Industrial Statistics with Minitab

Inbunden, Engelska, 2012

AvPere Grima Cintas,Lluis Marco Almagro,Xavier Tort-Martorell Llabres

1 299 kr

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Industrial Statistics with MINITAB 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.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.

Produktinformation

Utgivningsdatum2012-09-14
Mått158 x 236 x 24 mm
Vikt653 g
FormatInbunden
SpråkEngelska
Antal sidor432
FörlagJohn Wiley & Sons Inc
ISBN9780470972755

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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 ENBISThe 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 xiiiPart One Introduction and Graphical Techniques 11 A First Look 31.1 Initial Screen 31.2 Entering Data 41.3 Saving Data: Worksheets and Projects 51.4 Data Operations: An Introduction 51.5 Deleting and Inserting Columns and Rows 71.6 First Statistical Analyses 81.7 Getting Help 101.8 Personal Configuration 121.9 Assistant 131.10 Any Difficulties? 142 Graphics for Univariate Data 152.1 File ‘PULSE’ 152.2 Histograms 162.3 Changing the Appearance of Histograms 172.4 Histograms for Various Data Sets 212.5 Dotplots 232.6 Boxplots 242.7 Bar Diagrams 252.8 Pie Charts 272.9 Updating Graphs Automatically 282.10 Adding Text or Figures to a Graph 293 Pareto Charts and Cause–Effect Diagrams 313.1 File ‘DETERGENT’ 313.2 Pareto Charts 323.4 Cause-and-Effect Diagrams 354 Scatterplots 374.1 File ‘pulse’ 374.2 Stratification 384.3 Identifying Points on a Graph 394.4 Using the ‘Crosshairs’ Option 454.5 Scatterplots with Panels 464.6 Scatterplots with Marginal Graphs 484.7 Creating an Array of Scatterplots 505 Three Dimensional Plots 525.1 3D Scatterplots 525.2 3D Surface Plots 555.3 Contour Plots 586 Part One: Case Studies – Introduction and Graphical Techniques 626.1 Cork 626.2 Copper 686.3 Bread 736.4 Humidity 76Part Two Hypothesis Testing. Comparison of Treatments 797 Random Numbers and Numbers Following a Pattern 817.1 Introducing Values Following a Pattern 817.2 Sampling Random Data from a Column 837.3 Random Number Generation 837.4 Example: Solving a Problem Using Random Numbers 858 Computing Probabilities 878.1 Probability Distributions 878.2 Option ‘Probability Density’ or ‘Probability’ 888.3 Option ‘Cumulative Probability’ 898.4 Option ‘Inverse Cumulative Probability’ 898.5 Viewing the Shape of the Distributions 928.6 Equivalence between Sigmas of the Process and Defects per Million Parts Using ‘Cumulative Probability’ 929 Hypothesis Testing for Means and Proportions. Normality Test 959.1 Hypothesis Testing for One Mean 959.2 Hypothesis Testing and Confidence Interval for a Proportion 999.3 Normality Test 10010 Comparison of Two Means, Two Variances or Two Proportions 10310.1 Comparison of Two Means 10310.2 Comparison of Two Variances 10710.3 Comparison of Two Proportions 10911 Comparison of More than Two Means: Analysis of Variance 11011.1 ANOVA (Analysis of Variance) 11011.2 ANOVA with a Single Factor 11011.3 ANOVA with Two Factors 11411.4 Test for Homogeneity of Variances 11912 Part Two: Case Studies – Hypothesis Testing. Comparison of Treatments 12012.1 Welding 12012.2 Rivets 12412.3 Almonds 12612.4 Arrow 12712.5 U Piece 13112.6 Pores 133Part Three Measurement Systems Studies and Capability Studies 13713 Measurement System Study 13913.1 Crossed Designs and Nested Designs 13913.2 File ‘RR_CROSSED’ 14013.3 Graphical Analysis 14013.4 R&R Study for the Data in File ‘RR_CROSSED’ 14113.5 File ‘RR_NESTED’ 14713.6 Gage R&R Study for the Data in File ‘RR_NESTED’ 14713.7 File ‘GAGELIN’ 14813.8 Calibration and Linearity Study of the Measurement System 14814 Capability Studies 15114.1 Capability Analysis: Available Options 15114.2 File ‘VITA_C’ 15214.3 Capability Analysis (Normal Distribution) 15214.4 Interpreting the Obtained Information 15214.5 Customizing the Study 15414.6 ‘Within’ Variability and ‘Overall’ Variability 15514.7 Capability Study when the Sample Size is Equal to One 15814.8 