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The liquid crystalline state may be identified as a distinct and
unique state of matter which is characterised by properties which
resemble those of both solids and liquids. It was first recognised
in the middle of the last century through the study of nerve myelin
and derivatives of cholesterol. The research in the area really
gathered momentum, however, when as a result of the pioneering work
of Gray in the early 1970's organic compounds exhibiting liquid
crystalline properties were shown to be suitable to form the basis
of display devices in the electronic products. The study of liquid
crystals is truly multidisciplinary and has attached the attention
of physicists, biologists, chemists, mathematicians and electronics
engineers. It is therefore impossible to cover all these aspects
fully in two small volumes and therefore it was decided in view of
the overall title of the series to concentrate on the structural
and bonding aspects of the subject. The Chapters presented in these
two volumes have been organised to cover the following fundamental
aspects of the subiect. The calculation of the structures of liquid
crystals, an account of their dynamical properties and a discussion
of computer simulations of liquid crystalline phases formed by Gay
Berne mesogens. The relationships between molecular conformation
and packing are analysed in some detail. The crystal structures of
liquid crystal mesogens and the importance of their X ray
scattering properties for characterisational purposes are
discussed.
Written in simple language with relevant examples, Statistical
Methods in Biology: Design and Analysis of Experiments and
Regression is a practical and illustrative guide to the design of
experiments and data analysis in the biological and agricultural
sciences. The book presents statistical ideas in the context of
biological and agricultural sciences to which they are being
applied, drawing on relevant examples from the authors' experience.
Taking a practical and intuitive approach, the book only uses
mathematical formulae to formalize the methods where necessary and
appropriate. The text features extended discussions of examples
that include real data sets arising from research. The authors
analyze data in detail to illustrate the use of basic formulae for
simple examples while using the GenStat (R) statistical package for
more complex examples. Each chapter offers instructions on how to
obtain the example analyses in GenStat and R. By the time you reach
the end of the book (and online material) you will have gained: A
clear appreciation of the importance of a statistical approach to
the design of your experiments, A sound understanding of the
statistical methods used to analyse data obtained from designed
experiments and of the regression approaches used to construct
simple models to describe the observed response as a function of
explanatory variables, Sufficient knowledge of how to use one or
more statistical packages to analyse data using the approaches
described, and most importantly, An appreciation of how to
interpret the results of these statistical analyses in the context
of the biological or agricultural science within which you are
working. The book concludes with a guide to practical design and
data analysis. It gives you the understanding to better interact
with consultant statisticians and to identify statistical
approaches to add value to your scientific research.
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