003e \u003cp\u003e14.4 Interpreting the Obtained Information 152\u003c\/p\u003e \u003cp\u003e14.5 Customizing the Study 154\u003c\/p\u003e \u003cp\u003e14.6 ‘Within’ Variability and ‘Overall’ Variability 155\u003c\/p\u003e \u003cp\u003e14.7 Capability Study when the Sample Size is Equal to One 158\u003c\/p\u003e \u003cp\u003e14.8 A More Detailed Data Analysis (Capability Sixpack) 161\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Capability Studies for Attributes 163\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 File ‘BANK’ 163\u003c\/p\u003e \u003cp\u003e15.2 Capability Study for Variables that Follow a Binomial Distribution 163\u003c\/p\u003e \u003cp\u003e15.3 File ‘OVEN_PAINTED’ 166\u003c\/p\u003e \u003cp\u003e15.4 Capability Study for Variables that Follow a Poisson Distribution 166\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Part Three: Case Studies – R\u0026amp;R Studies and Capability Studies 168\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Diameter_measure 168\u003c\/p\u003e \u003cp\u003e16.2 Diameter_capability_1 173\u003c\/p\u003e \u003cp\u003e16.3 Diameter_capability_2 174\u003c\/p\u003e \u003cp\u003e16.4 Web_visits 176\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Four Multi-Vari Charts and Statistical Process Control 181\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Multi-Vari Charts 183\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 File ‘MUFFIN’ 183\u003c\/p\u003e \u003cp\u003e17.2 Multi-Vari Chart with Three Sources of Variation 184\u003c\/p\u003e \u003cp\u003e17.3 Multi-Vari Chart with Four Sources of Variation 186\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Control Charts I: Individual Observations 188\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 File ‘CHLORINE’ 188\u003c\/p\u003e \u003cp\u003e18.2 Graph of Individual Observations 188\u003c\/p\u003e \u003cp\u003e18.3 Customizing the Graph 191\u003c\/p\u003e \u003cp\u003e18.4 I Chart Options 192\u003c\/p\u003e \u003cp\u003e18.5 Graphs of Moving Ranges 196\u003c\/p\u003e \u003cp\u003e18.6 Graph of Individual Observations – Moving Ranges 197\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Control Charts II: Means and Ranges 198\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 File ‘VITA_C’ 198\u003c\/p\u003e \u003cp\u003e19.2 Means Chart 199\u003c\/p\u003e \u003cp\u003e19.3 Graphs of Ranges and Standard Deviations 200\u003c\/p\u003e \u003cp\u003e19.4 Graphs of Means-Ranges 201\u003c\/p\u003e \u003cp\u003e19.5 Some Ideas on How to Use Minitab as a Simulator of Processes for Didactic Reasons 201\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Control Charts for Attributes 204\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 File ‘MOTORS’ 204\u003c\/p\u003e \u003cp\u003e20.2 Plotting the Proportion of Defective Units (P) 204\u003c\/p\u003e \u003cp\u003e20.3 File ‘CATHETER’ 205\u003c\/p\u003e \u003cp\u003e20.4 Plotting the Number of Defective Units (NP) 206\u003c\/p\u003e \u003cp\u003e20.5 Plotting the Number of Defects per Constant Unit of Measurement (C) 208\u003c\/p\u003e \u003cp\u003e20.6 File ‘FABRIC’ 210\u003c\/p\u003e \u003cp\u003e20.7 Plotting the Number of Defects per Variable Unit of Measurement (U) 210\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Part Four: Case Studies – Multi-Vari Charts and Statistical Process Control 212\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e21.1 Bottles 212\u003c\/p\u003e \u003cp\u003e21.2 Mattresses (1st Part) 217\u003c\/p\u003e \u003cp\u003e21.3 Mattresses (2nd Part) 221\u003c\/p\u003e \u003cp\u003e21.4 Plastic (1st Part) 223\u003c\/p\u003e \u003cp\u003e21.5 Plastic (2nd Part) 224\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Five Regression and Multivariate Analysis 231\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Correlation and Simple Regression 235\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e22.1 Correlation Coefficient 235\u003c\/p\u003e \u003cp\u003e22.2 Simple Regression 238\u003c\/p\u003e \u003cp\u003e22.3 Simple Regression with ‘Fitted Line Plot’ 239\u003c\/p\u003e \u003cp\u003e22.4 Simple Regression with ‘Regression’ 244\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 Multiple Regression 247\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e23.1 File ‘CARS2’ 247\u003c\/p\u003e \u003cp\u003e23.2 Exploratory Analysis 247\u003c\/p\u003e \u003cp\u003e23.3 Multiple Regression 249\u003c\/p\u003e \u003cp\u003e23.4 Option Buttons 250\u003c\/p\u003e \u003cp\u003e23.5 Selection of the Best Equation: Best Subsets 252\u003c\/p\u003e \u003cp\u003e23.6 Selection of the Best Equation: Stepwise 254\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24 Multivariate Analysis 256\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e24.1 File ‘LATIN_AMERICA’ 256\u003c\/p\u003e \u003cp\u003e24.2 Principal Components 257\u003c\/p\u003e \u003cp\u003e24.3 Cluster Analysis for Observations 263\u003c\/p\u003e \u003cp\u003e24.4 Cluster Analysis for Variables 266\u003c\/p\u003e \u003cp\u003e24.5 Discriminant Analysis 267\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25 Part Five: Case Studies – Regression and Multivariate Analysis 272\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e25.1 Tree 272\u003c\/p\u003e \u003cp\u003e25.2 Power Plant 278\u003c\/p\u003e \u003cp\u003e25.3 Wear 285\u003c\/p\u003e \u003cp\u003e25.4 TV Failure 290\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Six Experimental Design and Reliability 293\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e26 Factorial Designs: Creation 295\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e26.1 Creation of the Design