Measurement Uncertainties in Science and Technology

Michael Grabe

ISBN 10: 3642058957 / ISBN 13: 9783642058950

Published by Springer Berlin Heidelberg, 2010

About this Item

Druck auf Anfrage Neuware - Printed after ordering - At the turn of the 19th century, Carl Friedrich Gauß founded error calculus by predicting the then unknown position of the planet Ceres. Ever since, error calculus has occupied a place at the heart of science. In this book, Grabe illustrates the breakdown of traditional error calculus in the face of modern measurement techniques. Revising Gauß' error calculus ab initio, he treats random and unknown systematic errors on an equal footing from the outset. Furthermore, Grabe also proposes what may be called well defined measuring conditions, a prerequisite for defining confidence intervals that are consistent with basic statistical concepts. The resulting measurement uncertainties are as robust and reliable as required by modern-day science, engineering and technology.

Seller Inventory # 9783642058950

Bibliographic Details

• Title: Measurement Uncertainties in Science and Technology
• Publisher: Springer Berlin Heidelberg
• Publication Date: 2010
• Language: English
• ISBN 10: 3642058957
• ISBN 13: 9783642058950
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 16D millimeters 235 millimeters width by 155 millimeters height by 16 millimeters depth
• Weight: 435 grams

About this title

Synopsis

At the turn of the 19^th century, Carl Friedrich Gauß founded error calculus by predicting the then unknown position of the planet Ceres. Ever since, error calculus has occupied a place at the heart of science. In this book, Grabe illustrates the breakdown of traditional error calculus in the face of modern measurement techniques. Revising Gauß’ error calculus ab initio, he treats random and unknown systematic errors on an equal footing from the outset. Furthermore, Grabe also proposes what may be called well defined measuring conditions, a prerequisite for defining confidence intervals that are consistent with basic statistical concepts. The resulting measurement uncertainties are as robust and reliable as required by modern-day science, engineering and technology.