Measurement Analysis: An Introduction To The Statistical Analysis Of Laboratory Data In Physics, Chemistry And The Life Sciences
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Accuracy Tests
Analysis of Variance
Author_Mario Caria
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Errors
Estimates
Experiments
Measurements
Probability
Processing of Data and Uncertainties
Product details
- ISBN 9781860942310
- Publication Date: 01 Nov 2000
- Publisher: Imperial College Press
- Publication City/Country: GB
- Product Form: Hardback
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This book deals with the statistical treatment of experimental data. It is also meant for those who are entirely new to the field of statistics and probability calculus, and those who wish to obtain rigorous estimates of the uncertainties associated with the experimental results of any discipline, such as meteorology, engineering, physics, chemistry and the life sciences. To understand the text, only a basic understanding of differential calculus is required.As an innovative teaching approach, simple laboratory class experiments are used as the basis for developing a detailed statistical analysis. This is done by directly using the students' logbooks without re-elaboration. The approach is profitable and can be easily pursued by the layman.People have, in the past, been confused by the many statistical definitions, formulae and assumptions. This book tries to avoid any arbitrary definition by using the recently introduced ISO directives.All the concepts, parameters and test variables for the modern treatment of experimental data are included. Among them are the error, uncertainty and its estimate, the distribution functions and the associated parameters. Every concept is associated with a simple experimental situation and the data analysis is performed in numerical detail. For completeness, the correlation of uncertainties with the error matrix is dealt with in greater detail. All the tests of hypotheses are presented. They are introduced from simple arguments and developed up to the analytical details. The applications of the tests to the fitting of experimental curves of the χ2, t and F tests, as well as the one most often used in the life sciences, the ANOVA, are shown.
