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E-raamat: Industrial Statistics with Minitab

(Technical University of Catalonia UPC, Barcelona, Spain), (Technical University of Catalonia UPC, Barcelona, Spain), (Technical University of Catalonia UPC, Barcelona, Spain)
  • Formaat: PDF+DRM
  • Ilmumisaeg: 26-Jul-2012
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781118383797
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Industrial Statistics with MinitabSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 26-Jul-2012
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781118383797
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Three researchers from the Polytechnic University of Catalonia explain to students and professionals how to use the proprietary statistical software as a tool for performing statistical analysis. Their examples and the applications they describe pivot around quality control and improvement situations, but they say the material could be applied in most any business. They cover basics and graphical techniques, testing hypotheses and comparing treatments, measurements systems studies and capability studies, multi-vari charts and statistical process control, regression and multivariate analysis, and experimental design and reliability. Annotation ©2013 Book News, Inc., Portland, OR (booknews.com)

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.

Preface xiii
PART ONE INTRODUCTION AND GRAPHICAL TECHNIQUES
1(78)
1 A First Look
3(12)
1.1 Initial Screen
3(1)
1.2 Entering Data
4(1)
1.3 Saving Data: Worksheets and Projects
5(1)
1.4 Data Operations: An Introduction
5(2)
1.5 Deleting and Inserting Columns and Rows
7(1)
1.6 First Statistical Analyses
8(2)
1.7 Getting Help
10(2)
1.8 Personal Configuration
12(1)
1.9 Assistant
13(1)
1.10 Any Difficulties?
14(1)
2 Graphics for Univariate Data
15(16)
2.1 File `PULSE'
15(1)
2.2 Histograms
16(1)
2.3 Changing the Appearance of Histograms
17(4)
2.4 Histograms for Various Data Sets
21(2)
2.5 Dotplots
23(1)
2.6 Boxplots
24(1)
2.7 Bar Diagrams
25(2)
2.8 Pie Charts
27(1)
2.9 Updating Graphs Automatically
28(1)
2.10 Adding Text or Figures to a Graph
29(2)
3 Pareto Charts and Cause-Effect Diagrams
31(6)
3.1 File `DETERGENT'
31(1)
3.2 Pareto Charts
32(3)
3.4 Cause-and-Effect Diagrams
35(2)
4 Scatterplots
37(15)
4.1 File `pulse'
37(1)
4.2 Stratification
38(1)
4.3 Identifying Points on a Graph
39(6)
4.4 Using the `Crosshairs' Option
45(1)
4.5 Scatterplots with Panels
46(2)
4.6 Scatterplots with Marginal Graphs
48(2)
4.7 Creating an Array of Scatterplots
50(2)
5 Three Dimensional Plots
52(10)
5.1 3D Scatterplots
52(3)
5.2 3D Surface Plots
55(3)
5.3 Contour Plots
58(4)
6 Part One: Case Studies - Introduction and Graphical Techniques
62(17)
6.1 Cork
62(6)
6.2 Copper
68(5)
6.3 Bread
73(3)
6.4 Humidity
76(3)
PART TWO HYPOTHESIS TESTING. COMPARISON OF TREATMENTS
79(58)
7 Random Numbers and Numbers Following a Pattern
81(6)
7.1 Introducing Values Following a Pattern
81(2)
7.2 Sampling Random Data from a Column
83(1)
7.3 Random Number Generation
83(2)
7.4 Example: Solving a Problem Using Random Numbers
85(2)
8 Computing Probabilities
87(8)
8.1 Probability Distributions
87(1)
8.2 Option `Probability Density' or `Probability'
