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273 pages

English

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Description

This book is a convenient and comprehensive guide to statistics. A resource for quality technicians and engineers in any industry, this second edition provides even more equations and examples for the reader—with a continued focus on algebra-based math. Those preparing for ASQ certification examinations, such as the Certified Quality Technician (CQT), Certified Six Sigma Green Belt (CSSGB), Certified Quality Engineer (CQE), Certified Six Sigma Black Belt (CSSBB), Certified Reliability Engineer (CRE), and Certified Supplier Quality Professional (CSQP), will find this book helpful as well.
Inside you’ll find:
• Complete calculations for determining confidence intervals, tolerances, sample size, outliers, process capability, and system reliability
• Newly added equations for hypothesis tests (such as the Kruskal-Wallis test and Levene’s test for equality of variances), the Taguchi method, and Weibull and log-normal distributions
• Hundreds of completed examples to demonstrate practical use of each equation
• 20+ appendices, including distribution tables, critical values tables, control charts, sampling plans, and a beta table

Sujets

Sciences & Techniques

Statistiques

Sciences appliquées

Ingénierie

Sciences appliquées

Ingénierie

Mathematics

Probability

Statistics

Sciences et techniques

Probabilités et statistiques

Informations

Publié par	ASQ Quality Press
Date de parution	31 mars 2022
Nombre de lectures	0
EAN13	9781636940168
Langue	English

Informations légales : prix de location à la page 0,3000€. Cette information est donnée uniquement à titre indicatif conformément à la législation en vigueur.

Extrait

Practical Engineering, Process, and Reliability Statistics
Second Edition
Mark Allen Durivage

Milwaukee, Wisconsin

American Society for Quality, Quality Press, Milwaukee, WI 53203 © 2015, 2022 by ASQ
All rights reserved. Published 2015, 2022. Printed in the United States of America .
26 25 24 23 22 EA 5 4 3 2 1
Library of Congress Cataloging-in-Publication data
Names: Durivage, Mark Allen, author.
Title: Practical engineering, process, and reliability statistics, second edition / By Mark Allen Durivage.
Description: Includes bibliographical references and index. | Milwaukee, WI: ASQ Quality Press, 2022.
Identifiers: LCCN: 2022933438 | ISBN 978-1-63694-015-1 (paperback) | 978-1-63694-016-8 (epub)
Subjects: LCSH Engineering—Statistical methods. | Reliability (Engineering)—Statistical methods. | BISAC MATHEMATICS / Probability & Statistics / General | TECHNOLOGY & ENGINEERING / Engineering / General
Classification: LCC TA340 .D87 2022 | DDC 620/.0045—dc23
No part of this book may be reproduced in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher.
ASQ advances individual, organizational, and community excellence worldwide through learning, quality improvement, and knowledge exchange.
Bookstores, wholesalers, schools, libraries, and organizations: Quality Press books are available at quantity discounts for bulk purchases for business, trade, or educational uses. For more information, please contact Quality Press at 800-248-1946 or books@asq.org.
To place orders or browse the selection of all Quality Press titles, visit our website at: http://www.asq.org/quality-press.

Table of Contents
H1595_Cover
H1595_TitlePg
H1595_CIP
H1595_Preface
H1595_Acknowledgements
01_H1595_Chapter1
02_H1595_Chapter2
03_H1595_Chapter3
04_H1595_Chapter4
05_H1595_Chapter5
06_H1595_Chapter6
07_H1595_Chapter7
08_H1595_Chapter8
09_H1595_Chapter9
10_H1595_Chapter10
11_H1595_Chapter11
12_H1595_Chapter12
13_H1595_Chapter13
14_H1595_Chapter14
15_H1595_Chapter15
16_H1595_Chapter16
17_H1595_Chapter17
18_H1595_Chapter18
19_H1595_Chapter19
20_H1595_Chapter20
21_H1595_Chapter21
22_H1595_Chapter22
23_H1595_Chapter23
24_H1595_Chapter24
25_H1595_Chapter25
26_H1595_Chapter26
27_H1595_Chapter27
28_H1595_Chapter28
29_H1595_Chapter29
30_H1595_Appendices
31_H1595_Acronyms
32_H1595_Definitions
33_H1595_References
34_H1595_Bibliography
H1595_AboutAuthor

Preface
P ractical Engineering, Process, and Reliability Statistics was written to aid quality technicians and engineers. Drawing on 30 years of quality-related work experience, my goal is to provide the quality professional working in virtually any industry a quick, convenient, and comprehensive guide to properly using statistics in an efficient and effective manner.
This second edition includes many practical and useful additions. I’ve included hypothesis tests such as the Kruskal-Wallis test and Levene’s test for equality of variances, expanded system reliability functions, and explained applications of the Weibull and log-normal distributions and Taguchi signal-to-noise ratios. I’ve also included new reference tables, such as the beta table, gamma distribution function table, and table of selected median rank percentages. A comprehensive list of acronyms, terms, and definitions related to statistics, quality control, and reliability engineering rounds out the content.
This book is a useful reference when preparing for and taking many of ASQ’s certification examinations. You will find it helpful when pursuing the Certified Quality Technician (CQT), Certified Six Sigma Green Belt (CSSGB), Certified Quality Engineer (CQE), Certified Six Sigma Black Belt (CSSBB), Certified Reliability Engineer (CRE), and Certified Supplier Quality Professional (CSQP) certifications. I assume readers have a working knowledge of basic statistical concepts; all examples use algebra-based problem-solving techniques rather than calculus to keep the math as straightforward as possible.

