Applied Data Analysis For Process Improvement: A Practical Guide To Six Sigma Black Belt Statistics
James L Lamprecht, "Applied Data Analysis For Process Improvement: A Practical Guide To Six Sigma Black Belt Statistics"
2005 | ISBN-10: 0873896483 | 304 pages | PDF | 2 MB
With the rise of Six Sigma, the use of statistics to analyze and improve processes has once again regained a prominent place in businesses around the world. An increasing number of employees and managers, bestowed with the titles Green Belt, Black Belt, or even Master Black Belts, are asked to apply statistical techniques to analyze and resolve industrial and non-industrial (also known as transactional) problems. These individuals are continuously faced with the daunting task of sorting out the vast array of sophisticated techniques placed at their disposal by an equally impressive array of statistical computer software packages.
This book is intended for the ever-growing number of certified Black Belts as well as uncertified others that would like to understand how data can be analyzed. Many courses, including Six Sigma Black Belt courses, do a good job introducing participants to a vast array of sophisticated statistical techniques in as little as ten days, leaning heavily on statistical software packages. Although it is true that one can simplify statistical principles, learning how to interpret results produced by any statistical software requires the understanding of statistics that this book concisely provides.
Numerous examples illustrate how various techniques are applied.
Each example is reviewed from the perspective of what was not said in the example; in other words, the very information you will be faced with when you conduct your own analysis.
Titles of some sections in the book include the word optional or advanced. These sections cover more advanced but nonetheless useful topics, but skipping these sections will not affect the overall flow of the various subjects presented.
Buy Premium From My Links To Support Me & Download with MaX SPeeD!