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 experimetal 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' to 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;
- hypotheses testing, and
- the application of scientific criteria.
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.
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