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Industrial Statistics with Minitab Book

Industrial Statistics with Minitab
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Industrial Statistics with Minitab, 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 th, Industrial Statistics with Minitab
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  • Industrial Statistics with Minitab
  • Written by author Pere Grima Cintas
  • Published by Wiley, John & Sons, Incorporated, 10/9/2012
  • 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 th
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Preface

Part One Introduction: Graphical Techniques

1 A First Look

1.1 Initial Screen

1.2 Entering Data

1.3 Saving Data: Worksheets and Projects

1.4 Data Operations: An Introduction

1.5 An Introduction to Data Management: Delete and Insert

1.6 First Statistical Analyses

1.7 Getting Help

1.8 Personal Configuration

1.9 Assistant

1.10 Any Difficulties?

2 Graphics for Univariate Data

2.1 File ‘PULSE’

2.2 Histograms

2.3 Changing the Appearance of Histograms

2.4 Histograms for Various Data Sets

2.5 Dotplots

2.6 Boxplots

2.7 Bar Diagrams

2.8 Pie Charts

2.9 Updating Graphs Automatically

2.10 Adding Text or Figures to a Graph

3 Pareto Charts and Cause–Effect Diagrams

3.1 File ‘DETERGENT’

3.2 Pareto Charts

3.3 Changing Appearance of Pareto Charts

3.4 Cause-and-Effect Diagrams

4 Scatterplots

4.1 Scatterplots

4.2 Stratification

4.3 Identifying Points on a Graph

4.4 Using the ‘Crosshairs’ Option

4.5 Scatterplots with Panels

4.6 Scatterplots with Marginal Graphs

4.7 Creating an Array of Scatterplots

5 Three Dimensional Plots

5.1 3D Scatterplots

5.2 Stratification

5.3 3D Surface Plots

5.4 Contour Plots

6 Part One Case Studies – Introduction: Graphical Techniques

6.1 Cork

6.2 Copper

6.3 Bread

6.4 Humidity

Part Two Hypothesis Testing: Comparison of Treatments

7 Random numbers and Numbers Following a Pattern

7.1 Introducing Values Following a Pattern

7.2 Sampling Random Data from a Column

7.3 Random Number Generation

7.4 Example: Solving a Problem Using Random Numbers

8 Computing Probabilities

8.1 Probability Distributions

8.2 Option ‘Probability Density’ or ‘Probability’

8.3 Option ‘Cumulative Probability’

8.4 Option ‘Inverse Cumulative Probability’

8.5 Viewing the Shape of the Distributions

8.6 Equivalence between Sigmas of the Process and Defects

per Million Parts Using ‘Cumulative Probability’

