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Books > Science & Mathematics > Mathematics > Applied mathematics
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Exploring Data in Engineering, the Sciences, and Medicine (Hardcover)
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Exploring Data in Engineering, the Sciences, and Medicine (Hardcover)
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Two recent and ongoing developments have greatly increased both the
range of opportunities for exploratory data analysis and the
variety of tools to support this type of analysis. First has been
the dramatic rise in the number of publicly available datasets
available free from the Internet and second has been the similarly
dramatic evolution of the Open Source software movement, making
powerful analysis packages like R also freely available. The
objective of this book is to provide a reasonably thorough
introduction to a useful subset of these analysis tools,
illustrating what they are, what they do, and when and how they
sometimes fail or do something very different than we expect them
to. Specific topics covered include descriptive characterizations
like summary statistics (mean, median, standard deviation, MAD
scale estimate, etc.), graphical techniques like boxplots and
nonparametric density estimates, various forms of regression
modeling (standard linear regression models, logistic regression,
and highly robust techniques like least trimmed squares), and the
recognition and treatment of important data anomalies like outliers
and missing data. In addition, the book also introduces a variety
of dynamic data analysis tools, including autocorrelation analysis,
parametric and nonparametric spectrum estimation, and the use of
nonlinear data cleaning filters to improve dynamic characterization
results. The book assumes familiarity with calculus and linear
algebra, but does not assume any prior exposure to probability or
statistics. Both simulation-based and real data examples are
included and the book is intended either as an introductory
textbook for an exploratory data analysis course like ones the
author taught at the ETH where some of this material was used, or
for self-study. Exercises are included at the end of each chapter
and both R code and datasets are available through the associated
OUP website.
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