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Statistical Design and Analysis of Biological Experiments (Hardcover, 1st ed. 2021)
Loot Price: R3,525
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Statistical Design and Analysis of Biological Experiments (Hardcover, 1st ed. 2021)
Series: Statistics for Biology and Health
Expected to ship within 12 - 17 working days
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This richly illustrated book provides an overview of the design and
analysis of experiments with a focus on non-clinical experiments in
the life sciences, including animal research. It covers the most
common aspects of experimental design such as handling multiple
treatment factors and improving precision. In addition, it
addresses experiments with large numbers of treatment factors and
response surface methods for optimizing experimental conditions or
biotechnological yields. The book emphasizes the estimation of
effect sizes and the principled use of statistical arguments in the
broader scientific context. It gradually transitions from classical
analysis of variance to modern linear mixed models, and provides
detailed information on power analysis and sample size
determination, including 'portable power' formulas for making quick
approximate calculations. In turn, detailed discussions of several
real-life examples illustrate the complexities and aberrations that
can arise in practice. Chiefly intended for students, teachers and
researchers in the fields of experimental biology and biomedicine,
the book is largely self-contained and starts with the necessary
background on basic statistical concepts. The underlying ideas and
necessary mathematics are gradually introduced in increasingly
complex variants of a single example. Hasse diagrams serve as a
powerful method for visualizing and comparing experimental designs
and deriving appropriate models for their analysis. Manual
calculations are provided for early examples, allowing the reader
to follow the analyses in detail. More complex calculations rely on
the statistical software R, but are easily transferable to other
software. Though there are few prerequisites for effectively using
the book, previous exposure to basic statistical ideas and the
software R would be advisable.
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