Statistical Methods for Quality Engineering

A Practical Guide for Quality Professionals
Erscheinungsjahr 2027
ISBN: 978-3-032-41444-1
Verlag: Springer

Buch, Englisch, Format (B × H): 155 mm x 235 mm

This book equips the quality engineer with tools to help ensure compliance and quality and is easily accessible to anyone with lower-division math. Most texts on statistics for quality assurance and control, contain only formulas with little explanation as to how those formulas came to be. Quality Engineers are often trained in some engineering discipline and then thrust into the world of quality. They know how to design a circuit, a mechanical device or a chemical process or maybe even a facility, but they are not even sure they know what it means to engineer quality.

This text goes beyond simply giving rules or formulas. It provides the engineer with some insight as to statistical methods they might encounter and are required to evaluate the appropriateness of those methods. This goal reaches beyond simply determining whether a product or process complies with specifications or regulatory documents.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Pardo, Scott A.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik Mathematik Stochastik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining

Inhaltsverzeichnis

Why Quality Matters.- Fundamentals of Probability and Statistical Inference.- Tests on Means: A Reminder.- Tests on Proportions: A Different Way with Binary Data.- Confidence.- Some Confidence Interval Computations.- Acceptance Sampling.- Equivalence and Non-Inferiority.- Capability: Fundamentals.- Bayesian Methods.- Linear Models: Regression.- Some Regression Variants.- Analysis of Variance (ANOVA) and Designed Experiments.- Factorial Experiments.- Taguchi and Off-Line Quality: Quadratic Loss.- Multivariate Quality.-Dynamic Systems: Box-Jenkins Approach.-Shewhart Control Charts.- Nonlinear Predictive Modeling: Machine Learning.- Nonlinear and Logistic Regression Models.- Model Selection and Model Building.- Reliability and Survival: Time to Failure.

Über Autor(innen)

Scott Pardo has been applying statistical methods to a wide variety of problems, from military communications networks to pacemaker longevity to pharmacokinetics to injection molding of syringes, for over 40 years. He has spent most of the last 32 years working in the medical device industries, including pacemakers, in-vitro diagnostics, and blood collection tubes. He has written several books on various statistical topics, including (Springer). Dr. Pardo has a Ph.D. in Industrial Engineering (Engineering Statistics) from the University of Southern California, and is a Six Sigma Master Black Belt. He is the author of , , , and