Acknowledgments
I would like to acknowledge the previous work of Robert A. Dovich in Quality Engineering Statistics and Reliability Statistics . This book is an expansion of his efforts in an attempt to continue the method of presenting statistical applications in a simple, easy-to-follow format. Several sections of this book come directly from his previous work. I have made some changes to clarify and augment some of his points and present the topics in a consistent manner. I would like to thank those who have inspired, taught, and trained me throughout my academic and professional career. Lastly, I would like to acknowledge the patience of my wife, Dawn, and my sons, Jack and Sam, which allowed me time to research, write, and update Practical Engineering, Process, and Reliability Statistics, second edition.

Limit of Liability/ Disclaimer of Warranty
T he author has put forth his best efforts in compiling the content of this book; however, no warranty with respect to the material’s accuracy or completeness is made. Additionally, no warranty is made in regards to applying the recommendations made in this book to any business structure or environments. Businesses should consult regulatory, quality, and/or legal professionals prior to deciding on the appropriateness of advice and recommendations made within this book. The author shall not be held liable for loss of profit or other commercial damages resulting from the employment of recommendations made within this book including special, incidental, consequential, or other damages.

1
Point Estimates and Measures of Dispersion
W hen performing statistical tests, we usually work with data that are samples drawn from a population. We use sample data to make estimates about the population. The first estimate is usually a point estimate (central tendency).
As we will see in Chapter 2, “Confidence Intervals,” these point estimates are subject to sampling error and should be interpreted with caution, especially for small sample sizes. The accuracy of the point estimates becomes greater as the sample size gets larger.
There are several point estimates commonly made by quality technicians, quality engineers, and reliability engineers. These include estimates of central tendency, such as the mean (average), median, and mode.
Estimates of dispersion include the range, variance, standard deviation, coefficient of variation, and others. Descriptions of the shape of the distribution include skewness and kurtosis.
Estimates of Central Tendency for Variables Data
The most common measure of central tendency is the average or sample mean . The true (unknown) population mean is denoted by the letter μ and is estimated by X -bar ( X ). To estimate the parameter μ using X -bar, w e use the following formula:

where
X = Data point
n = Number of data points
Example: Using the following seven data points, estimate the population mean μ by finding X -bar:
43, 45, 40, 39, 42, 44, 41

Another estimate of central tendency or location is the median . The median is a simpler value to determine because it can be determined without a mathematical calculation. The median value is most useful when there are outlying data points that could artificially inflate or deflate the arithmetic mean. To find the median, place the data points in an ordered form, generally the lowest value on the left and the greatest value on the right.
Example: Using the following seven data points, determine the median:
43, 45, 40, 39, 42, 44, 41
Order the data points and select the point in the middle:
39, 40, 41, 42 , 43, 44, 45
This example yields 42 as the median value for these seven data points. In the case where there is an even number of data points, add the two values in the middle and divide by two.
Example: Using the following six data points, determine the median:
43, 40, 39, 42, 44, 41
Order the data points and select the points in the middle, and divide by two:
39, 40, 41 , 42 , 43, 44

The calculated median value is 41.5.
The mode is the most frequently occurring value(s) in a set of data. A set of data may contain one mode, two modes (bimodal), many modes, or no mode.
Example: Given the following data points, determine the mode:
39, 40, 41 , 41 , 42, 43
The mode is 41, as it is the most frequently appearing value in the data set.
When the population set is unimodal and symmetrical, such as in the normal (Gaussian) distribution, the values of mean, median, and mode will occur at the same location, as shown in Figure 1.1. When the distribution is skewed, these values diverge, as shown in Figures 1.2 and 1.3.

Range for Variables Data
The range is the simplest method of measuring the spread of sample or population data. To calculate the range, we use the following formula:
r = X h – X l
where
r = Range
X h = Largest value
X l = Smallest value
Example: Using the following seven data points, determine the range:
43, 45, 40, 39, 42, 44, 41
Order the data points and select the largest and smallest values:
39 , 40, 41, 42, 43, 44, 45
r = X h – X l = 45 – 39 = 6
The range of this set of data is 6.
Variance and Standard Deviation for Variables Data
This section will focus on the two primary measures of dispersion or variation for individual values. The two principal measures of dispersion are the variance σ 2 , which is estimated from sample data, and the statistic s 2 , which is estimated by the statistic s . We see that the standard deviation is the square root of the variance. For vari