Experimental Design and Data Analysis for Biologists


Author: Gerry P. Quinn,Michael J. Keough
Publisher: Cambridge University Press
ISBN: 1139432893
Category: Nature
Page: N.A
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An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data. The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models. Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models. Multivariate techniques, including classification and ordination, are then introduced. Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results. The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature. The book is supported by a website that provides all data sets, questions for each chapter and links to software.

An Introduction To Experimental Design And Statistics For Biology


Author: David Heath
Publisher: CRC Press
ISBN: 9780203499245
Category: Mathematics
Page: 384
View: 7936

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This illustrated textbook for biologists provides a refreshingly clear and authoritative introduction to the key ideas of sampling, experimental design, and statistical analysis. The author presents statistical concepts through common sense, non-mathematical explanations and diagrams. These are followed by the relevant formulae and illustrated by worked examples. The examples are drawn from all areas of biology, from biochemistry to ecology and from cell to animal biology. The book provides everything required in an introductory statistics course for biology undergraduates, and it is also useful for more specialized undergraduate courses in ecology, botany, and zoology.

Biostatistical Design and Analysis Using R

A Practical Guide
Author: Dr Murray Logan
Publisher: John Wiley & Sons
ISBN: 144436247X
Category: Science
Page: 576
View: 9628

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R — the statistical and graphical environment is rapidly emerging as an important set of teaching and research tools for biologists. This book draws upon the popularity and free availability of R to couple the theory and practice of biostatistics into a single treatment, so as to provide a textbook for biologists learning statistics, R, or both. An abridged description of biostatistical principles and analysis sequence keys are combined together with worked examples of the practical use of R into a complete practical guide to designing and analyzing real biological research. Topics covered include: simple hypothesis testing, graphing exploratory data analysis and graphical summaries regression (linear, multi and non-linear) simple and complex ANOVA and ANCOVA designs (including nested, factorial, blocking, spit-plot and repeated measures) frequency analysis and generalized linear models. Linear mixed effects modeling is also incorporated extensively throughout as an alternative to traditional modeling techniques. The book is accompanied by a companion website www.wiley.com/go/logan/r with an extensive set of resources comprising all R scripts and data sets used in the book, additional worked examples, the biology package, and other instructional materials and links.

Experimental Design for Biologists


Author: David J. Glass
Publisher: N.A
ISBN: 9781621820413
Category: Science
Page: 294
View: 8265

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"Experimental Design for Biologists is a unique and successful handbook on the theory and practice of effective design of scientific experiments, based on a well-received course by the author. This second edition is entirely reorganized, rewritten, and includes new material and figures. The material is presented in seven parts: Philosophy of Scientific Experimentation, Mapping Out the Project, System Validation, Experimental Design, Examples, What Comes After, and Putting It All Together. Experimental Design for Biologists, Second Edition, is an essential source in designing a sound research plan, critical to the success of graduate students"--

Statistics for Terrified Biologists


Author: Helmut van Emden
Publisher: John Wiley & Sons
ISBN: 1118541677
Category: Science
Page: 360
View: 1782

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“We highly recommend it—not just for statistically terrified biology students and faculty, but also for those who are occasionally anxious or uncertain. In addition to being a good starting point to learn statistics, it is a useful place to return to refresh your memory.” –The Quarterly Review of Biology, March 2009 "During the entire course of my Ph.D. I've been (embarrasingly) looking for a way to teach myself the fundamentals of statistical analysis. At this point in my education, I've come to realize that often times, simply knowing the basics is enough for you to properly apply even the most complex analytical methods. ‘Statistics for Terrified Biologists’ has been just such a book - it was more than worth the $40 I spent on it, and while my 'book clubs' aren't meant to be reviews, I highly recommend the book to anyone who's in a similar predicament to my own." –Carlo Artieri's Blog Book Club The typical biology student is “hardwired” to be wary of any tasks involving the application of mathematics and statistical analyses, but the plain fact is much of biology requires interpretation of experimental data through the use of statistical methods. This unique textbook aims to demystify statistical formulae for the average biology student. Written in a lively and engaging style, Statistics for Terrified Biologists draws on the author’s 30 years of lecturing experience. One of the foremost entomologists of his generation, van Emden has an extensive track record for successfully teaching statistical methods to even the most guarded of biology students. For the first time basic methods are presented using straightforward, jargon-free language. Students are taught to use simple formulae accurately to interpret what is being measured with each test and statistic, while at the same time learning to recognize overall patterns and guiding principles. Complemented by simple illustrations and useful case studies, this is an ideal statistics resource tool for undergraduate biology and environmental science students who lack confidence in their mathematical abilities.

