G. Robin Henderson

Six Sigma Quality Improvement with Minitab (eBook, PDF)

• Format: PDF

Produktbeschreibung

This book aims to enable readers to understand and implement, via the widely used statistical software package Minitab (Release 16), statistical methods fundamental to the Six Sigma approach to the continuous improvement of products, processes and services. The second edition includes the following new material: * Pareto charts and Cause-and-Effect diagrams * Time-weighted control charts cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) * Multivariate control charts * Acceptance sampling by attributes and variables (not provided in Release 14) * Tests of association using the chi-square distribution * Logistic regression * Taguchi experimental designs

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 528
• Erscheinungstermin: 9. Juni 2011
• Englisch
• ISBN-13: 9781119975335
• Artikelnr.: 37340424

Autorenporträt

G. Robin Henderson, Statistical Consultant, Halcro Consultancy.

Inhaltsangabe

Foreword. Preface. Acknowledgements. About the Author. 1 Introduction. 1.1
Quality and Quality Improvement. 1.2 Six Sigma Quality Improvement. 1.3 The
Six Sigma Roadmap and DMAIC. 1.4 The Role of Statistical Methods in Six
Sigma. 1.5 Minitab and its Role in the Implementation of Statistical
Methods. 1.6 Exercises and Follow-Up Activities. 2 Data Display, Summary
and Manipulation. 2.1 The Run Chart - a First Minitab Session. 2.1.1 Input
of Data Via Keyboard and Creation of a Run Chart in Minitab. 2.1.2 Minitab
Projects and Their Components. 2.2 Display and Summary of Univariate Data.
2.2.1 Histogram and Distribution. 2.2.2 Shape of a Distribution. 2.2.3
Location. 2.2.4 Variability. 2.3 Data Input, Output, Manipulation and
Management. 2.3.1 Data Input and Output. 2.3.2 Stacking and Unstacking of
Data; Changing Data Type and Coding. 2.3.3 Case Study Demonstrating
Ranking, Sorting and Extraction of Information from Date/Time Data. 2.4
Exercises and Follow-Up Activities. 3 Exploratory Data Analysis, Display
and Summary of Multivariate Data. 3.1 Exploratory Data Analysis. 3.1.1
Stem-and-Leaf Displays. 3.1.2 Outliers and Outlier Detection. 3.1.3
Boxplots. 3.1.4 Brushing. 3.2 Display and Summary of Bivariate and
Multivariate Data. 3.2.1 Bivariate Data - Scatterplots and Marginal Plots.
3.2.2 Covariance and Correlation. 3.2.3 Multivariate Data - Matrix Plots.
3.2.4 Multi-Vari Charts. 3.3 Other Displays. 3.3.1 Pareto Charts. 3.3.2
Cause-and-Effect Diagrams. 3.4 Exercises and Follow-Up Activities. 4
Statistical Models. 4.1 Fundamentals of Probability. 4.1.1 Concept and
Notation. 4.1.2 Rules for Probabilities. 4.2 Probability Distributions for
Counts and Measurements. 4.2.1 Binomial Distribution. 4.2.2 Poisson
Distribution. 4.2.3 Normal (Gaussian) Distribution. 4.3 Distribution of
Means and Proportions. 4.3.1 Two Preliminary Results. 4.3.2 Distribution of
the Sample Mean. 4.3.3 Distribution of the Sample Proportion. 4.4
Multivariate Normal Distribution. 4.5 Statistical Models Applied to
Acceptance Sampling. 4.5.1 Acceptance Sampling by Attributes. 4.5.2
Acceptance Sampling by Variables. 4.6 Exercises and Follow-Up Activities. 5
Control Charts. 5.1 Shewhart Charts for Measurement Data. 5.1.1 I and MR
Charts for Individual Measurements. 5.1.2 Tests for Evidence of Special
Cause Variation on Shewhart Charts. 5.1.3 Xbar and R Charts for Samples
(Subgroups) of Measurements. 5.2 Shewhart Charts for Attribute Data. 5.2.1
P Chart for Proportion Nonconforming. 5.2.2 NP Chart for Number
Nonconforming. 5.2.3 C Chart for Count of Nonconformities. 5.2.4 U Chart
for Nonconformities Per Unit. 5.2.5 Funnel Plots. 5.3 Time-Weighted Control
