This new text is an ideal introduction to statistics for biomolecular science students based on a successful course and relating statistical theory to real life examples. Data Analysis for Biomolecular Sciences covers the statistics of biological tests and assays, communication of the results of analyses and the use of statistical tests to aid decision making. Topics include probability, precision and accuracy of measurements, significance testing, the t-test, analysis of variance and linear/nonlinear regression.
Students of biological subjects often think of statistics as difficult or unappealing so Data Analysis for the Biomolecular Sciences aims to demystify the subject and provide motivation for learning data handling techniques. The text demonstrates the value of the subject in situations ranging from prenatal screening for spina bifida to the use of DNA profiling as criminal evidence, putting every statistical concept into context by providing numerous examples taken from medical science.
Recognising that scientists now use computers to perform statistical analyses, the author has also been able to avoid some of the more difficult mathematics and put more emphasis on the interpretation of figures produced by computer software. An entire chapter is devoted to worked examples of data handling performed with Microsoft Excel.
Data Analysis for Biomolecular Sciences is aimed at first year undergraduates studying Biochemistry, Physiology, Pharmacology, and other biological subjects involving assaying biomolecules.
Table of contents
Preface; Probability; Significance; Measurements and Assays; Precision; Accuracy; Relationships Between Variables; Screening for Colorectal Cancer; Data Handling with a Computer; Exercises; Index.
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