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This book leaves the conventional view of chemical structures far behind: it demonstrates how a wealth of valuable, but hitherto unused information can be extracted from available structural data. For example, a single structure determination does not reveal much about a reaction pathway, but a sufficiently large number of comparable structures does. Finding the 'right' question is as important as is the intelligent use of crystallographic databases. Contributions by F.H. Allen, T.L. Blundell, I.D. Brown, H.B. Bürgi, J.D. Dunitz, L. Leiserowitz and others, authoritatively discuss the structure correlation method as well as illustrative results in detail, covering such apparently unrelated subjects as * Bond strength relations in soldis * Crystal structure prediction * Reaction pathways of organic molecules * Ligand/receptor interactions and enzyme mechanisms This book will be useful to the academic and industrial reader alike. It offers both fundamental aspects and diverse applications of what will surely become a powerful branch of structural chemistry.
Spectra-Structure Correlation focuses on absorption spectroscopy of organic compounds, including radiation, absorption, and analysis of compounds. The publication first offers information on wavelength classification of absorption spectra; intensities and shapes of absorption bands; mechanisms for the absorption of radiation; and solvent, phase, and temperature effects. The text also focuses on the spectra of hydrocarbons, as well as olefins, cyclopropanes, benzenes, allenes and cumulenes, cyclobutanes, cyclopentanes, and cyclohexanes. The manuscript reviews compounds with oxygen and nitrogen functions. Discussions focus on aldehydes and ketones, alcohols, carboxylic acids, phenols, ethers and peroxides, acid derivatives, amides and imides, amines, and nitriles and related functions. The text also ponders on organic compounds containing halogen, sulfur, phosphorus, silicon, or boron, inorganic compounds, and complex materials. Concerns include polymers, steroids, purines, pyrimidines, nucleic acids, amino acids, polypeptides, and proteins. The publication is a dependable reference for readers interested in absorption spectroscopy or organic compounds.
The book includes all main physical properties of d- and f-transition-metal systems and corresponding theoretical concepts. Special attention is paid to the theory of magnetism and transport phenomena. Some examples of non-traditional questions which are treated in detail in the book: the influence of density of states singularities on electron properties; many-electron description of strong itinerant magnetism; mechanisms of magnetic anisotropy; microscopic theory of anomalous transport phenomena in ferromagnets. Besides considering classical problems of solid state physics as applied to transition metals, modern developments in the theory of correlation effects in d- and f-compounds are considered within many-electron models. The book contains, where possible, a simple physical discussion. More difficult questions are considered in Appendices.
In this book the most important techniques available for longitudinal data analysis are discussed. This discussion includes simple techniques such as the paired t-test and summary statistics, but also more sophisticated techniques such as generalised estimating equations and random coefficient analysis. A distinction is made between longitudinal analysis with continuous, dichotomous, and categorical outcome variables. It should be stressed that the emphasis of the discussion lies on the interpretation of the different techniques and on the comparison of the results of different techniques. Furthermore, special chapters will deal with the analysis of two measurements, experimental studies and the problem of missing data in longitudinal studies. Finally, an extensive overview of (and a comparison between) different software packages is provided. It is important to realise that this book is a practical guide and especially suitable for non-statisticians.
This book goes beyond the truism that 'correlation does not imply causation' and explores the logical and methodological relationships between correlation and causation. It presents a series of statistical methods that can test, and potentially discover, cause-effect relationships between variables in situations in which it is not possible to conduct randomised or experimentally controlled experiments. Many of these methods are quite new and most are generally unknown to biologists. In addition to describing how to conduct these statistical tests, the book also puts the methods into historical context and explains when they can and cannot justifiably be used to test or discover causal claims. Written in a conversational style that minimises technical jargon, the book is aimed at practising biologists and advanced students, and assumes only a very basic knowledge of introductory statistics.
This book covers recent developments in correlated data analysis. It utilizes the class of dispersion models as marginal components in the formulation of joint models for correlated data. This enables the book to cover a broader range of data types than the traditional generalized linear models. The reader is provided with a systematic treatment for the topic of estimating functions, and both generalized estimating equations (GEE) and quadratic inference functions (QIF) are studied as special cases. In addition to the discussions on marginal models and mixed-effects models, this book covers new topics on joint regression analysis based on Gaussian copulas.
This book explores the diverse challenges facing the EU and in particular examines the impediments to financial stability and sustainable growth and how these can be overcome. Among the topics explored are the extent to which monetary union has favored real convergence, competitive imbalances in the eurozone, and the impacts of austerity measures. Potential solutions are closely scrutinized, highlighting the need for linked fiscal, monetary, credit, and investment choices. Opportunities for public and private investment in infrastructure, human capital, the environment, and innovation are emphasized, as is the role of fiscal stimulus targeting aggregate demand and output. Detailed attention is paid to the importance of coordination of macroeconomic policies and the scope for reforms in EMU design and EU governance. In this context, the proposals in the recent Five Presidents’ Report are assessed, along with other ideas regarding progressive steps aimed at closer economic, financial, and political union in the medium to long term. Readers will also find separate scrutiny of the Greek crisis and the effectiveness of the third economic adjustment programme. The book comprises a selection of contributions presented at the XXVIII Villa Mondragone International Economic Seminar.
From the reviews of the First Edition. "An interesting, useful, and well-written book on logistic regression models . . . Hosmer and Lemeshow have used very little mathematics, have presented difficult concepts heuristically and through illustrative examples, and have included references." —Choice "Well written, clearly organized, and comprehensive . . . the authors carefully walk the reader through the estimation of interpretation of coefficients from a wide variety of logistic regression models . . . their careful explication of the quantitative re-expression of coefficients from these various models is excellent." —Contemporary Sociology "An extremely well-written book that will certainly prove an invaluable acquisition to the practicing statistician who finds other literature on analysis of discrete data hard to follow or heavily theoretical." —The Statistician In this revised and updated edition of their popular book, David Hosmer and Stanley Lemeshow continue to provide an amazingly accessible introduction to the logistic regression model while incorporating advances of the last decade, including a variety of software packages for the analysis of data sets. Hosmer and Lemeshow extend the discussion from biostatistics and epidemiology to cutting-edge applications in data mining and machine learning, guiding readers step-by-step through the use of modeling techniques for dichotomous data in diverse fields. Ample new topics and expanded discussions of existing material are accompanied by a wealth of real-world examples-with extensive data sets available over the Internet.