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.
Author: Robert Knell
Publisher: Robert Knell
Release Date: 2014-05-14
R is now the most widely used statistical software in academic science and it is rapidly expanding into other fields such as finance. R is almost limitlessly flexible and powerful, hence its appeal, but can be very difficult for the novice user. There are no easy pull-down menus, error messages are often cryptic and simple tasks like importing your data or exporting a graph can be difficult and frustrating. Introductory R is written for the novice user who knows a little about statistics but who hasn't yet got to grips with the ways of R. This new edition is completely revised and greatly expanded with new chapters on the basics of descriptive statistics and statistical testing, considerably more information on statistics and six new chapters on programming in R. Topics covered include: A walkthrough of the basics of R's command line interface Data structures including vectors, matrices and data frames R functions and how to use them Expanding your analysis and plotting capacities with add-in R packages A set of simple rules to follow to make sure you import your data properly An introduction to the script editor and advice on workflow A detailed introduction to drawing publication-standard graphs in R How to understand the help files and how to deal with some of the most common errors that you might encounter. Basic descriptive statistics The theory behind statistical testing and how to interpret the output of statistical tests Thorough coverage of the basics of data analysis in R with chapters on using chi-squared tests, t-tests, correlation analysis, regression, ANOVA and general linear models What the assumptions behind the analyses mean and how to test them using diagnostic plots Explanations of the summary tables produced for statistical analyses such as regression and ANOVA Writing your own functions in R Using table operations to manipulate matrices and data frames Using conditional statements and loops in R programmes. Writing longer R programmes. The techniques of statistical analysis in R are illustrated by a series of chapters where experimental and survey data are analysed. There is a strong emphasis on using real data from real scientific research, with all the problems and uncertainty that implies, rather than well-behaved made-up data that give ideal and easy to analyse results.
Author: Gordon A. Fox
Publisher: OUP Oxford
Release Date: 2015-01-29
The application and interpretation of statistics are central to ecological study and practice. Ecologists are now asking more sophisticated questions than in the past. These new questions, together with the continued growth of computing power and the availability of new software, have created a new generation of statistical techniques. These have resulted in major recent developments in both our understanding and practice of ecological statistics. This novel book synthesizes a number of these changes, addressing key approaches and issues that tend to be overlooked in other books such as missing/censored data, correlation structure of data, heterogeneous data, and complex causal relationships. These issues characterize a large proportion of ecological data, but most ecologists' training in traditional statistics simply does not provide them with adequate preparation to handle the associated challenges. Uniquely, Ecological Statistics highlights the underlying links among many statistical approaches that attempt to tackle these issues. In particular, it gives readers an introduction to approaches to inference, likelihoods, generalized linear (mixed) models, spatially or phylogenetically-structured data, and data synthesis, with a strong emphasis on conceptual understanding and subsequent application to data analysis. Written by a team of practicing ecologists, mathematical explanations have been kept to the minimum necessary. This user-friendly textbook will be suitable for graduate students, researchers, and practitioners in the fields of ecology, evolution, environmental studies, and computational biology who are interested in updating their statistical tool kits. A companion web site provides example data sets and commented code in the R language.
Author: Stanley E. Lazic
Publisher: Cambridge University Press
Release Date: 2016-12-08
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.
Author: William V. Dashek
Publisher: Science Pub Incorporated
Release Date: 2006-01-01
The present volume is a textbook for "upper" undergraduate and beginning graduate students. This textbook stresses concepts and is inquiry-oriented. To this end, there is extensive use of original research literature. As we live in an era of literature explosion, one must be selective. These judgments will naturally vary with each investigator. Input was sought from colleagues in deciding the literature to include. In addition to provision of select research literature, this volume presents citations and summaries of certain laboratory methods. In this connection, the textbook stresses quantitative data to enhance the student's analytical abilities. Thus, the volume contains computer-spread sheets and references to statistical packages, e.g. Harvard Graphics and Statistica.
Author: Xiaohua Douglas Zhang
Publisher: Cambridge University Press
Release Date: 2011-02-21
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.
Author: Olivier Walusinski
Publisher: Karger Medical and Scientific Publishers
Release Date: 2010-01-01
Yawning is a stereotyped phylogenetically ancient phenomenon that occurs in almost all vertebrates. As an emotional behavior and an expressive movement, yawning has many consequences; nevertheless, it has so far been poorly addressed in medical research and practice. Bringing together the latest research from many fields, this volume integrates current insights within embryology, ethology, neurophysiology, psychology, fMRI and pathology. The phylogenetic and ontogenetic aspects of yawning offer an interesting perspective on human development, and its occurrence in neurological diseases - an area explored by only a few investigators - may provide useful clinical information. This book will make valuable and fascinating reading to neurologists, sleep specialists, psychologists, ethologists and pharmacologists, as well as to anybody interested in uncovering the mystery of yawning.
