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<p>Preface xiii</p> <p><b>Part One Introduction and Graphical Techniques 1</b></p> <p><b>1 A First Look 3</b></p> <p>1.1 Initial Screen 3</p> <p>1.2 Entering Data 4</p> <p>1.3 Saving Data: Worksheets and Projects 5</p> <p>1.4 Data Operations: An Introduction 5</p> <p>1.5 Deleting and Inserting Columns and Rows 7</p> <p>1.6 First Statistical Analyses 8</p> <p>1.7 Getting Help 10</p> <p>1.8 Personal Configuration 12</p> <p>1.9 Assistant 13</p> <p>1.10 Any Difficulties? 14</p> <p><b>2 Graphics for Univariate Data 15</b></p> <p>2.1 File ‘PULSE’ 15</p> <p>2.2 Histograms 16</p> <p>2.3 Changing the Appearance of Histograms 17</p> <p>2.4 Histograms for Various Data Sets 21</p> <p>2.5 Dotplots 23</p> <p>2.6 Boxplots 24</p> <p>2.7 Bar Diagrams 25</p> <p>2.8 Pie Charts 27</p> <p>2.9 Updating Graphs Automatically 28</p> <p>2.10 Adding Text or Figures to a Graph 29</p> <p><b>3 Pareto Charts and Cause–Effect Diagrams 31</b></p> <p>3.1 File ‘DETERGENT’ 31</p> <p>3.2 Pareto Charts 32</p> <p>3.4 Cause-and-Effect Diagrams 35</p> <p><b>4 Scatterplots 37</b></p> <p>4.1 File ‘pulse’ 37</p> <p>4.2 Stratification 38</p> <p>4.3 Identifying Points on a Graph 39</p> <p>4.4 Using the ‘Crosshairs’ Option 45</p> <p>4.5 Scatterplots with Panels 46</p> <p>4.6 Scatterplots with Marginal Graphs 48</p> <p>4.7 Creating an Array of Scatterplots 50</p> <p><b>5 Three Dimensional Plots 52</b></p> <p>5.1 3D Scatterplots 52</p> <p>5.2 3D Surface Plots 55</p> <p>5.3 Contour Plots 58</p> <p><b>6 Part One: Case Studies – Introduction and Graphical Techniques 62</b></p> <p>6.1 Cork 62</p> <p>6.2 Copper 68</p> <p>6.3 Bread 73</p> <p>6.4 Humidity 76</p> <p><b>Part Two Hypothesis Testing. Comparison of Treatments 79</b></p> <p><b>7 Random Numbers and Numbers Following a Pattern 81</b></p> <p>7.1 Introducing Values Following a Pattern 81</p> <p>7.2 Sampling Random Data from a Column 83</p> <p>7.3 Random Number Generation 83</p> <p>7.4 Example: Solving a Problem Using Random Numbers 85</p> <p><b>8 Computing Probabilities 87</b></p> <p>8.1 Probability Distributions 87</p> <p>8.2 Option ‘Probability Density’ or ‘Probability’ 88</p> <p>8.3 Option ‘Cumulative Probability’ 89</p> <p>8.4 Option ‘Inverse Cumulative Probability’ 89</p> <p>8.5 Viewing the Shape of the Distributions 92</p> <p>8.6 Equivalence between Sigmas of the Process and Defects per Million Parts Using <i>‘Cumulative Probability’ </i>92</p> <p><b>9 Hypothesis Testing for Means and Proportions. Normality Test 95</b></p> <p>9.1 Hypothesis Testing for One Mean 95</p> <p>9.2 Hypothesis Testing and Confidence Interval for a Proportion 99</p> <p>9.3 Normality Test 100</p> <p><b>10 Comparison of Two Means, Two Variances or Two Proportions 103</b></p> <p>10.1 Comparison of Two Means 103</p> <p>10.2 Comparison of Two Variances 107</p> <p>10.3 Comparison of Two Proportions 109</p> <p><b>11 Comparison of More than Two Means: Analysis of Variance 110</b></p> <p>11.1 ANOVA (Analysis of Variance) 110</p> <p>11.2 ANOVA with a Single Factor 110</p> <p>11.3 ANOVA with Two Factors 114</p> <p>11.4 Test for Homogeneity of Variances 119</p> <p><b>12 Part Two: Case Studies – Hypothesis Testing. Comparison of Treatments 120</b></p> <p>12.1 Welding 120</p> <p>12.2 Rivets 124</p> <p>12.3 Almonds 126</p> <p>12.4 Arrow 127</p> <p>12.5 U Piece 131</p> <p>12.6 Pores 133</p> <p><b>Part Three Measurement Systems Studies and Capability Studies 137</b></p> <p><b>13 Measurement System Study 139</b></p> <p>13.1 Crossed Designs and Nested