88(1)
8.3 Option `Cumulative Probability'
89(1)
8.4 Option `Inverse Cumulative Probability'
89(3)
8.5 Viewing the Shape of the Distributions
92(1)
8.6 Equivalence between Sigmas of the Process and Defects per Million Parts Using `Cumulative Probability'
92(3)
9 Hypothesis Testing for Means and Proportions. Normality Test
95(8)
9.1 Hypothesis Testing for One Mean
95(4)
9.2 Hypothesis Testing and Confidence Interval for a Proportion
99(1)
9.3 Normality Test
100(3)
10 Comparison of Two Means, Two Variances or Two Proportions
103(7)
10.1 Comparison of Two Means
103(4)
10.2 Comparison of Two Variances
107(2)
10.3 Comparison of Two Proportions
109(1)
11 Comparison of More than Two Means: Analysis of Variance
110(10)
11.1 ANOVA (Analysis of Variance)
110(1)
11.2 ANOVA with a Single Factor
110(4)
11.3 ANOVA with Two Factors
114(5)
11.4 Test for Homogeneity of Variances
119(1)
12 Part Two: Case Studies - Hypothesis Testing. Comparison of Treatments
120(17)
12.1 Welding
120(4)
12.2 Rivets
124(2)
12.3 Almonds
126(1)
12.4 Arrow
127(4)
12.5 U Piece
131(2)
12.6 Pores
133(4)
PART THREE MEASUREMENT SYSTEMS STUDIES AND CAPABILITY STUDIES
137(44)
13 Measurement System Study
139(12)
13.1 Crossed Designs and Nested Designs
139(1)
13.2 File `RR_CROSSED'
140(1)
13.3 Graphical Analysis
140(1)
13.4 R&R Study for the Data in File `RR_CROSSED'
141(6)
13.5 File `RR_NESTED'
147(1)
13.6 Gage R&R Study for the Data in File `RR_NESTED'
147(1)
13.7 File `GAGELIN'
148(1)
13.8 Calibration and Linearity Study of the Measurement System
148(3)
14 Capability Studies
151(12)
14.1 Capability Analysis: Available Options
151(1)
14.2 File `VITA_C'
152(1)
14.3 Capability Analysis (Normal Distribution)
152(1)
14.4 Interpreting the Obtained Information
152(2)
14.5 Customizing the Study
154(1)
14.6 `Within' Variability and `Overall' Variability
155(3)
14.7 Capability Study when the Sample Size Is Equal to One
158(3)
14.8 A More Detailed Data Analysis (Capability Sixpack)
161(2)
15 Capability Studies for Attributes
163(5)
15.1 File `BANK'
163(1)
15.2 Capability Study for Variables that Follow a Binomial Distribution
163(3)
15.3 File `OVEN_PAINTED'
166(1)
15.4 Capability Study for Variables that Follow a Poisson Distribution
166(2)
16 Part Three: Case Studies - R&R Studies and Capability Studies
168(13)
16.1 Diameter_measure
168(5)
16.2 Diameter_capability_1
173(1)
16.3 Diameter_capability_2
174(2)
16.4 Web_visits
176(5)
PART FOUR MULTI-VARI CHARTS AND STATISTICAL PROCESS CONTROL
181(50)
17 Multi-Vari Charts
183(5)
17.1 File `MUFFIN'
183(1)
17.2 Multi-Vari Chart with Three Sources of Variation
184(2)
17.3 Multi-Vari Chart with Four Sources of Variation
186(2)
18 Control Charts I: Individual Observations
188(10)
18.1 File `CHLORINE'
188(1)
18.2 Graph of Individual Observations
188(3)
18.3 Customizing the Graph
191(1)
18.4 I Chart Options
192(4)
18.5 Graphs of Moving Ranges
196(1)
18.6 Graph of Individual Observations - Moving Ranges
197(1)
19 Control Charts II: Means and Ranges
198(6)
19.1 File `VITA_C'
198(1)
19.2 Means Chart
199(1)
19.3 Graphs of Ranges and Standard Deviations
200(1)
19.4 Graphs of Means-Ranges
201(1)
19.5 Some Ideas on How to Use Minitab as a Simulator of Processes for Didactic Reasons
201(3)