Charts. 5.3.1 Moving Averages and their Applications. 5.3.2 Exponentially
Weighted Moving Average Control Charts. 5.3.3 Cumulative Sum Control
Charts. 5.4 Process Adjustment. 5.4.1 Process Tampering. 5.4.2
Autocorrelated Data and Process Feedback Adjustment. 5.5 Multivariate
Control Charts. 5.6 Exercises and Follow-Up Activities. 6 Process
Capability Analysis. 6.1 Process Capability. 6.1.1 Process Capability
Analysis with Measurement Data. 6.1.2 Process Capability Indices and Sigma
Quality Levels. 6.1.3 Process Capability Analysis with Nonnormal Data.
6.1.4 Tolerance Intervals. 6.1.5 Process Capability Analysis with Attribute
Data. 6.2 Exercises and Follow-Up Activities. 7 Process Experimentation
with a Single Factor. 7.1 Fundamentals of Hypothesis Testing. 7.2 Tests and
Confidence Intervals for the Comparison of Means and Proportions with a
Standard. 7.2.1 Tests Based on the Standard Normal Distribution - z-Tests.
7.2.2 Tests Based on the Student t-Distribution - t-Tests. 7.2.3 Tests for
Proportions. 7.2.4 Nonparametric Sign and Wilcoxon Tests. 7.3 Tests and
Confidence Intervals for the Comparison of Two Means or Two Proportions.
7.3.1 Two-Sample t-Tests. 7.3.2 Tests for Two Proportions. 7.3.3
Nonparametric Mann-Whitney Test. 7.4 The Analysis of Paired Data - t-Tests
and Sign Tests. 7.5 Experiments with a Single Factor Having More Than Two
Levels. 7.5.1 Design and Analysis of a Single-Factor Experiment. 7.5.2 The
Fixed Effects Model. 7.5.3 The Random Effects Model. 7.5.4 The
Nonparametric Kruskal-Wallis Test. 7.6 Blocking in Single-Factor
Experiments. 7.7 Experiments with a Single Factor, with More Than Two
Levels, where the Response is a Proportion. 7.8 Tests for Equality of
Variances. 7.9 Exercises and Follow-Up Activities. 8 Process
Experimentation with Two or More Factors. 8.1 General Factorial
Experiments. 8.1.1 Creation of a General Factorial Experimental Design.
8.1.2 Display and Analysis of Data from a General Factorial Experiment.
8.1.3 The Fixed Effects Model, Comparisons. 8.1.4 The Random Effects Model,
Components of Variance. 8.2 Full Factorial Experiments in the 2^k Series.
8.2.1 2² Factorial Experimental Designs, Display and Analysis of Data.
8.2.2 Models and Associated Displays. 8.2.3 Examples of 2³ and 2^4
Experiments, the Use of Pareto and Normal Probability Plots of Effects. 8.3
Fractional Factorial Experiments in the 2^k-p Series. 8.3.1 Introduction to
Fractional Factorial Experiments, Confounding and Resolution. 8.3.2 Case
Study Examples. 8.4 Taguchi Experimental Designs. 8.5 Exercises and
Follow-Up Activities. 9 Evaluation of Measurement Processes. 9.1
Measurement Process Concepts. 9.1.1 Bias, Linearity, Repeatability and
Reproducibility. 9.1.2 Inadequate Measurement Units. 9.2 Gauge
Repeatability and Reproducibility Studies. 9.3 Comparison of Measurement
Systems. 9.4 Attribute Scenarios. 9.5 Exercises and Follow-Up Activities.
10 Regression and Model Building. 10.1 Regression with a Single Predictor
Variable. 10.2 Multiple Regression. 10.3 Response Surface Methods. 10.4
Categorical Data and Logistic Regression. 10.4.1 Tests of Association Using
the Chi-Square Distribution. 10.4.2 Binary Logistic Regression. 10.5
Exercises and Follow-Up Activities. 11 Learning More and Further Minitab.
11.1 Learning More about Minitab and Obtaining Help. 11.1.1 Meet Minitab.
11.1.2 Help. 11.1.3 StatGuide. 11.1.4 Tutorials. 11.1.5 Assistant. 11.1.6
Glossary, Methods and Formulas. 11.1.7 Minitab on the Web and
Knowledgebase/FAQ. 11.2 Macros. 11.2.1 Minitab Session Commands. 11.2.2
Global and Local Minitab Macros. 11.3 Further Features of Minitab. 11.4
Quality Companion. 11.5 Postscript. Appendix 1. Appendix 2. Appendix 3.
Appendix 4. References. Index.