Statistics for the quality control chemistry laboratory / Eamonn Mullins.

Statistical methods are essential tools for analysts, particularly those working in Quality Control Laboratories. This book provides a sound introduction to their use in analytical chemistry, without requiring a strong mathematical background. It emphasises simple graphical methods of data analysis,...

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Bibliographic Details
Main Author: Mullins, Eamonn
Format: eBook
Language:English
Published: Cambridge : Royal Society of Chemistry, 2003.
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Online Access:Click for online access

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520 |a Statistical methods are essential tools for analysts, particularly those working in Quality Control Laboratories. This book provides a sound introduction to their use in analytical chemistry, without requiring a strong mathematical background. It emphasises simple graphical methods of data analysis, such as control charts, which are a key tool in Internal Laboratory Quality Control and which are also a fundamental requirement in laboratory accreditation. A large part of the book is concerned with the design and analysis of laboratory experiments, including sample size determination. Practical case studies and many real datasets, from both QC laboratories and the research literature, are used to illustrate the ideas in action. The aim of Statistics for the Quality Control Chemistry Laboratory is to give the reader a strong grasp of the concept of statistical variation in laboratory data and of the value of simple statistical ideas and methods in thinking about and manipulating such data. It will be invaluable to analysts working in QC laboratories in industry, hospitals and public health, and will also be welcomed as a textbook for aspiring analysts in colleges and universities. 
500 |a Includes index. 
504 |a Includes bibliographical references and index. 
505 0 |a Variability in analytical measurements -- Control charts in the analytical laboratory -- Some important statistical ideas -- Simple comparative studies -- Studying complex systems -- Fitting equations to data -- The design and analysis of laboratory studies re-visited -- Assessing measurement quality. 
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