Experimental Design for Laboratory Biologists

Maximising Information and Improving Reproducibility
Author: Stanley E. Lazic
Publisher: Cambridge University Press
ISBN: 1316810674
Category: Medical
Page: N.A
View: 5980

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Specifically intended for lab-based biomedical researchers, this practical guide shows how to design experiments that are reproducible, with low bias, high precision, and widely applicable results. With specific examples from research using both cell cultures and model organisms, it explores key ideas in experimental design, assesses common designs, and shows how to plan a successful experiment. It demonstrates how to control biological and technical factors that can introduce bias or add noise, and covers rarely discussed topics such as graphical data exploration, choosing outcome variables, data quality control checks, and data pre-processing. It also shows how to use R for analysis, and is designed for those with no prior experience. An accompanying website (https://stanlazic.github.io/EDLB.html) includes all R code, data sets, and the labstats R package. This is an ideal guide for anyone conducting lab-based biological research, from students to principle investigators working in either academia or industry.

Statistical Methods in Biology

Design and Analysis of Experiments and Regression
Author: S.J. Welham,S.A. Gezan,S.J. Clark,A. Mead
Publisher: CRC Press
ISBN: 1439898057
Category: Mathematics
Page: 608
View: 899

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Written in simple language with relevant examples, Statistical Methods in Biology: Design and Analysis of Experiments and Regression is a practical and illustrative guide to the design of experiments and data analysis in the biological and agricultural sciences. The book presents statistical ideas in the context of biological and agricultural sciences to which they are being applied, drawing on relevant examples from the authors’ experience. Taking a practical and intuitive approach, the book only uses mathematical formulae to formalize the methods where necessary and appropriate. The text features extended discussions of examples that include real data sets arising from research. The authors analyze data in detail to illustrate the use of basic formulae for simple examples while using the GenStat® statistical package for more complex examples. Each chapter offers instructions on how to obtain the example analyses in GenStat and R. By the time you reach the end of the book (and online material) you will have gained: A clear appreciation of the importance of a statistical approach to the design of your experiments, A sound understanding of the statistical methods used to analyse data obtained from designed experiments and of the regression approaches used to construct simple models to describe the observed response as a function of explanatory variables, Sufficient knowledge of how to use one or more statistical packages to analyse data using the approaches described, and most importantly, An appreciation of how to interpret the results of these statistical analyses in the context of the biological or agricultural science within which you are working. The book concludes with a guide to practical design and data analysis. It gives you the understanding to better interact with consultant statisticians and to identify statistical approaches to add value to your scientific research.

Experimental Design for the Life Sciences


Author: Graeme Ruxton,Nick Colegrave
Publisher: Oxford University Press
ISBN: 0199569126
Category: Education
Page: 178
View: 9910

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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.

Experiments in Ecology

Their Logical Design and Interpretation Using Analysis of Variance
Author: A. J. Underwood
Publisher: Cambridge University Press
ISBN: 9780521556965
Category: Nature
Page: 504
View: 3903

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First published in 1996, this book is a logical and consistent approach to experimental design using statistical principles.

Optimal High-Throughput Screening

Practical Experimental Design and Data Analysis for Genome-Scale RNAi Research
Author: Xiaohua Douglas Zhang
Publisher: Cambridge University Press
ISBN: 1139498371
Category: Medical
Page: N.A
View: 2349

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This concise, self-contained and cohesive book focuses on commonly used and recently developed methods for designing and analyzing high-throughput screening (HTS) experiments from a statistically sound basis. Combining ideas from biology, computing and statistics, the author explains experimental designs and analytic methods that are amenable to rigorous analysis and interpretation of RNAi HTS experiments. The opening chapters are carefully presented to be accessible both to biologists with training only in basic statistics and to computational scientists and statisticians with basic biological knowledge. Biologists will see how new experiment designs and rudimentary data-handling strategies for RNAi HTS experiments can improve their results, whereas analysts will learn how to apply recently developed statistical methods to interpret HTS experiments.

Statistics for Biologists


Author: D. J. Finny
Publisher: Springer Science & Business Media
ISBN: 9400958552
Category: Science
Page: 166
View: 3576

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This book has grown from nine hours oflectures, and about the same time in tutorial classes, that attempt to give first-year students of biology some understanding of statistics. I am convinced that such a short course should not be mathematical (though it can employ basic mathematical symbolism), and that it should give students an appreciation of statistical argument, even though this limits the amount of detailed instruction in techniques of analysis that can be included. A statistical cookery book would have been easier to write and much easier to read, but lacking in true educational content. I am more concerned to show 'why' than to present methods and rules. A further constraint, that of remaining within a reasonable price range, prevents reiteration of explanations: the reader is expected to remember what he has read, for he will not find standard terms and ideas explained afresh on each occasion of use. Many books that introduce statistics to biologists blur distinctions and evade logical issues, for example by failing to emphasize the distinction between a parameter and an estimator from a sample or by neglecting the role of randomization. On this, I aim to be un compromisingly correct - at least until reviewers point out my errors - but to do so through realistic examples rather than abstract symbolism.