Author: Victor Bloomfield
Publisher: Springer Science & Business Media
Release Date: 2009-06-05
This book provides an introduction to two important aspects of modern bioch- istry, molecular biology, and biophysics: computer simulation and data analysis. My aim is to introduce the tools that will enable students to learn and use some f- damental methods to construct quantitative models of biological mechanisms, both deterministicandwithsomeelementsofrandomness;tolearnhowconceptsofpr- ability can help to understand important features of DNA sequences; and to apply a useful set of statistical methods to analysis of experimental data. The availability of very capable but inexpensive personal computers and software makes it possible to do such work at a much higher level, but in a much easier way, than ever before. TheExecutiveSummaryofthein?uential2003reportfromtheNationalAcademy of Sciences, “BIO 2010: Transforming Undergraduate Education for Future - search Biologists” , begins The interplay of the recombinant DNA, instrumentation, and digital revolutions has p- foundly transformed biological research. The con?uence of these three innovations has led to important discoveries, such as the mapping of the human genome. How biologists design, perform, and analyze experiments is changing swiftly. Biological concepts and models are becoming more quantitative, and biological research has become critically dependent on concepts and methods drawn from other scienti?c disciplines. The connections between the biological sciences and the physical sciences, mathematics, and computer science are rapidly becoming deeper and more extensive.
The field of plant population ecology has advanced considerably in the last decade since the first edition was published. In particular there have been substantial and ongoing advances in statistics and modelling applications in population ecology, as well as an explosion of new techniques reflecting the availability of new technologies (e.g. affordable and accurate Global Positioning Systems) and advances in molecular biology. This new edition has been updated and revised with more recent examples replacing older ones where appropriate. The book's trademark question-driven approach has been maintained and some important topics such as the metapopulation concept which are missing entirely from the current edition are now included throughout the text.
Providing an interface between dry-bench bioinformaticians and wet-lab biologists, DNA Methylation Microarrays: Experimental Design and Statistical Analysis presents the statistical methods and tools to analyze high-throughput epigenomic data, in particular, DNA methylation microarray data. Since these microarrays share the same underlying principles as gene expression microarrays, many of the analyses in the text also apply to microarray-based gene expression and histone modification (ChIP-on-chip) studies. After introducing basic statistics, the book describes wet-bench technologies that produce the data for analysis and explains how to preprocess the data to remove systematic artifacts resulting from measurement imperfections. It then explores differential methylation and genomic tiling arrays. Focusing on exploratory data analysis, the next several chapters show how cluster and network analyses can link the functions and roles of unannotated DNA elements with known ones. The book concludes by surveying the open source software (R and Bioconductor), public databases, and other online resources available for microarray research. Requiring only limited knowledge of statistics and programming, this book helps readers gain a solid understanding of the methodological foundations of DNA microarray analysis.
Author: Robert D. Hall
Publisher: John Wiley & Sons
Release Date: 2011-02-25
Biology of Plant Metabolomics is an exciting new volume in Wiley-Blackwell's highly successful Annual Plant Reviews series. Concentrating on the biology and biological relevance of plant metabolomics, each chapter, written by internationally-acknowledged experts in the field from at least two different research groups, combines a review of the existing biological results with an extended assessment of possible future developments and the impact that these will have on the type of research needed for the future. Following a general introduction, this exciting volume includes details of metabolomics of model species including Arabidopsis and tomato. Further chapters provide in-depth coverage of abiotic stress, data integration, systems biology, genetics, genomics, chemometrics and biostatisitcs. Applications of plant metabolomics in food science, plant ecology and physiology are also comprehensively covered. Biology of Plant Metabolomics provides cutting edge reviews of many major aspects of this new and exciting subject. It is an essential purchase for plant scientists, plant geneticists and physiologists. All libraries in universities and research establishments where biological sciences are studied and taught should have a copy of this Annual Plant Reviews volume on their shelves.
Author: David J. Glass
Release Date: 2014-08-06
"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"--
Author: Norman T. J. Bailey
Publisher: Cambridge University Press
Release Date: 1995-01-26
Generations of biologists have relied on this useful book, which presents the basic concepts of statistics lucidly and convincingly. It recognizes that students must be aware of when to use standard techniques and how to apply the results they obtain. Because many biologists do not have a strong mathematical background, the arguments are gauged in terms that can be easily understood by those with only an elementary knowledge of algebra. Mathematical derivations are avoided and formulae are only used as a convenient shorthand. Although the subject is presented with great simplicity, the coverage is wide and will satisfy the needs of those working in many disciplines. New material for this third edition includes consideration of pocket electronic calculators and a special chapter devoted to a discussion of problems associated with numerical calculation, electronic calculators, and computers.