Designs 139</p> <p>13.2 File ‘RR_CROSSED’ 140</p> <p>13.3 Graphical Analysis 140</p> <p>13.4 R&R Study for the Data in File ‘RR_CROSSED’ 141</p> <p>13.5 File ‘RR_NESTED’ 147</p> <p>13.6 Gage R&R Study for the Data in File ‘RR_NESTED’ 147</p> <p>13.7 File ‘GAGELIN’ 148</p> <p>13.8 Calibration and Linearity Study of the Measurement System 148</p> <p><b>14 Capability Studies 151</b></p> <p>14.1 Capability Analysis: Available Options 151</p> <p>14.2 File ‘VITA_C’ 152</p> <p>14.3 Capability Analysis (Normal Distribution) 152</p> <p>14.4 Interpreting the Obtained Information 152</p> <p>14.5 Customizing the Study 154</p> <p>14.6 ‘Within’ Variability and ‘Overall’ Variability 155</p> <p>14.7 Capability Study when the Sample Size is Equal to One 158</p> <p>14.8 A More Detailed Data Analysis (Capability Sixpack) 161</p> <p><b>15 Capability Studies for Attributes 163</b></p> <p>15.1 File ‘BANK’ 163</p> <p>15.2 Capability Study for Variables that Follow a Binomial Distribution 163</p> <p>15.3 File ‘OVEN_PAINTED’ 166</p> <p>15.4 Capability Study for Variables that Follow a Poisson Distribution 166</p> <p><b>16 Part Three: Case Studies – R&R Studies and Capability Studies 168</b></p> <p>16.1 Diameter_measure 168</p> <p>16.2 Diameter_capability_1 173</p> <p>16.3 Diameter_capability_2 174</p> <p>16.4 Web_visits 176</p> <p><b>Part Four Multi-Vari Charts and Statistical Process Control 181</b></p> <p><b>17 Multi-Vari Charts 183</b></p> <p>17.1 File ‘MUFFIN’ 183</p> <p>17.2 Multi-Vari Chart with Three Sources of Variation 184</p> <p>17.3 Multi-Vari Chart with Four Sources of Variation 186</p> <p><b>18 Control Charts I: Individual Observations 188</b></p> <p>18.1 File ‘CHLORINE’ 188</p> <p>18.2 Graph of Individual Observations 188</p> <p>18.3 Customizing the Graph 191</p> <p>18.4 I Chart Options 192</p> <p>18.5 Graphs of Moving Ranges 196</p> <p>18.6 Graph of Individual Observations – Moving Ranges 197</p> <p><b>19 Control Charts II: Means and Ranges 198</b></p> <p>19.1 File ‘VITA_C’ 198</p> <p>19.2 Means Chart 199</p> <p>19.3 Graphs of Ranges and Standard Deviations 200</p> <p>19.4 Graphs of Means-Ranges 201</p> <p>19.5 Some Ideas on How to Use Minitab as a Simulator of Processes for Didactic Reasons 201</p> <p><b>20 Control Charts for Attributes 204</b></p> <p>20.1 File ‘MOTORS’ 204</p> <p>20.2 Plotting the Proportion of Defective Units (P) 204</p> <p>20.3 File ‘CATHETER’ 205</p> <p>20.4 Plotting the Number of Defective Units (NP) 206</p> <p>20.5 Plotting the Number of Defects per Constant Unit of Measurement (C) 208</p> <p>20.6 File ‘FABRIC’ 210</p> <p>20.7 Plotting the Number of Defects per Variable Unit of Measurement (U) 210</p> <p><b>21 Part Four: Case Studies – Multi-Vari Charts and Statistical Process Control 212</b></p> <p>21.1 Bottles 212</p> <p>21.2 Mattresses (1st Part) 217</p> <p>21.3 Mattresses (2nd Part) 221</p> <p>21.4 Plastic (1st Part) 223</p> <p>21.5 Plastic (2nd Part) 224</p> <p><b>Part Five Regression and Multivariate Analysis 231</b></p> <p><b>22 Correlation and Simple Regression 235</b></p> <p>22.1 Correlation Coefficient 235</p> <p>22.2 Simple Regression 238</p> <p>22.3 Simple Regression with ‘Fitted Line Plot’ 239</p> <p>22.4 Simple Regression with ‘Regression’ 244</p> <p><b>23 Multiple Regression 247</b></p> <p>23.1 File ‘CARS2’ 247</p> <p>23.2 Exploratory Analysis 247</p> <p>23.3 Multiple Regression 249</p> <p>23.4 Option Buttons 250</p> <p>23.5 Selection of the Best Equation: Best Subsets 252</p> <p>23.6 Selection of the Best Equation: Stepwise 254</p> <p><b>24 