Design and Analysis in Chemical ResearchRoy L. Tranter CRC Press, 2000 - 558 pagina's 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. |
Inhoudsopgave
Statistical thinkingThe benefits and problems of | 1 |
Essentials of data gathering and data description | 34 |
Sampling | 85 |
Interpreting results | 113 |
Robust resistant and nonparametric methods | 145 |
Experiment designIdentifying factors that affect responses | 188 |
Designs for response surface modellingQuantifying the relation | 237 |
Analysis of Variance Understanding and modelling variability | 279 |
Optimisation and control | 314 |
References | 361 |
Linear regression | 421 |
Latent variable regression methods | 473 |
CONTENTS | 505 |
References | 545 |
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algorithms analysis of variance analytical ANOVA applied approach assay assess average batch blocks calculated central composite design centre points chapter chemical Chemometrics clustering coefficients common cause variation confidence interval control charts correlation Cusum data reconstruction data set data values Deconvolution degrees of freedom detect equation estimate example experiment factors filter function genetic algorithms groups Hoaglin input interactions investigation laboratory latent variable least squares letter value linear model linear regression matrix mean measurements median minimising Minitab monitoring multivariate neural network noise nonlinear Normal distribution objects obtained optimisation outliers parameters peak plot prediction predictor principal component analysis principal components problem random randomised range regression model replicates residuals response surface robust sample Section Shewhart shown in Figure shows significant standard deviation statistical methods sum of squares Table techniques temperature two-factor variation vector weight x₁ zero