Matrix 295\u003c\/p\u003e \u003cp\u003e26.2 Design Matrix with Data Already in the Worksheet 301\u003c\/p\u003e \u003cp\u003e\u003cb\u003e27 Factorial Designs: Analysis 303\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e27.1 Calculating the Effects and Determining the Significant Ones 303\u003c\/p\u003e \u003cp\u003e27.2 Interpretation of Results 308\u003c\/p\u003e \u003cp\u003e27.3 A Recap with a Fractional Factorial Design 310\u003c\/p\u003e \u003cp\u003e\u003cb\u003e28 Response Surface Methodology 313\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e28.1 Matrix Design Creation and Data Collection 313\u003c\/p\u003e \u003cp\u003e28.2 Analysis of the Results 317\u003c\/p\u003e \u003cp\u003e28.3 Contour Plots and Response Surface Plots 322\u003c\/p\u003e \u003cp\u003e\u003cb\u003e29 Reliability 325\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e29.1 File 325\u003c\/p\u003e \u003cp\u003e29.2 Nonparametric Analysis 326\u003c\/p\u003e \u003cp\u003e29.3 Identification of the Best Model for the Data 329\u003c\/p\u003e \u003cp\u003e29.4 Parametric Analysis 330\u003c\/p\u003e \u003cp\u003e29.5 General Graphical Display of Reliability Data 333\u003c\/p\u003e \u003cp\u003e\u003cb\u003e30 Part Six: Case Studies – Design of Experiments and Reliability 335\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e30.1 Cardigan 335\u003c\/p\u003e \u003cp\u003e30.2 Steering wheel – 1 340\u003c\/p\u003e \u003cp\u003e30.3 Steering Wheel – 2 343\u003c\/p\u003e \u003cp\u003e30.4 Paper Helicopters 345\u003c\/p\u003e \u003cp\u003e30.5 Microorganisms 349\u003c\/p\u003e \u003cp\u003e30.6 Jam 359\u003c\/p\u003e \u003cp\u003e30.7 Photocopies 365\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendices 371\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA1 Appendix 1: Answers to Questions that Arise at the Beginning 373\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA2 Appendix 2: Managing Data 377\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA2.1 Copy Columns with Restrictions (File: ‘PULSE’) 377\u003c\/p\u003e \u003cp\u003eA2.2 Selection of Data when Plotting a Graph 381\u003c\/p\u003e \u003cp\u003eA2.3 Stacking and Unstacking of Columns (File ‘BREAD’) 382\u003c\/p\u003e \u003cp\u003eA2.4 Coding and Sorting Data 386\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA3 Appendix 3: Customization of Minitab 390\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA3.1 Configuration Options 390\u003c\/p\u003e \u003cp\u003eA3.2 Use of Toolbars 392\u003c\/p\u003e \u003cp\u003eA3.3 Add Elements to an Existing Toolbar 392\u003c\/p\u003e \u003cp\u003eA3.4 Create Custom Toolbars 393\u003c\/p\u003e \u003cp\u003eIndex 397\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePere Grima\u003c\/b\u003e, Professor, Department of the Technical University of Catalonia UPC, Barcelona, Spain.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eLluis Marco\u003c\/b\u003e, Assistant Professor, Department of the Technical University of Catalonia UPC, Barcelona, Spain.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eXavier Tort-Martorell\u003c\/b\u003e, Department Director Statistics Department,the Technical University of Catalonia UPC, Barcelona, Spain. Xavier Tort-Martorell is president elect of ENBIS\u003cbr\u003eThe 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 \u0026amp; Gamble and ITP.\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eIndustrial Statistics with Minitab\u003c\/b\u003e Pere Grima Cintas, Lluís Marco Almagro, Xavier Tort-Martorell Llabrés \u003ci\u003eUniversitat Politècnica de Catalunya - BarcelonaTech, Barcelona, Spain\u003c\/i\u003e  \u003c\/p\u003e\u003cp\u003e\u003ci\u003eIndustrial Statistics with Minitab\u003c\/i\u003e demonstrates the use of Minitab as a tool for performing statistical analysis in an industrial context. This book covers introductory industrial statistics, exploring the most commonly used techniques alongside those that serve to give an overview of more complex issues. A plethora of examples in Minitab are featured along with case studies for each of the statistical techniques presented. \u003c\/p\u003e\u003cp\u003e\u003cb\u003e\u003ci\u003eIndustrial Statistics with Minitab:\u003c\/i\u003e\u003c\/b\u003e \u003c\/p\u003e\u003cul\u003e \u003cli\u003eProvides comprehensive coverage of user-friendly practical guidance to the essential statistical methods applied in industry.\u003c\/li\u003e \u003cli\u003eExplores statistical techniques and how they can be used effectively with the help of Minitab 16.\u003c\/li\u003e \u003cli\u003eContains extensive illustrative examples and case studies throughout and assumes no previous statistical knowledge.\u003c\/li\u003e \u003cli\u003eEmphasises data graphics and visualisation, and the most used industrial statistical tools, such as Statistical Process Control and Design of Experiments.\u003c\/li\u003e \u003cli\u003eIs supported by an accompanying website featuring case studies and the corresponding datasets.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eSix Sigma Green Belts and Black Belts will find explanations and examples of the most relevant techniques in DMAIC projects. This book can also be used as quick reference enabling the reader to be confident enough to explore other Minitab capabilities.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989418623205,"sku":"NP9780470972755","price":89.5,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470972755.jpg?v=1761784028","url":"https:\/\/k12savings.com\/products\/industrial-statistics-with-minitab-isbn-9780470972755","provider":"K12savings","version":"1.0","type":"link"}