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Modern Statistics for the Life Sciences

Modern Statistics for the Life Sciences Author Wageningen UR.
ISBN-10 OCLC:212421073
Release 2008
Pages
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Modern Statistics for the Life Sciences has been writing in one form or another for most of life. You can find so many inspiration from Modern Statistics for the Life Sciences also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Modern Statistics for the Life Sciences book for free.



Statistics for the Life Sciences

Statistics for the Life Sciences Author Myra L. Samuels
ISBN-10 0321652800
Release 2012
Pages 654
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Statistics for the Life Sciences, Fourth Edition, covers the key concepts of statistics as applied to the life sciences, while incorporating the tools and themes of modern data analysis. This text uses an abundance of real data in the exercises and examples, and minimizes computation, so that readers can focus on the statistical concepts and issues, not the mathematics. Basic algebra is assumed as a prerequisite.



Modern Statistics for the Social and Behavioral Sciences

Modern Statistics for the Social and Behavioral Sciences Author Rand Wilcox
ISBN-10 9781498796804
Release 2017-08-15
Pages 706
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Requiring no prior training, Modern Statistics for the Social and Behavioral Sciences provides a two-semester, graduate-level introduction to basic statistical techniques that takes into account recent advances and insights that are typically ignored in an introductory course. Hundreds of journal articles make it clear that basic techniques, routinely taught and used, can perform poorly when dealing with skewed distributions, outliers, heteroscedasticity (unequal variances) and curvature. Methods for dealing with these concerns have been derived and can provide a deeper, more accurate and more nuanced understanding of data. A conceptual basis is provided for understanding when and why standard methods can have poor power and yield misleading measures of effect size. Modern techniques for dealing with known concerns are described and illustrated. Features: Presents an in-depth description of both classic and modern methods Explains and illustrates why recent advances can provide more power and a deeper understanding of data Provides numerous illustrations using the software R Includes an R package with over 1300 functions Includes a solution manual giving detailed answers to all of the exercises This second edition describes many recent advances relevant to basic techniques. For example, a vast array of new and improved methods is now available for dealing with regression, including substantially improved ANCOVA techniques. The coverage of multiple comparison procedures has been expanded and new ANOVA techniques are described. Rand Wilcox is a professor of psychology at the University of Southern California. He is the author of 13 other statistics books and the creator of the R package WRS. He currently serves as an associate editor for five statistics journals. He is a fellow of the Association for Psychological Science and an elected member of the International Statistical Institute.



The Practice of Statistics in the Life Sciences

The Practice of Statistics in the Life Sciences Author Brigitte Baldi
ISBN-10 9781464133213
Release 2013-12-15
Pages 608
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This remarkably engaging textbook gives biology students an introduction to statistical practice all their own. It covers essential statistical topics with examples and exercises drawn from across the life sciences, including the fields of nursing, public health, and allied health. Based on David Moore’s The Basic Practice of Statistics, PSLS mirrors that #1 bestseller’s signature emphasis on statistical thinking, real data, and what statisticians actually do. The new edition includes new and updated exercises, examples, and samples of real data, as well as an expanded range of media tools for students and instructors.



The Fundamentals of Modern Statistical Genetics

The Fundamentals of Modern Statistical Genetics Author Nan M. Laird
ISBN-10 1441973389
Release 2010-12-13
Pages 226
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This book covers the statistical models and methods that are used to understand human genetics, following the historical and recent developments of human genetics. Starting with Mendel’s first experiments to genome-wide association studies, the book describes how genetic information can be incorporated into statistical models to discover disease genes. All commonly used approaches in statistical genetics (e.g. aggregation analysis, segregation, linkage analysis, etc), are used, but the focus of the book is modern approaches to association analysis. Numerous examples illustrate key points throughout the text, both of Mendelian and complex genetic disorders. The intended audience is statisticians, biostatisticians, epidemiologists and quantitatively- oriented geneticists and health scientists wanting to learn about statistical methods for genetic analysis, whether to better analyze genetic data, or to pursue research in methodology. A background in intermediate level statistical methods is required. The authors include few mathematical derivations, and the exercises provide problems for students with a broad range of skill levels. No background in genetics is assumed.



Maths and Stats for the Life and Medical Sciences

Maths and Stats for the Life and Medical Sciences Author Michael Harris
ISBN-10 1904842119
Release 2005-01
Pages 187
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Many students now begin life and medical science degrees with little mathematical knowledge. This text provides an overview of the maths and stats required for these degree programmes with relevant examples to aid the learning process.



