Six Sigma Quality Improvement with MINITAB explains the most important statistical methods employed in Six Sigma and demonstrates their implementation via the very popular, and user-friendly, statistical software package MINITAB.

Features:

• Introduction to key statistical methods for Quality Improvement using MINITAB.
• Minimal prior knowledge of statistical methods and no prior knowledge of MINITAB assumed.
• Easy-to-follow guidance for Six Sigma Green and Black Belts and others involved in Quality Improvement.
• Provides informative follow-up exercises, from a wide variety of scenarios, on each topic.
• Employs random data generation in MINITAB to aid understanding of key statistical concepts.
• Supported by a Website featuring data sets for download and notes and answers for the follow-up exercises.
• Developed from the author’s wealth of experience gained from many years working both in education and consultancy

Contents

Introduction

• Quality and quality improvement.
• Six Sigma quality improvement.
• The Six Sigma roadmap and DMAIC.
• The role of statistical methods in Six Sigma.
• MINITAB and its role in the implementation of statistical methods

Introduction to MINITAB, data display, summary and manipulation

• The run chart – a first MINITAB session.
• Display and summary of univariate data.
• Data input, output, manipulation and management

Exploratory data analysis, display and summary of multivariate data

• Exploratory data analysis.
• Display and summary of bivariate and multivariate data

Statistical models

• Fundamentals of probability.
• Probability distributions for counts and measurements.
• Distribution of means and proportions

Control charts

• Shewhart charts for measurement data.
• Shewhart charts for attribute data.
• Process adjustment

Process capability analysis

• Process capability

Process experimentation with a single factor

• Fundamental concepts in hypothesis testing.
• Tests and confidence intervals for the comparison of means and of proportions with a standard.
• Tests and confidence intervals for the comparison of two means or two proportions.
• The analysis of paired data – t-tests and sign tests.
• Experiments with a single factor having more than two levels.
• Blocking in single–factor experiments.
• Experiments with a single factor, with more than two levels, where the response is a proportion.
• Tests for equality of variance

Process experimentation with two or more factors

• General factorial experiments.
• Full factorial experiments in the 2k series.
• Fractional factorial experiments in the 2k–p series

Evaluation of measurement processes

• Measurement process concepts.
• Gauge repeatability and reproducibility (R&R) studies.
• Attribute scenarios

Regression and model building

• Regression with a single predictor variable.
• Multiple regression.
• Response surface methods

More about MINITAB

• Learning more about MINITAB and obtaining help.
• Macros.
• Further MINITAB.
• Postscript.

Index

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