Mathematical and Statistical Methods for Genetic Analysis


Author: Kenneth Lange
Publisher: Springer Science & Business Media
ISBN: 1475727399
Category: Mathematics
Page: 265
View: 7840

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Geneticists now stand on the threshold of sequencing the genome in its entirety. The unprecedented insights into human disease and evolution offered by mapping and sequencing are transforming medicine and agriculture. This revolution depends vitally on the contributions made by applied mathematicians, statisticians, and computer scientists. Kenneth Lange has written a book to enable graduate students in the mathematical sciences to understand and model the epidemiological and experimental data encountered in genetics research. Mathematical, statistical, and computational principles relevant to this task are developed hand-in-hand with applications to gene mapping, risk prediction, and the testing of epidemiological hypotheses. The book covers many topics previously only accessible in journal articles, such as pedigree analysis algorithms, Markov chain, Monte Carlo methods, reconstruction of evolutionary trees, radiation hybrid mapping, and models of recombination. The whole is backed by numerous exercise sets.

Choosing and Using Statistics

A Biologist's Guide
Author: Calvin Dytham
Publisher: John Wiley & Sons
ISBN: 1405198389
Category: Science
Page: 304
View: 433

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Choosing and Using Statistics remains an invaluable guide for students using a computer package to analyse data from research projects and practical class work. The text takes a pragmatic approach to statistics with a strong focus on what is actually needed. There are chapters giving useful advice on the basics of statistics and guidance on the presentation of data. The book is built around a key to selecting the correct statistical test and then gives clear guidance on how to carry out the test and interpret the output from four commonly used computer packages: SPSS, Minitab, Excel, and (new to this edition) the free program, R. Only the basics of formal statistics are described and the emphasis is on jargon-free English but any unfamiliar words can be looked up in the extensive glossary. This new 3rd edition of Choosing and Using Statistics is a must for all students who use a computer package to apply statistics in practical and project work. Features new to this edition: Now features information on using the popular free program, R Uses a simple key and flow chart to help you choose the right statistical test Aimed at students using statistics for projects and in practical classes Includes an extensive glossary and key to symbols to explain any statistical jargon No previous knowledge of statistics is assumed

Statistical Methods in Agriculture and Experimental Biology, Third Edition


Author: Roger Mead
Publisher: CRC Press
ISBN: 1351414283
Category: Mathematics
Page: 488
View: 2403

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The third edition of this popular introductory text maintains the character that won worldwide respect for its predecessors but features a number of enhancements that broaden its scope, increase its utility, and bring the treatment thoroughly up to date. It provides complete coverage of the statistical ideas and methods essential to students in agriculture or experimental biology. In addition to covering fundamental methodology, this treatment also includes more advanced topics that the authors believe help develop an appreciation of the breadth of statistical methodology now available. The emphasis is not on mathematical detail, but on ensuring students understand why and when various methods should be used. New in the Third Edition: A chapter on the two simplest yet most important methods of multivariate analysis Increased emphasis on modern computer applications Discussions on a wider range of data types and the graphical display of data Analysis of mixed cropping experiments and on-farm experiments

Foundational and Applied Statistics for Biologists Using R


Author: Ken A. Aho
Publisher: CRC Press
ISBN: 1439873399
Category: Mathematics
Page: 618
View: 6581

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Full of biological applications, exercises, and interactive graphical examples, Foundational and Applied Statistics for Biologists Using R presents comprehensive coverage of both modern analytical methods and statistical foundations. The author harnesses the inherent properties of the R environment to enable students to examine the code of complicated procedures step by step and thus better understand the process of obtaining analysis results. The graphical capabilities of R are used to provide interactive demonstrations of simple to complex statistical concepts. Assuming only familiarity with algebra and general calculus, the text offers a flexible structure for both introductory and graduate-level biostatistics courses. The first seven chapters address fundamental topics in statistics, such as the philosophy of science, probability, estimation, hypothesis testing, sampling, and experimental design. The remaining four chapters focus on applications involving correlation, regression, ANOVA, and tabular analyses. Unlike classic biometric texts, this book provides students with an understanding of the underlying statistics involved in the analysis of biological applications. In particular, it shows how a solid statistical foundation leads to the correct application of procedures, a clear understanding of analyses, and valid inferences concerning biological phenomena. Web Resource An R package (asbio) developed by the author is available from CRAN. Accessible to those without prior command-line interface experience, this companion library contains hundreds of functions for statistical pedagogy and biological research. The author’s website also includes an overview of R for novices.

