Statistics for Health Care Professionals: An Introduction
Ian Scott and Deborah Mazhindu

Sage 2005 ISBN: 0761974768, 076197475X 248 pages PDF 1,4 MB

A companion website is available for this !
Statistics for Health Care Professionals is an accessible guide to understanding statistics within health care practice. Focusing on quantitative approaches to investigating problems, the book introduces the basic rules and principles of statistics.

Challenging the notion that statistics are often incomprehensible and complex to use, the authors begin presenting a `how to’ section explaining how specific statistical tests can be performed. They also help readers to understand the language of statistics, which is often a stumbling block for those coming to the subject for the first time. The reader is taught how to calculate statistics hand as well as being introduced to computer packages to make life easier, and then how to analyse these results.

As the results of health care research are so integral to decision-making and developing new practice within the profession, the book encourages the reader to think critically about data analysis and research design, and how these can impact upon evidence based practice. This critical stance is also crucial in the assessment of the many reports and documents issued within the health industry.

Statistics for Health Care Professionals includes practical examples of statistical techniques throughout, and the exercises within and at the end of each chapter help readers to learn and to develop proficiency. There is also a glossary at the end of the book for quick and easy referencing.

This book is essential reading for those coming to statistics for the first time within a health care setting.

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Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning
Alan Julian Izenman

Springer 2008-08-28 ISBN: 0387781889 734 pages PDF 10,7 MB

Remarkable advances in computation and data storage and the ready availability of huge data sets have been the keys to the growth of the new disciplines of data mining and machine learning, while the enormous success of the Human Genome Project has opened up the field of bioinformatics.

These exciting developments, which led to the introduction of many innovative statistical tools for high-dimensional data analysis, are described here in detail. The author takes a broad perspective; for the first time in a book on multivariate analysis, nonlinear methods are discussed in detail as well as linear methods. Techniques covered range from traditional multivariate methods, such as multiple regression, principal components, canonical variates, linear discriminant analysis, factor analysis, clustering, multidimensional scaling, and correspondence analysis, to the newer methods of density estimation, projection pursuit, neural networks, multivariate reduced-rank regression, nonlinear manifold learning, bagging, boosting, random forests, independent component analysis, support vector machines, and classification and regression trees. Another unique feature of this book is the discussion of database management systems.

This book is appropriate for advanced undergraduate students, graduate students, and researchers in statistics, computer science, artificial intelligence, psychology, cognitive sciences, business, medicine, bioinformatics, and engineering. Familiarity with multivariable calculus, linear algebra, and probability and statistics is required. The book presents a carey-integd mixture of theory and applications, and of classical and modern multivariate statistical techniques, including Bayesian methods. There are over 60 interesting data sets used as examples in the book, over 200 exercises, and many color illustrations and photographs.

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Analyzing the Large Number of Variables in Biomedical and Satellite Imagery
Phillip I. Good

Publisher: Wiley 2011 187 Pages ISBN: 0470927143 PDF 4 MB

This book grew out of an online interactive offered through statcourse.com…, and it soon became apparent to the author that the course was too limited in terms of time and length in light of the broad backgrounds of the enrolled students. The statisticians who took the course needed to be brought up to speed both on the biological con as well as on the specialized statistical methods needed to handle large arrays. Biologists and physicians, even though y knowledgeable concerning the procedures used to gene microaarrays, EEGs, or MRIs, needed a introduction to the resampling methods—the bootstrap, decision trees, and permutation tests, before the specialized methods applicable to large arrays could be introduced. As the intended audience for this book consists both of statisticians and of medical and biological research workers as well as all those research workers who make use of satellite imagery including agronomists and meteorologists, the book provides a step–step approach to not only the specialized methods needed to analyze the data from microarrays and images, but also to the resampling methods, step-down multi-comparison procedures, multivariate analysis, as well as data collection and pre-processing. While many alternate techniques for analysis have been introduced in the past decade, the author has selected only those techniques for which software is available along with a list of the available links from which the software may be purchased or downloaded without charge. Topical coverage includes: very large arrays; permutation tests; applying permutation tests; gathering and preparing data for analysis; multiple tests; bootstrap; applying the bootstrap; classification methods; decision trees; and applying decision trees.

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