Springer Handbook of Engineering Statistics

Springer Handbook of Engineering Statistics

 
Edition number: 2nd ed. 2023
Publisher: Springer
Date of Publication:
Number of Volumes: 1 pieces, Book
 
Normal price:

Publisher's listprice:
EUR 385.19
Estimated price in HUF:
158 948 HUF (151 379 HUF + 5% VAT)
Why estimated?
 
Your price:

146 232 (139 269 HUF + 5% VAT )
discount is: 8% (approx 12 716 HUF off)
The discount is only available for 'Alert of Favourite Topics' newsletter recipients.
Click here to subscribe.
 
Availability:

Estimated delivery time: Currently 3-5 weeks.
Not in stock at Prospero.
Can't you provide more accurate information?
 
  Piece(s)

 
 
 
 
Product details:

ISBN13:9781447175025
ISBN10:1447175026
Binding:Hardback
No. of pages:1150 pages
Size:279x210 mm
Weight:3439 g
Language:English
Illustrations: 400 Illustrations, color; 50 Tables, color
614
Category:
Short description:

This handbook gathers the full range of statistical techniques and tools required by engineers, analysts and scientists from all fields. The book is a comprehensive place to look for methods and solutions to practical problems within - but not limited to - data science, quality assurance in design and production engineering.  

The tools of engineering statistics are relevant for modeling and prediction of products, processes and services, but also for the analysis of ongoing processes, the reliability and life-cycle assessment of products and services, and finally to achieve realistic predictions on how to improve processes and products. 

This book contains contributions from around 115 leading experts in statistics, biostatistics, engineering statistics, reliability engineering, and related areas. It covers the various methods as well as their applications from industrial control to failure mechanism and analysis, medicine, business intelligence, electronic packaging, and risk management. It enables readers to choose the most appropriate method through its wide range of selection of statistical techniques and tools.

For the second edition all chapters have been thoroughly updated to reflect the current state-of-the-art. Included are also more than 30 completely new contriubutions revolving around current trends related to modern statistical computing, including: data science, big data, machine learning, optimization, data fusion, high dimensional data, voting systems, life testing, related statistical artificial intelligence (AI) and reliability physics and failure mode mechanisms.

This Springer Handbook of Engineering Statistics provides comprehensive literature with up-to-date statistical methodologies, algorithms, computation methods and tools that can be served as a main reference for researchers, engineers, business analysts, educators and students in all applied fields affected by statistics.



Long description:

In today?s global and highly competitive environment, continuous improvement in the processes and products of any field of engineering is essential for survival. This book gathers together the full range of statistical techniques required by engineers from all fields. It will assist them to gain sensible statistical feedback on how their processes or products are functioning and to give them realistic predictions of how these could be improved. The handbook will be essential reading for all engineers and engineering-connected managers who are serious about keeping their methods and products at the cutting edge of quality and competitiveness.



Engineers and practitioners contribute to society through their ability to apply basic scientific principles to real problems in an effective and efficient manner. They must collect data to test their products every day as part of the design and testing process and also after the product or process has been rolled out to monitor its effectiveness. Model building, data collection, data analysis and data interpretation form the core of sound engineering practice.


After the data has been gathered the engineer must be able to sift them and interpret them correctly so that meaning can be exposed from a mass of undifferentiated numbers or facts. To do this he or she must be familiar with the fundamental concepts of correlation, uncertainty, variability and risk in the face of uncertainty.


In today?s global and highly competitive environment, continuous improvement in the processes and products of any field of engineering is essential for survival. Many organisations have shown that the first step to continuous improvement is to integrate the widespread use of statistics and basic data analysis into the manufacturing development process as well as into the day-to-day business decisions taken in regard to engineering processes.


The Springer Handbook of Engineering Statistics gathers together the full range of statistical techniques required by engineers from all fields to gain sensible statistical feedback on how their processes or products are functioning and to give them realistic predictions of how these could be improved.


Featuring:



  • Contributions from leading experts in statistics and their application to engineering from industrial control to academic medicine and financial risk management giving all-round authoritative coverage.

  • Wide-ranging selection of statistical techniques showing the proper way to use each to enable the reader to choose the method most appropriate for his or her purposes.

  • Extensive and easy-to-use subject index making information quickly available to the reader.

The handbook will be essential reading for all engineers and engineering-connected managers who are serious about keeping their methods and products at the cutting edge of quality and competitiveness.



Engineers and practitioners contribute to society through their ability to apply basic scientific principles to real problems in an effective and efficient manner. They must collect data to test their products every day as part of the design and testing process and also after the product or process has been rolled out to monitor its effectiveness. Model building, data collection, data analysis and data interpretation form the core of sound engineering practice.


After the data has been gathered the engineer must be able to sift them and interpret them correctly so that meaning can be exposed from a mass of undifferentiated numbers or facts. To do this he or she must be familiar with the fundamental concepts of correlation, uncertainty, variability and risk in the face of uncertainty.


In today?s global and highly competitive environment, continuous improvement in the processes and products of any field of engineering is essential for survival. Many organisations have shown that the first step to continuous improvement is to integrate the widespread use of statistics and basic data analysis into the manufacturing development process as well as into the day-to-day business decisions taken in regard to engineering processes.


The Springer Handbook of Engineering Statistics gathers together the full range of statistical techniques required by engineers from all fields to gain sensible statistical feedback on how their processes or products are functioning and to give them realistic predictions of how these could be improved.


Featuring:



  • Contributions from leading experts in statistics and their application to engineering from industrial control to academic medicine and financial risk management giving all-round authoritative coverage.

  • Wide-ranging selection of statistical techniques showing the proper way to use each to enable the reader to choose the method most appropriate for his or her purposes.

  • Extensive and easy-to-use subject index making information quickly available to the reader.

The handbook will be essential reading for all engineers and engineering-connected managers who are serious about keeping their methods and products at the cutting edge of quality and competitiveness.

Table of Contents:
Part I: Fundamental Statistics and its Applications.- Part II: Process Monitoring and Improvement.- Part III: Reliability Models and Survival Analysis.- Part IV: Advanced Statistical Methods and Modeling.- Part V: Statistical Computing and Data Mining.- Part VI: Applications in Engineering Statistics.