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

Industrial Statistics with Minitab (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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.

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Produktdetails

• Verlag: Wiley-Blackwell
• Seitenzahl: 424
• Erscheinungstermin: 2. August 2012
• Englisch
• ISBN-13: 9781118383780
• Artikelnr.: 37339765

Autorenporträt

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

Inhaltsangabe

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