Statistical Analysis of Gene Expression Microarray Data


Author: Terry Speed
Publisher: CRC Press
ISBN: 9780203011232
Category: Science
Page: 240
View: 2364

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Although less than a decade old, the field of microarray data analysis is now thriving and growing at a remarkable pace. Biologists, geneticists, and computer scientists as well as statisticians all need an accessible, systematic treatment of the techniques used for analyzing the vast amounts of data generated by large-scale gene expression studies. And there is arguably no group better qualified to do so than the authors of this book. Statistical Analysis of Gene Expression Microarray Data promises to become the definitive basic reference in the field. Under the editorship of Terry Speed, some of the world's most pre-eminent authorities have joined forces to present the tools, features, and problems associated with the analysis of genetic microarray data. These include:: Model-based analysis of oligonucleotide arrays, including expression index computation, outlier detection, and standard error applications Design and analysis of comparative experiments involving microarrays, with focus on \ two-color cDNA or long oligonucleotide arrays on glass slides Classification issues, including the statistical foundations of classification and an overview of different classifiers Clustering, partitioning, and hierarchical methods of analysis, including techniques related to principal components and singular value decomposition Although the technologies used in large-scale, high throughput assays will continue to evolve, statistical analysis will remain a cornerstone of their success and future development. Statistical Analysis of Gene Expression Microarray Data will help you meet the challenges of large, complex datasets and contribute to new methodological and computational advances.

Big Data Analysis for Bioinformatics and Biomedical Discoveries


Author: Shui Qing Ye
Publisher: CRC Press
ISBN: 149872454X
Category: Mathematics
Page: 274
View: 9636

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Demystifies Biomedical and Biological Big Data Analyses Big Data Analysis for Bioinformatics and Biomedical Discoveries provides a practical guide to the nuts and bolts of Big Data, enabling you to quickly and effectively harness the power of Big Data to make groundbreaking biological discoveries, carry out translational medical research, and implement personalized genomic medicine. Contributing to the NIH Big Data to Knowledge (BD2K) initiative, the book enhances your computational and quantitative skills so that you can exploit the Big Data being generated in the current omics era. The book explores many significant topics of Big Data analyses in an easily understandable format. It describes popular tools and software for Big Data analyses and explains next-generation DNA sequencing data analyses. It also discusses comprehensive Big Data analyses of several major areas, including the integration of omics data, pharmacogenomics, electronic health record data, and drug discovery. Accessible to biologists, biomedical scientists, bioinformaticians, and computer data analysts, the book keeps complex mathematical deductions and jargon to a minimum. Each chapter includes a theoretical introduction, example applications, data analysis principles, step-by-step tutorials, and authoritative references.

Observing Animal Behaviour

Design and analysis of quantitative data
Author: Marian Stamp Dawkins
Publisher: OUP Oxford
ISBN: 0191037443
Category: Nature
Page: 176
View: 5470

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This book introduces the reader to the power of observation before, and sometimes instead of, experimental manipulation in the study of animal behaviour. It starts with simple and easily accessible methods suitable for student projects, before going on to demonstrate the possibilities that now exist for far more sophisticated analyses of observational data. At a time when animal welfare considerations are attracting political as well as scientific debate, the potential for non-intrusive studies on animals is being increasingly recognized. Observation emerges as a valuable alternative approach, often yielding highly informative results in situations (such as on zoos, farms or for wild animals) where more invasive experimental techniques would be undesirable, unethical or just plain impossible. However, to justify its place alongside experimentation as a rigorous scientific method, observation needs to be just as disciplined and systematic and have just as much attention paid to project design in the way that observations are made and recorded. Observing Animal Behaviour takes the reader through all these stages: from the initial observations, to the formulation of hypotheses, and their subsequent testing with further systematic observations. Although designed principally as a companion text for advanced undergraduate and students taking courses in animal behaviour, this accessible text will be essential reading for anyone wanting to study animal behaviour using observational methods rather than experimentation, and assumes no previous knowledge of animals, statistics or scientific method. It will be of particular relevance and use to those professional researchers and consultants in the behavioural sciences who seek a compact but comprehensive introduction to the quantitative observation of animal behaviour.