Statistics Innovation Statistical Design (12 results)
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Paperback. Condition: New Books. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will b…e requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
Language: English
Published by Springer, 2008
- Softcover
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Add to basketSoft cover. Condition: New. ISBN:9788847008144.
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 4. 1. 1 ImportanceofComputerSimulation The importance of experimenting for quality improvement and innovation of pr- ucts and processes is now very well known: 'experimenting' means to implement signi cant and intentional changes with the aim of ob…taining useful information. In particular, the majority of industrial experiments have two goals: - To quantify the dependence of one or more observable response variables on a group of input factors in the design or the manufacturing of a product, in order to forecast the behavior of the system in a reliable way. - To identify the level settings for the inputs (design parameters) that are capable of optimizing the response. The set of rules that govern experiments for technological improvement in a ph- ical set-up are now comprehensively labeled 'DoE. ' In recent years, the use of - perimentation in engineering design has received renewed momentum through the utilization of computer experiments (see Sacks et al. 1989, Santner et al. 2003), which has been steadily growing in the last two decades. These experimentsare run on a computer code implementing a simulation model of a physical system of int- est. This enables us to explore the complex relationships between input and output variables. Themain advantageofthis is that thesystem becomesmore'observable,' since computer runs are generally easier and cheaper than measurements taken in a physical set-up, and the exploration can be carried out more thoroughly. This is particularly attractive in industrial design applications where the goal is system - timization. 4. 1.
- Softcover
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Taschenbuch. Condition: Neu. Statistics for Innovation | Statistical Design of "Continuous" Product Innovation | Pasquale Erto | Taschenbuch | xvi | Englisch | 2008 | Springer | EAN 9788847008144 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]c…om | Anbieter: preigu.
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Condition: Sehr gut. Zustand: Sehr gut | Seiten: 284 | Sprache: Englisch | Produktart: Bücher | 4. 1. 1 ImportanceofComputerSimulation The importance of experimenting for quality improvement and innovation of pr- ucts and processes is now very well known: ¿experimenting¿ means to implement signi?cant and intentional changes with… the aim of obtaining useful information. In particular, the majority of industrial experiments have two goals: ¿ To quantify the dependence of one or more observable response variables on a group of input factors in the design or the manufacturing of a product, in order to forecast the behavior of the system in a reliable way. ¿ To identify the level settings for the inputs (design parameters) that are capable of optimizing the response. The set of rules that govern experiments for technological improvement in a ph- ical set-up are now comprehensively labeled ¿DoE. ¿ In recent years, the use of - perimentation in engineering design has received renewed momentum through the utilization of computer experiments (see Sacks et al. 1989, Santner et al. 2003), which has been steadily growing in the last two decades. These experimentsare run on a computer code implementing a simulation model of a physical system of int- est. This enables us to explore the complex relationships between input and output variables. Themain advantageofthis is that thesystem becomesmore¿observable,¿ since computer runs are generally easier and cheaper than measurements taken in a physical set-up, and the exploration can be carried out more thoroughly. This is particularly attractive in industrial design applications where the goal is system - timization. 4. 1.
