Topics In Biostatistics
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Topics in Biostatistics
Author: Walter T. Ambrosius
language: en
Publisher: Springer Science & Business Media
Release Date: 2007-07-06
This book presents a multidisciplinary survey of biostatics methods, each illustrated with hands-on examples. It introduces advanced methods in statistics, including how to choose and work with statistical packages. Specific topics of interest include microarray analysis, missing data techniques, power and sample size, statistical methods in genetics. The book is an essential resource for researchers at every level of their career.
Biostatistics for Medical and Biomedical Practitioners
Author: Julien I. E. Hoffman
language: en
Publisher: Academic Press
Release Date: 2019-03-20
Basic Biostatistics for Medical and Biomedical Practitioners, Second Edition makes it easier to plan experiments, with an emphasis on sample size. It also shows what choices are available when simple tests are unsuitable and offers investigators an overview of how the kinds of complex tests that they won't do on their own work. The second edition presents a new, revised and enhanced version of the chapters, taking into consideration new developments and tools available, discussing topics, such as the basic aspects of statistics, continuous distributions, hypothesis testing, discrete distributions, probability in epidemiology and medical diagnosis, comparing means, regression and correlation. This book is a valuable source for students and researchers looking to expand or refresh their understanding of statistics as it applies to the biomedical and research fields. Based on the author's 40+ years of teaching statistics to medical fellows and biomedical researchers across a wide range of fields, it is a valuable source for researchers who need to understand more about biostatistics to apply it to their work.
Biostatistics with R
Author: Jan Lepš
language: en
Publisher: Cambridge University Press
Release Date: 2020-07-30
A straightforward introduction to a wide range of statistical methods for field biologists, using thoroughly explained R code.