Applied Statistics for the Six Sigma Green Belt

Citation

Narasimhan, K. (2007), "Applied Statistics for the Six Sigma Green Belt", The TQM Magazine, Vol. 19 No. 3, pp. 283-284. https://doi.org/10.1108/09544780710745702

This book comprises 11 chapters, which are supported by 163 figures and 30 tables, and 6 appendices of useful statistical tables of binomial, Poisson and normal distributions and Critical values of Chi Squared, t and F for various degrees of freedom). The emphasis of the book is on understanding data collection, analysis and making decision based on the information generated.

Gupta and Walker are both professors at the University of Southern Maine, USA. The former has taught mathematics and statistical theory for over 30 years and authored a number of books and articles. The latter has also authored books and articles and his field is technology; and he has guided organizations to implement Six Sigma. He is an ASQ certified Quality Manager, Quality Engineer, and Quality Auditor – Biomedical.

In Chapter 1, readers are introduced to the context of Six Sigma (SS) by a brief explanation of SS both as a concept and a problem solving and process improvement approach. Chapter 2 is a very short chapter that introduces the topic of statistics. In particular, the concepts of samples and populations, and types of data, are briefly covered. The following two chapters deal respectively with describing data graphically and numerically (using measures of centrality and dispersion) to highlight the information contained in any data collected. The construction of Box‐Whisker plot and how to use it are also explained.

Chapters 5 to 8 deal in some depth the concept of probability and probability distributions of discrete and continuous random variables, and sampling distributions. Various distributions (for example, Binomial, Hyper‐geometric, Poisson, the Normal and Exponential, Chi‐Square, Student's t, and Snedecor's F) are defined and a number of worked examples are included to illustrate their application.

The next two chapters are devoted to explaining methods of statistical inference by estimating the unknown parameters of a population based on information contained in the sample selected (for both single and two populations) and commonly used tests of hypotheses.

The application of computer‐based tools such as MINITAB (Version 14), JMP (version 5.1), are briefly explained in the final chapter.

This applied statistics book is a useful to anyone that is interested in enhancing quality by improving the operational process.