[Download] RSM Simplified: Optimizing Processes Using Response Surface Methods for Design of Experiments, Second Edition de Mark J. Anderson Libros Gratis en EPUB, RSM Simplified: Optimizing Processes Using Response Surface Methods for Design of Experiments, Second Edition Libro pdf espanol
Descripción - Reseña del editor Anderson and Whitcomb pick up where they left off in DOE Simplified with RSM Simplified -- a practical tool for design of experiments that anyone with a minimum of technical training can understand and appreciate. Their approach is simple and fun for those who desire knowledge on response surface methods but are put off by the academic nature of other books on the topic. RSM Simplified keeps formulas to a minimum and makes liberal use of figures, charts, graphs, and checklists. It offers many relevant examples with amusing sidebars and do-it-yourself exercises that will lead readers to the peak potential for their product quality and process efficiency. BiografÃa del autor Mark Anderson is a principal and general manager of Stat-Ease, Inc. Prior to joining the firm, he spearheaded an award-winning quality improvement program for an international manufacturer, generating millions of dollars in profit. Mark offers a diverse array of experience in process development, quality assurance, marketing, purchasing, and general management. Mark is also co-author of: DOE Simplified: Practical Tools for Effective Experimentation and RSM Simplified: Optimizing Processes Using Response Surface Methods for Design of Experiments, and has published numerous articles on design of experiments (DOE). He is also a guest lecturer at the University of MN Chemical Engineering & Materials Science department and the Ohio State University Fisher College of Business.
1 / Design Of Experiments / Statistics ~ Response surface methods (RSM) are powerful optimization tools in the arsenal of statistical design of experiments (DOE). Before employing RSM, process engineers should take full advantage of a far simpler tool for DOE -- two-level factorials, which can be very effective for screening the vital few factors (including interactions) from the trivial many that have no significant impact.
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