Modern Statistical Methods for Astronomy

Modern Statistical Methods for Astronomy Author Eric D. Feigelson
ISBN-10 9780521767279
Release 2012-07-12
Pages 476
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"Modern astronomical research is beset with a vast range of statistical challenges, ranging from reducing data from megadatasets to characterizing an amazing variety of variable celestial objects or testing astrophysical theory. Yet most astronomers still use a narrow suite of traditional statistical methods. Linking astronomy to the world of modern statistics, this volume is a unique resource, introducing astronomers to advanced statistics through ready-to-use code in the public-domain R statistical software environment"--



An Introduction to Statistics with Python

An Introduction to Statistics with Python Author Thomas Haslwanter
ISBN-10 9783319283166
Release 2016-07-20
Pages 278
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This textbook provides an introduction to the free software Python and its use for statistical data analysis. It covers common statistical tests for continuous, discrete and categorical data, as well as linear regression analysis and topics from survival analysis and Bayesian statistics. Working code and data for Python solutions for each test, together with easy-to-follow Python examples, can be reproduced by the reader and reinforce their immediate understanding of the topic. With recent advances in the Python ecosystem, Python has become a popular language for scientific computing, offering a powerful environment for statistical data analysis and an interesting alternative to R. The book is intended for master and PhD students, mainly from the life and medical sciences, with a basic knowledge of statistics. As it also provides some statistics background, the book can be used by anyone who wants to perform a statistical data analysis.



Mathematical and Statistical Applications in Life Sciences and Engineering

Mathematical and Statistical Applications in Life Sciences and Engineering Author Avishek Adhikari
ISBN-10 9789811053702
Release 2017-12-06
Pages 372
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The book includes articles from eminent international scientists discussing a wide spectrum of topics of current importance in mathematics and statistics and their applications. It presents state-of-the-art material along with a clear and detailed review of the relevant topics and issues concerned. The topics discussed include message transmission, colouring problem, control of stochastic structures and information dynamics, image denoising, life testing and reliability, survival and frailty models, analysis of drought periods, prediction of genomic profiles, competing risks, environmental applications and chronic disease control. It is a valuable resource for researchers and practitioners in the relevant areas of mathematics and statistics.



Statistics for the Life Sciences Pearson New International Edition

Statistics for the Life Sciences  Pearson New International Edition Author Myra L. Samuels
ISBN-10 9781292037073
Release 2013-08-29
Pages 640
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Statistics for the Life Sciences, Fourth Edition, is the perfect book for introductory statistics classes, covering the key concepts of statistics as applied to the life sciences, while incorporating the tools and themes of modern data analysis. This text uses an abundance of real data in the exercises and examples to minimize computation, so that students can focus on the statistical concepts and issues, not the mathematics. Basic algebra is assumed as a prerequisite. ¿ This latest edition is also available as an enhanced Pearson eText. This exciting new version features an embedded version of StatCrunch, allowing students to analyze data sets while reading the book. ¿ For graduate or undergraduate courses in biology, agronomy, medical and health sciences, nutrition, pharmacy, animal science, physical education, forestry, and other life sciences.



Mathematics for the Life Sciences

Mathematics for the Life Sciences Author Erin N. Bodine
ISBN-10 9781400852772
Release 2014-08-17
Pages 640
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The life sciences deal with a vast array of problems at different spatial, temporal, and organizational scales. The mathematics necessary to describe, model, and analyze these problems is similarly diverse, incorporating quantitative techniques that are rarely taught in standard undergraduate courses. This textbook provides an accessible introduction to these critical mathematical concepts, linking them to biological observation and theory while also presenting the computational tools needed to address problems not readily investigated using mathematics alone. Proven in the classroom and requiring only a background in high school math, Mathematics for the Life Sciences doesn't just focus on calculus as do most other textbooks on the subject. It covers deterministic methods and those that incorporate uncertainty, problems in discrete and continuous time, probability, graphing and data analysis, matrix modeling, difference equations, differential equations, and much more. The book uses MATLAB throughout, explaining how to use it, write code, and connect models to data in examples chosen from across the life sciences. Provides undergraduate life science students with a succinct overview of major mathematical concepts that are essential for modern biology Covers all the major quantitative concepts that national reports have identified as the ideal components of an entry-level course for life science students Provides good background for the MCAT, which now includes data-based and statistical reasoning Explicitly links data and math modeling Includes end-of-chapter homework problems, end-of-unit student projects, and select answers to homework problems Uses MATLAB throughout, and MATLAB m-files with an R supplement are available online Prepares students to read with comprehension the growing quantitative literature across the life sciences Forthcoming online answer key, solution guide, and illustration package (available to professors)



