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.
- 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
- 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
- Fundamentals of probability.
- Probability distributions for counts and measurements.
- Distribution of means and proportions
- Shewhart charts for measurement data.
- Shewhart charts for attribute data.
- Process adjustment
Process capability analysis
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.
- Further MINITAB.