20 Control Charts for Attributes
204(8)
20.1 File `MOTORS'
204(1)
20.2 Plotting the Proportion of Defective Units (P)
204(1)
20.3 File `CATHETER'
205(1)
20.4 Plotting the Number of Defective Units (NP)
206(2)
20.5 Plotting the Number of Defects per Constant Unit of Measurement (C)
208(2)
20.6 File `FABRIC'
210(1)
20.7 Plotting the Number of Defects per Variable Unit of Measurement (U)
210(2)
21 Part Four: Case Studies - Multi-Vari Charts and Statistical Process Control
212(19)
21.1 Bottles
212(5)
21.2 Mattresses (1st Part)
217(4)
21.3 Mattresses (2nd Part)
221(2)
21.4 Plastic (1st Part)
223(1)
21.5 Plastic (2nd Part)
224(7)
PART FIVE REGRESSION AND MULTIVARIATE ANALYSIS
231(62)
22 Correlation and Simple Regression
235(12)
22.1 Correlation Coefficient
235(3)
22.2 Simple Regression
238(1)
22.3 Simple Regression with `Fitted Line Plot'
239(5)
22.4 Simple Regression with `Regression'
244(3)
23 Multiple Regression
247(9)
23.1 File `CARS2'
247(1)
23.2 Exploratory Analysis
247(2)
23.3 Multiple Regression
249(1)
23.4 Option Buttons
250(2)
23.5 Selection of the Best Equation: Best Subsets
252(2)
23.6 Selection of the Best Equation: Stepwise
254(2)
24 Multivariate Analysis
256(16)
24.1 File `LATIN_AMERICA'
256(1)
24.2 Principal Components
257(6)
24.3 Cluster Analysis for Observations
263(3)
24.4 Cluster Analysis for Variables
266(1)
24.5 Discriminant Analysis
267(5)
25 Part Five: Case Studies - Regression and Multivariate Analysis
272(21)
25.1 Tree
272(6)
25.2 Power Plant
278(7)
25.3 Wear
285(5)
25.4 TV Failure
290(3)
PART SIX EXPERIMENTAL DESIGN AND RELIABILITY
293(78)
26 Factorial Designs: Creation
295(8)
26.1 Creation of the Design Matrix
295(6)
26.2 Design Matrix with Data Already in the Worksheet
301(2)
27 Factorial Designs: Analysis
303(10)
27.1 Calculating the Effects and Determining the Significant Ones
303(5)
27.2 Interpretation of Results
308(2)
27.3 A Recap with a Fractional Factorial Design
310(3)
28 Response Surface Methodology
313(12)
28.1 Matrix Design Creation and Data Collection
313(4)
28.2 Analysis of the Results
317(5)
28.3 Contour Plots and Response Surface Plots
322(3)
29 Reliability
325(10)
29.1 File
325(1)
29.2 Nonparametric Analysis
326(3)
29.3 Identification of the Best Model for the Data
329(1)
29.4 Parametric Analysis
330(3)
29.5 General Graphical Display of Reliability Data
333(2)
30 Part Six: Case Studies - Design of Experiments and Reliability
335(36)
30.1 Cardigan
335(5)
30.2 Steering wheel - 1
340(3)
30.3 Steering Wheel - 2
343(2)
30.4 Paper Helicopters
345(4)
30.5 Microorganisms
349(10)
30.6 Jam
359(6)
30.7 Photocopies
365(6)
APPENDICES
371(26)
A1 Appendix 1: Answers to Questions that Arise at the Beginning
373(4)
A2 Appendix 2: g Data
377(20)
A2.1 Copy Columns with Restrictions (File: `PULSE')
377(4)
A2.2 Selection of Data when Plotting a Graph
381(1)
A2.3 Stacking and Unstacking of Columns (File `BREAD')
382(4)
A2.4 Coding and Sorting Data
386(4)
A3 Appendix 3: Customization of Minitab
390(1)
A3.1 Configuration Options
390(2)
A3.2 Use of Toolbars
392(1)
A3.3 Add Elements to an Existing Toolbar
392(1)
A3.4 Create Custom Toolbars
393(4)
Index 397
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.
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