**Author**: Thomas W. MacFarland,Jan M. Yates

**Publisher:** Springer

**ISBN:** 3319306340

**Category:** Medical

**Page:** 329

**View:** 1996

This book contains a rich set of tools for nonparametric analyses, and the purpose of this text is to provide guidance to students and professional researchers on how R is used for nonparametric data analysis in the biological sciences: To introduce when nonparametric approaches to data analysis are appropriate To introduce the leading nonparametric tests commonly used in biostatistics and how R is used to generate appropriate statistics for each test To introduce common figures typically associated with nonparametric data analysis and how R is used to generate appropriate figures in support of each data set The book focuses on how R is used to distinguish between data that could be classified as nonparametric as opposed to data that could be classified as parametric, with both approaches to data classification covered extensively. Following an introductory lesson on nonparametric statistics for the biological sciences, the book is organized into eight self-contained lessons on various analyses and tests using R to broadly compare differences between data sets and statistical approach.

All students and researchers in environmental and biologicalsciences require statistical methods at some stage of their work.Many have a preconception that statistics are difficult andunpleasant and find that the textbooks available are difficult tounderstand. Practical Statistics for Environmental and BiologicalScientists provides a concise, user-friendly, non-technicalintroduction to statistics. The book covers planning and designingan experiment, how to analyse and present data, and the limitationsand assumptions of each statistical method. The text does not referto a specific computer package but descriptions of how to carry outthe tests and interpret the results are based on the approachesused by most of the commonly used packages, e.g. Excel, MINITAB andSPSS. Formulae are kept to a minimum and relevant examples areincluded throughout the text.

Statistics a tool of research; Data and distributions; The normal distribution; Probability; Inference; Hypothesis testing; Enumeration data chi-square and poisson distributions; Analysis of variance; Correlation and regression; Analysis of covariance, Nonparametric tests.

Practical Statistics for the Biological Sciences is a handbook of statistical methods for use by workers in the biological sciences at all levels from undergraduate to post-doctoral researcher. The book presents, in a clear and compact form, the most common statistical tests used in the biosciences. It provides a series of flow charts to help in selecting the appropriate test. Each test is described concisely and illustrated by worked examples. The text is accompanied by a CD-ROM containing both Windows and Apple Macintosh versions of PractiStat, a new programme for statistical analysis. PractiStat can be used both as a teaching aid to work through the examples provided and as a valuable, easy to use tool for analysis of the users' own data. The programme provides a simple, intuitive interface and permits the application of the common statistical tests and procedures used by bioscientists.

Even though an understanding of experimental design and statistics is central to modern biology, undergraduate and graduate students studying biological subjects often lack confidence in their numerical abilities. Allaying the anxieties of students, Introduction to Statistics for Biology, Third Edition provides a painless introduction to the subject while demonstrating the importance of statistics in contemporary biological studies. New to the Third Edition More detailed explanation of the ideas of elementary probability to simplify the rationale behind hypothesis testing, before moving on to simple tests An emphasis on experimental design and data simulation prior to performing an experiment A general template for carrying out statistical tests from hypothesis to interpretation Worked examples and updated Minitab analyses and graphics A CD-ROM that contains a free trial version of Minitab Using Minitab throughout to present practical examples, the authors emphasize the interpretation of computer output. With its nontechnical approach and practical advice, this student-friendly introductory text lays the foundation for the advanced study of statistical analysis.

Using a statistical perspective, this work introduces a number of practical examples from diverse forms of microscopy, medical imaging systems, remote sensing & contact prints to demonstrate the different range of image analysis applications. Explains the nature of digital images & terminology use. Reference is also made to extensions of multivariate & multi-temporal images. The appendix contains useful guidance on selecting hardware/software for a specific application.

Milton's Statistical Methods in the Biological and Health Sciences offers comprehensive coverage for the applied statistics course for health and bio-related majors. This course focuses primarily on developing basic statistical techniques and relevant applications within a framework that addresses the needs of these specific audiences.