Multivariate Analysis 256</b></p> <p>24.1 File ‘LATIN_AMERICA’ 256</p> <p>24.2 Principal Components 257</p> <p>24.3 Cluster Analysis for Observations 263</p> <p>24.4 Cluster Analysis for Variables 266</p> <p>24.5 Discriminant Analysis 267</p> <p><b>25 Part Five: Case Studies – Regression and Multivariate Analysis 272</b></p> <p>25.1 Tree 272</p> <p>25.2 Power Plant 278</p> <p>25.3 Wear 285</p> <p>25.4 TV Failure 290</p> <p><b>Part Six Experimental Design and Reliability 293</b></p> <p><b>26 Factorial Designs: Creation 295</b></p> <p>26.1 Creation of the Design Matrix 295</p> <p>26.2 Design Matrix with Data Already in the Worksheet 301</p> <p><b>27 Factorial Designs: Analysis 303</b></p> <p>27.1 Calculating the Effects and Determining the Significant Ones 303</p> <p>27.2 Interpretation of Results 308</p> <p>27.3 A Recap with a Fractional Factorial Design 310</p> <p><b>28 Response Surface Methodology 313</b></p> <p>28.1 Matrix Design Creation and Data Collection 313</p> <p>28.2 Analysis of the Results 317</p> <p>28.3 Contour Plots and Response Surface Plots 322</p> <p><b>29 Reliability 325</b></p> <p>29.1 File 325</p> <p>29.2 Nonparametric Analysis 326</p> <p>29.3 Identification of the Best Model for the Data 329</p> <p>29.4 Parametric Analysis 330</p> <p>29.5 General Graphical Display of Reliability Data 333</p> <p><b>30 Part Six: Case Studies – Design of Experiments and Reliability 335</b></p> <p>30.1 Cardigan 335</p> <p>30.2 Steering wheel – 1 340</p> <p>30.3 Steering Wheel – 2 343</p> <p>30.4 Paper Helicopters 345</p> <p>30.5 Microorganisms 349</p> <p>30.6 Jam 359</p> <p>30.7 Photocopies 365</p> <p><b>Appendices 371</b></p> <p><b>A1 Appendix 1: Answers to Questions that Arise at the Beginning 373</b></p> <p><b>A2 Appendix 2: Managing Data 377</b></p> <p>A2.1 Copy Columns with Restrictions (File: ‘PULSE’) 377</p> <p>A2.2 Selection of Data when Plotting a Graph 381</p> <p>A2.3 Stacking and Unstacking of Columns (File ‘BREAD’) 382</p> <p>A2.4 Coding and Sorting Data 386</p> <p><b>A3 Appendix 3: Customization of Minitab 390</b></p> <p>A3.1 Configuration Options 390</p> <p>A3.2 Use of Toolbars 392</p> <p>A3.3 Add Elements to an Existing Toolbar 392</p> <p>A3.4 Create Custom Toolbars 393</p> <p>Index 397</p>

<p><b>Pere Grima Cintas, Lluís Marco-Almagro</b> <b>and Xavier Tort-Martorell Llabrés</b>, Universitat Politècnica de Catalunya. BarcelonaTech Barcelona, Spain</p>

<p><b>Industrial Statistics with Minitab</b></br> Pere Grima Cintas, Lluís Marco Almagro, Xavier Tort-Martorell Llabrés</br> <i>Universitat Politècnica de Catalunya - BarcelonaTech, Barcelona, Spain</i> <p><i>Industrial Statistics with Minitab</i> 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. <p><b><i>Industrial Statistics with Minitab:</i></b> <ul> <li>Provides comprehensive coverage of user-friendly practical guidance to the essential statistical methods applied in industry.</li> <li>Explores statistical techniques and how they can be used effectively with the help of Minitab 16.</li> <li>Contains extensive illustrative examples and case studies throughout and assumes no previous statistical knowledge.</li> <li>Emphasises data graphics and visualisation, and the most used industrial statistical tools, such as Statistical Process Control and Design of Experiments.</li> <li>Is supported by an accompanying website featuring case studies and the corresponding datasets.</li> </ul> <p>Six 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.

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