Design and analysis in chemical research edited by Roy L. Tranter.
Series Sheffield analytical chemistry ; 3Detalles de publicación: Sheffield, England Sheffield Academic Press 2000.Descripción: xviii, 558 p. ill. 24 cmISBN:- 0-8493-9746-4 (hardcover : alk. paper)
- 21 543.072 D4571
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| Libro | Biblioteca Encarnación Valdés Colección General bev | 543.072 D4571 (Navegar estantería(Abre debajo)) | Disponible | 80000002116831 |
Descripciones mejoradas de Syndetics:
Chemists in research and development laboratories have relatively few published resources on the design and analysis of experiments. In recent years massive changes have occurred in the tools and instrumentation at their disposal, in the scale of databases linking the properties of pure materials, solutions or other mixtures to molecular structure, and in the sheer ability to collect data through automated data acquisition systems. Despite these advances, many chemists still apply only rudimentary data analysis techniques and remain unaware of the advances made in information extraction over the last decade.
Design and Analysis in Chemical Research provides the means to overcome that problem. An international panel of contributors address the principles of design and analysis in chemical research and development, with a thoughtful, user-friendly approach. Organized in chapters dealing with major activities, this volume generates understanding through numerous examples and practical applications drawn from research and development chemistry. The authors concentrate on principles and interpretation rather than formal derivation and proof, and adopt the unifying theme that statistics and chemometrics are essentially extensions of the logical processes used every day by chemists. Thus, they allow a greater understanding of problems more quickly and easily than purely intuitive methods.
Includes bibliographical references and index.
Tabla de contenidos provista por Syndetics
- Statistical thinking: the benefits and problems of a statistical approach
- Essentials of data gathering and data description
- Sampling
- Interpreting results
- Robust, resistant and nonparametric methods
- Experiment design: identifying factors that affect responses
- Designs for response surface modelling: quantifying the relationship between factors and response
- Analysis of variance: understanding and modelling variability
- Optimisation and control
- Grouping data together: cluster analysis and pattern recognition
- Linear regression
- Latent variable regression methods
- Data reconstruction methods for data processing
- References
- Index