A More Detailed Data Analysis (Capability Sixpack) 16115 Capability Studies for Attributes 16315.1 File ‘BANK’ 16315.2 Capability Study for Variables that Follow a Binomial Distribution 16315.3 File ‘OVEN_PAINTED’ 16615.4 Capability Study for Variables that Follow a Poisson Distribution 16616 Part Three: Case Studies – R&R Studies and Capability Studies 16816.1 Diameter_measure 16816.2 Diameter_capability_1 17316.3 Diameter_capability_2 17416.4 Web_visits 176Part Four Multi-Vari Charts and Statistical Process Control 18117 Multi-Vari Charts 18317.1 File ‘MUFFIN’ 18317.2 Multi-Vari Chart with Three Sources of Variation 18417.3 Multi-Vari Chart with Four Sources of Variation 18618 Control Charts I: Individual Observations 18818.1 File ‘CHLORINE’ 18818.2 Graph of Individual Observations 18818.3 Customizing the Graph 19118.4 I Chart Options 19218.5 Graphs of Moving Ranges 19618.6 Graph of Individual Observations – Moving Ranges 19719 Control Charts II: Means and Ranges 19819.1 File ‘VITA_C’ 19819.2 Means Chart 19919.3 Graphs of Ranges and Standard Deviations 20019.4 Graphs of Means-Ranges 20119.5 Some Ideas on How to Use Minitab as a Simulator of Processes for Didactic Reasons 20120 Control Charts for Attributes 20420.1 File ‘MOTORS’ 20420.2 Plotting the Proportion of Defective Units (P) 20420.3 File ‘CATHETER’ 20520.4 Plotting the Number of Defective Units (NP) 20620.5 Plotting the Number of Defects per Constant Unit of Measurement (C) 20820.6 File ‘FABRIC’ 21020.7 Plotting the Number of Defects per Variable Unit of Measurement (U) 21021 Part Four: Case Studies – Multi-Vari Charts and Statistical Process Control 21221.1 Bottles 21221.2 Mattresses (1st Part) 21721.3 Mattresses (2nd Part) 22121.4 Plastic (1st Part) 22321.5 Plastic (2nd Part) 224Part Five Regression and Multivariate Analysis 23122 Correlation and Simple Regression 23522.1 Correlation Coefficient 23522.2 Simple Regression 23822.3 Simple Regression with ‘Fitted Line Plot’ 23922.4 Simple Regression with ‘Regression’ 24423 Multiple Regression 24723.1 File ‘CARS2’ 24723.2 Exploratory Analysis 24723.3 Multiple Regression 24923.4 Option Buttons 25023.5 Selection of the Best Equation: Best Subsets 25223.6 Selection of the Best Equation: Stepwise 25424 Multivariate Analysis 25624.1 File ‘LATIN_AMERICA’ 25624.2 Principal Components 25724.3 Cluster Analysis for Observations 26324.4 Cluster Analysis for Variables 26624.5 Discriminant Analysis 26725 Part Five: Case Studies – Regression and Multivariate Analysis 27225.1 Tree 27225.2 Power Plant 27825.3 Wear 28525.4 TV Failure 290Part Six Experimental Design and Reliability 29326 Factorial Designs: Creation 29526.1 Creation of the Design Matrix 29526.2 Design Matrix with Data Already in the Worksheet 30127 Factorial Designs: Analysis 30327.1 Calculating the Effects and Determining the Significant Ones 30327.2 Interpretation of Results 30827.3 A Recap with a Fractional Factorial Design 31028 Response Surface Methodology 31328.1 Matrix Design Creation and Data Collection 31328.2 Analysis of the Results 31728.3 Contour Plots and Response Surface Plots 32229 Reliability 32529.1 File 32529.2 Nonparametric Analysis 32629.3 Identification of the Best Model for the Data 32929.4 Parametric Analysis 33029.5 General Graphical Display of Reliability Data 33330 Part Six: Case Studies – Design of Experiments and Reliability 33530.1 Cardigan 33530.2 Steering wheel – 1 34030.3 Steering Wheel – 2 34330.4 Paper Helicopters 34530.5 Microorganisms 34930.6 Jam 35930.7 Photocopies 365Appendices 371A1 Appendix 1: Answers to Questions that Arise at the Beginning 373A2 Appendix 2: Managing Data 377A2.1 Copy Columns with Restrictions (File: ‘PULSE’) 377A2.2 Selection of Data when Plotting a Graph 381A2.3 Stacking and Unstacking of Columns (File ‘BREAD’) 382A2.4 Coding and Sorting Data 386A3 Appendix 3: Customization of Minitab 390A3.1 Configuration Options 390A3.2 Use of Toolbars 392A3.3 Add Elements to an Existing Toolbar 392A3.4 Create Custom Toolbars 393Index 397