000			a
999			_c31936 _d31936
008			230417b xxu||||| |||| 00| 0 eng d
020			_a9780367241704
082			_a001.434028553 _bSHA
100			_aShaw, Benjamin D.
245			_aUncertainty analysis of experimental data with R
260			_bCRC Press, _a2017 _cBoca Raton :
300			_aix, 195 p. ; _bill., _c24 cm
365			_b2495.00 _cINR _d01
504			_aIncludes bibliographical references and index.
520			_aThis would be an excellent book for undergraduate, graduate and beyond ... The style of writing is easy to read and the author does a good job of adding humor in places. The integration of basic programming in R with the data that is collected for any experiment provides a powerful platform for analysis of data ... having the understanding of data analysis that this book offers will really help researchers examine their data and consider its value from multiple perspectives - and this applies to people who have small AND large data sets alike! This book also helps people use a free and basic software system for processing and plotting simple to complex functions." Michelle Pantoya, Texas Tech UniversityMeasurements of quantities that vary in a continuous fashion, e.g., the pressure of a gas, cannot be measured exactly and there will always be some uncertainty with these measured values, so it is vital for researchers to be able to quantify this data. Uncertainty Analysis of Experimental Data with R covers methods for evaluation of uncertainties in experimental data, as well as predictions made using these data, with implementation in R. The books discusses both basic and more complex methods including linear regression, nonlinear regression, and kernel smoothing curve fits, as well as Taylor Series, Monte Carlo and Bayesian approaches. Features:1. Extensive use of modern open source software (R).2. Many code examples are provided.3. The uncertainty analyses conform to accepted professional standards (ASME).4. The book is self-contained and includes all necessary material including chapters on statistics and programming in R. Benjamin D. Shaw is a professor in the Mechanical and Aerospace Engineering Department at the University of California, Davis. His research interests are primarily in experimental and theoretical aspects of combustion. Along with other courses, he has taught undergraduate and graduate courses on engineering experimentation and uncertainty analysis. He has published widely in archival journals and became an ASME Fellow in 2003.?
650			_aAccept/reject method
650			_a Bayesian approach
650			_aConfidence interval
650			_aCurve fitting technique
650			_aData visualization
650			_a Elemental sysmatic error
650			_aGeneral linear regression theory
650			_aKernel smoothing methods
650			_aLinear regression
650			_aMonte Carlo(MC) methods
650			_aNonparametric boot-strapping
650			_aProbability density function
650			_aTaylor series approach
942			_2ddc _cBK