Six Sigma quality improvement with Minitab
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...
| Autor principal: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Hoboken, N.J. :
John Wiley & Sons
2011.
|
| Edición: | 2nd ed |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009628678306719 |
Tabla de Contenidos:
- Six Sigma Quality Improvement with Minitab; Contents; 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 data2.2.1 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 detection3.1.3 Boxplots; 3.1.4 Brushing; 3.2 Display and summary of bivariate andmultivariate 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 distribution4.2.3 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 charts5.1.3 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
