Spc for the Rest of Us: A Personal Path to Statistical Process Control .This work explains the mysteries and intricacies of statistical process control, the set of tools that are a vital part of total quality management. It shows how to summarize data, sample, use graphi
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| Title | : | Spc for the Rest of Us: A Personal Path to Statistical Process Control |
| Author | : | |
| Rating | : | 4.81 (128 Votes) |
| Asin | : | 0201563665 |
| Format Type | : | Hardcover |
| Number of Pages | : | 448 Pages |
| Publish Date | : | 0000-00-00 |
| Genre | : |
This work explains the mysteries and intricacies of statistical process control, the set of tools that are a vital part of total quality management. It shows how to summarize data, sample, use graphics, evaluate a process statistically, predict important process characteristics, use calculators and computers in an SPC programme, and speak statistics with meaning and without jargon.
Editorial : Hy Pitt combines extensive technical expertise with an interactive hands-on style that makes SPC easy to understand. -- Debra Owens, VP, Corporate Quality Management, Baxter Healthcare Corp.
Hy Pitt is the best SPC teacher, period. Hy Pitt loves SPC and it shows. -- Raymond Wachniak, Corporate Director Quality (Retired) Bridgestone/Firestone, Inc.
It is one of the best written and easy to understand books on statistics ever published. -- Lawrence K. Meyers, Nationally Recognized Design of Experiments Expert, Principal, K. W. Tunnell Company, Inc.
Its flexible, easy-to-follow format is a no-brainer for almost anyone's lifestyle and it's filled with dazzlingly delectable recipes for both the 5 and 2-day diet intervals.. I couldn't ask for more from this book and am hoping that there will be more to come. Nothing new. A fantastic book and a real insight into what was really happening at the motor manufacturer in Ireland. Several important concepts are introduced in intuitive ways including pattern recognition and machine learning, prediction error, cross-validation and bootstrap, and overfitting of models.
Chapter is again aimed at the practical by emphaiszing data structure and data bases and by introducing data quality issues including data inconsistencies, outlying observations (which becomes more complicated in multivariate analysis as many directions in a multivariate space can be considered extreme), missing data, and common to today's research data containing many variables but only a few observations such as gen
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