Modern Directional Statistics

Modern Directional Statistics Author Christophe Ley
ISBN-10 9781351645782
Release 2017-08-03
Pages 176
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Modern Directional Statistics collects important advances in methodology and theory for directional statistics over the last two decades. It provides a detailed overview and analysis of recent results that can help both researchers and practitioners. Knowledge of multivariate statistics eases the reading but is not mandatory. The field of directional statistics has received a lot of attention over the past two decades, due to new demands from domains such as life sciences or machine learning, to the availability of massive data sets requiring adapted statistical techniques, and to technological advances. This book covers important progresses in distribution theory,high-dimensional statistics, kernel density estimation, efficient inference on directional supports, and computational and graphical methods. Christophe Ley is professor of mathematical statistics at Ghent University. His research interests include semi-parametrically efficient inference, flexible modeling, directional statistics and the study of asymptotic approximations via Stein’s Method. His achievements include the Marie-Jeanne Laurent-Duhamel prize of the Société Française de Statistique and an elected membership at the International Statistical Institute. He is associate editor for the journals Computational Statistics & Data Analysis and Econometrics and Statistics. Thomas Verdebout is professor of mathematical statistics at Université libre de Bruxelles (ULB). His main research interests are semi-parametric statistics, high- dimensional statistics, directional statistics and rank-based procedures. He has won an annual prize of the Belgian Academy of Sciences and is an elected member of the International Statistical Institute. He is associate editor for the journals Statistics and Probability Letters and Journal of Multivariate Analysis.



Contemporary Statistical Models for the Plant and Soil Sciences

Contemporary Statistical Models for the Plant and Soil Sciences Author Oliver Schabenberger
ISBN-10 9781420040197
Release 2001-11-13
Pages 760
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Despite its many origins in agronomic problems, statistics today is often unrecognizable in this context. Numerous recent methodological approaches and advances originated in other subject-matter areas and agronomists frequently find it difficult to see their immediate relation to questions that their disciplines raise. On the other hand, statisticians often fail to recognize the riches of challenging data analytical problems contemporary plant and soil science provides. The first book to integrate modern statistics with crop, plant and soil science, Contemporary Statistical Models for the Plant and Soil Sciences bridges this gap. The breadth and depth of topics covered is unusual. Each of the main chapters could be a textbook in its own right on a particular class of data structures or models. The cogent presentation in one text allows research workers to apply modern statistical methods that otherwise are scattered across several specialized texts. The combination of theory and application orientation conveys ìwhyî a particular method works and ìhowî it is put in to practice. About the CD-ROM The accompanying CD-ROM is a key component of the book. For each of the main chapters additional sections of text are available that cover mathematical derivations, special topics, and supplementary applications. It supplies the data sets and SAS code for all applications and examples in the text, macros that the author developed, and SAS tutorials ranging from basic data manipulation to advanced programming techniques and publication quality graphics. Contemporary statistical models can not be appreciated to their full potential without a good understanding of theory. They also can not be applied to their full potential without the aid of statistical software. Contemporary Statistical Models for the Plant and Soil Science provides the essential mix of theory and applications of statistical methods pertinent to research in life sciences.



Computer Age Statistical Inference

Computer Age Statistical Inference Author Bradley Efron
ISBN-10 9781107149892
Release 2016-07-20
Pages 475
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Take an exhilarating journey through the modern revolution in statistics with two of the ringleaders.



Data Analysis for the Life Sciences with R

Data Analysis for the Life Sciences with R Author Rafael A. Irizarry
ISBN-10 9781498775861
Release 2016-10-04
Pages 376
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This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data. They include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained.



Experimental Design for the Life Sciences

Experimental Design for the Life Sciences Author Graeme Ruxton
ISBN-10 9780199569120
Release 2010-11-04
Pages 178
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Experimental Design for the Life Sciences teaches the reader how to effectively design experiments to ensure today's students are equipped with the skills they need to be the researchers of tomorrow. With a refreshingly approachable and articulate style, the book explains the essential elements of experimental design in clear, practical terms, so the reader can grasp and apply even the most challenging concepts, including power analysis andpseudoreplication. The inter-relatedness of experimental design, statistics, and ethical considerations is emphasised throughout the book and, above all, Experimental Design for the Life Sciencesdemonstrates how good experimental design relies on clear thinking and biological understanding, not mathematical or statistical complexity - putting it at the heart of any biosciences student's education.



Mathematical Models in Biology

Mathematical Models in Biology Author Elizabeth S. Allman
ISBN-10 0521525861
Release 2004
Pages 370
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Linear and non-linear models of populations, molecular evolution, phylogenetic tree construction, genetics, and infectious diseases are presented with minimal prerequisites.