Recent years have seen massive changes in the tools and instrumentation available to chemists, in the scale of databases linking the properties of pure materials, solutions or other mixtures to molecular structure, and in the sheer ability of chemists 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 or so. This volume covers the principles of design and analysis in chemical research and development. It is organised in chapters dealing with major activities, and understanding is generated through large numbers of examples and practical applications relating to research and development chemistry. Authors adopt a user--friendly approach, concentrating on principles and interpretation rather than formal derivation and proof. A principal theme is that statistics and chemometrics (which relies on statistics) are essentially extensions of the logical processes used every day by chemists, and that they bring greater understanding of problems more quickly and easily than purely intuitive methods.
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.