Synopsis:
Data Fitting in the Chemical Sciences Peter Gans, School of Chemistry, The University of Leeds, Leeds, UK Data fitting is a technique of central importance in modern experimental science. It is the means by which data is tested against a model of the experimental system, be it a theoretical or empirical model. In this book an all-round approach is adopted in which the first stage of data-fitting is seen as data collection, the second is numerical processing and the third a critical evaluation of the 'goodness' of fit in both statistical and common sense terms. Each stage is considered in detail: the sources and nature of experimental errors; the theory of least-squares fitting; probability theory; hypothesis testing, and the application of scientific criteria. The theory is complemented by three chapters on a wide range of applications. The emphasis of this book is on methodology: why certain procedures are preferred rather than how any one procedure is implemented. The author aims to assist people in extracting from their data its full information content, i.e. to use their data, not abuse it.
From the Publisher:
Details some of the procedures for the fitting of experimental data. The first section deals with general theory, followed by a critical examination of the validity of the results including an introduction to methods of statistical testing and a discussion of least-squares fitting--a powerful procedure used to reduce errors in experimental measurements. The second half looks at ways in which the theory is applied to typical data-fitting problems such as fitting with polynominals and the techniques of smoothing and differentiation. Features excellent coverage of spline, trigonometric, surface, Gaussian, Lorentzian and related functions. Also contains a wealth of diagrams to facilitate understanding.
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