9 Hypothesis Testing: The Normality Test

9.1 Hypothesis Testing for One Mean

9.2 Hypothesis Testing and Confidence Interval for a Proportion

9.3 Normality Test

10 Comparison of Two Means, Two Variances or Two Proportions

10.1 Comparison of Two Means

10.2 Comparison of Two Variances

10.3 Comparison of Two Proportions

11 Comparison of More Than Two Means: Analysis of Variance

11.1 ANOVA (Analysis of Variance)

11.2 ANOVA with a Single Factor

11.3 ANOVA with Two Factors

11.4 Test for Homogeneity of Variances

12 Part Two Case Studies - Hypothesis Testing: Comparison of Treatments

12.1 Welding

12.2 Rivets

12.3 Almonds

12.4 Arrow

12.5 U Piece

12.6 Pores

Part Three Measurement Systems Studies and Capability studies

13 Measurement System Study

13.1 Crossed Designs and Nested Designs

13.2 File ‘RR_CROSSED’

13.3 Graphical Analysis

13.4 R&R Study for the Data in File ‘RR_CROSSED’

13.5 File ‘RR_NESTED’

13.6 Gage R&R Study for the Data in File ‘RR_NESTED’

13.7 File ‘GAGELIN’

13.8 Calibration and Linearity Study of the Measurement System

14 Capability Studies

14.1 Available Options

14.2 File ‘VITA_C’

14.3 Capability Analysis (Normal Distribution)

14.4 Interpreting the Obtained Information

14.5 Customizing the Study

14.6 ‘Within’ Variability and ‘Overall’ Variability

14.7 Capability Study when the Sample Size Is Equal to One

14.8 A More Detailed Data Analysis (Capability Sixpack)

15 Capability Studies for Attributes

15.1 File ‘BANK’

15.2 Capability Study for Variables that Follow a Binomial Distribution

15.3 File ‘OVEN_PAINTED’

15.4 Capability Study for Variables that Follow a Poisson Distribution

16 Part Three Case Studies – R&R Studies and Capability Studies

16.1 Diameter_measure

16.2 Diameter_capability_1

16.3 Diameter_capability_2

16.4 Web_visits

Part Four Multi-Vari Charts and Statistical Process Control

17 Multi-Vari Charts

17.1 File ‘MUFFIN’

17.2 Multi-Vari Chart with Three Sources of Variation

17.3 Multi-Vari Chart with Four Sources of Variation

18 Control Charts I: Individual Observations

18.1 File ‘CHLORINE’

18.2 Graph of Individual Observations

18.3 Customizing the Graph

18.4 I Chart Options

18.5 Graphs of Moving Ranges

18.6 Graph of Individual Observations – Moving Ranges

19 Control Charts II: Means and Ranges

19.1 Use of File ‘VITA_C’

19.2 Means Chart

19.3 Graphs of Ranges and Standard Deviations

19.4 Graphs of Means-Ranges

19.5 Some Ideas on How to Use MINITAB as a Simulator of Processes for Didactic Reasons

20 Control Charts for Attributes

20.1 File ‘MOTORS’

20.2 Plotting the Proportion of Defective Units (P)

20.3 File ‘CATHETER’

20.4 Plotting the Number of Defective Units (NP)

20.5 Plotting the Number of Defects per Constant Unit of Measurement (C)

20.6 File ‘FABRIC’

20.7 Plotting the Number of Defects per Variable Unit of Measurement (U)

21 Part Four Case Studies – Multi-Vari Charts and Statistical Process Control

21.1 Bottles

21.2 Mattresses (1st Part)

21.3 Mattresses (2nd Part)

21.4 Plastic (1st Part)

21.5 Plastic (2nd Part)

Part Five Regression and Multivariate Analysis

22 Correlation and Simple Regression

22.1 Correlation Coefficient

22.2 Simple Regression

22.3 Simple Regression with ‘Fitted Line Plot’

22.4 Simple Regression with ‘Regression’

23 Multiple Regression

23.1 File ‘CARS2’

23.3 Exploratory Analysis

23.3 Multiple Regression

23.4 Option Buttons

23.5 Selection of the Best Equation: Best Subsets

23.6 Selection of the Best Equation: Stepwise

24 Multivariate Analysis

24.1 File ‘LATIN_AMERICA’

24.2 Principal Components

24.3 Cluster Analysis for Observations

24.4 Cluster Analysis for Variables

24.5 Discriminant Analysis

25 Part Five Case Studies – Regression and Multivariate Analysis

25.1 Tree

25.2 Power Plant

25.3 Wear

25.4 TV Failure

Part Six Experimental Design and Reliability

26 Selection of the Experimental Plan Using a Factorial Design

26.1 Creation of the Design Matrix

26.2 Definition of a Design Matrix when the Data is Previously Entered in the Worksheet

27 Analysis and Interpretation of the Results of a Factorial Design

27.1 Calculating the Effects and Determining the Significant Ones

28 Response Surface Methodology

28.1 Matrix Design Creation and Data Collection

28.2 Analysis of the Results

28.3 Contour Plots and Response Surface Plots

29 Reliability

29.1 File ‘INJECTION’

29.2 Nonparametric Analysis

29.3 Identification of the Best Model for the Data

29.4 Parametric Analysis

29.5 General Graphical Display of Reliability Data

30 Part Six Case Studies – Design of Experiments and Reliability

30.1 Cardigan

30.2 Steering Wheel – 1

30.3 Steering Wheel – 2

30.4 Paper Helicopters

30.5 Microorganisms

30.6 Jam

30.7 Photocopies

Appendix 1 Answers to Questions that Arise at the Beginning

Appendix 2 Managing Data

A2.1 Copy Columns with Restrictions (File: ‘PULSE’)

A2.2 Selection of Data when Plotting a Graph

A2.3 Stacking and Unstacking of Columns (File ‘BREAD’)

A2.4 Coding and Sorting Data

Appendix 3 Customization of MINITAB

A3.1 Configuration Options

A3.2 Use of Toolbars

A3.3 Add Elements to an Existing Toolbar

A3.4 Create Custom Toolbars

Index


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Industrial Statistics with Minitab, <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 th, Industrial Statistics with Minitab

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Industrial Statistics with Minitab, <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 th, Industrial Statistics with Minitab

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Industrial Statistics with Minitab, <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 th, Industrial Statistics with Minitab

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