A book/disk text for a one- or two-semester course in basics of applied biostatistics. Learning features include summaries of formulas and terms, exercises, and statistical tables. This third edition reduces emphasis on methods of computation, and concentrates more on the rationale and utility of v

This introductory text presents the use of statistical methods as an integral part of biological investigation, yet one whose superficial complexities have deterred many biologists from using them. The author argues that the difficulties, such as they are, do not lie in mathematical manipulation, but in grasping a few simple, but unfamiliar concepts. He emphasizes the need for precisely defining problems and for careful selection of the most appropriate methods - a wide range of which are described and illustrated. Each chapter ends with a set of problems which are intended to help the student gain practical experience. No previous knowledge is assumed, and the student is encouraged to develop a competent and critical approach to analysing numerical data. In this second edition, the scope of the book has been extended, problems have been solved in a more satisfactory way, and a greater number of illustrative examples have been added.

This introductory undergraduate textbook is the first statistics textbook built around the General Linear Model.

The use of statistics in biology, medicine, engineering, and the sciences has grown dramatically in recent years and having a basic background in the subject has become a near necessity for students and researchers in these fields. Although many introductory statistics books already exist, too often their focus leans towards theory and few help readers gain effective experience in using a standard statistical software package. Designed to be used in a first course for graduate or upper-level undergraduate students, Basic Statistical Methods and Models builds a practical foundation in the use of statistical tools and imparts a clear understanding of their underlying assumptions and limitations. Without getting bogged down in proofs and derivations, thorough discussions help readers understand why the stated methods and results are reasonable. The use of the statistical software Minitab is integrated throughout the book, giving readers valuable experience with computer simulation and problem-solving techniques. The author focuses on applications and the models appropriate to each problem while emphasizing Monte Carlo methods, the Central Limit Theorem, confidence intervals, and power functions. The text assumes that readers have some degree of maturity in mathematics, but it does not require the use of calculus. This, along with its very clear explanations, generous number of exercises, and demonstrations of the extensive uses of statistics in diverse areas applications make Basic Statistical Methods and Models highly accessible to students in a wide range of disciplines.

BIOCALCULUS: CALCULUS, PROBABILITY, AND STATISTICS FOR THE LIFE SCIENCES shows students how calculus relates to biology, with a style that maintains rigor without being overly formal. The text motivates and illustrates the topics of calculus with examples drawn from many areas of biology, including genetics, biomechanics, medicine, pharmacology, physiology, ecology, epidemiology, and evolution, to name a few. Particular attention has been paid to ensuring that all applications of the mathematics are genuine, and references to the primary biological literature for many of these has been provided so that students and instructors can explore the applications in greater depth. Although the focus is on the interface between mathematics and the life sciences, the logical structure of the book is motivated by the mathematical material. Students will come away with a sound knowledge of mathematics, an understanding of the importance of mathematical arguments, and a clear understanding of how these mathematical concepts and techniques are central in the life sciences. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

Statistics for Environmental Biology and Toxicology presents and illustrates statistical methods appropriate for the analysis of environmental data obtained in biological or toxicological experiments. Beginning with basic probability and statistical inferences, this text progresses through non-linear and generalized linear models, trend testing, time-to-event data and analysis of cross-classified tabular and categorical data. For the more complex analyses, extensive examples including SAS and S-PLUS programming code are provided to assist the reader when implementing the methods in practice.

For courses in Introductory Statistics Real-world applications connect statistical concepts to everyday life. Biostatistics for the Biological and Health Sciences uses a variety of real-world applications to bring statistical theories and methods to life. Through these examples and a friendly writing style, the 2nd Edition ensures that you understand concepts and develop skills in critical thinking, technology, and communication. The result of collaboration between a biological sciences expert and the author of the #1 statistics book in the country, Biostatistics for the Biological and Health Sciences provides an excellent introduction to statistics for readers interested in the biological, life, medical, and health sciences. Also available with MyLab Statistics MyLab(tm) Statistics is an online homework, tutorial, and assessment program designed to work with this text to engage students and improve results. Within its structured environment, students practice what they learn, test their understanding, and pursue a personalized study plan that helps them absorb course material and understand difficult concepts. Note: You are purchasing a standalone product; MyLab(tm) does not come packaged with this content. Students, if interested in purchasing this title with MyLab, ask your instructor for the correct package ISBN and Course ID. Instructors, contact your Pearson representative for more information. If you would like to purchase both the physical text and MyLab, search for: 0134718011/9780134718019 Biostatistics for the Biological and Health Sciences Plus MyLab Statistics with Pearson eText -- Access Card Package Package consists of: 0134039017/9780134039015 Biostatistics for the Biological and Health Sciences 0321847997/9780321847997 MyLab Statistics Glue-in Access Card 032184839X/9780321848390 MyLab Statistics Inside Sticker for Glue-In Packages

This is the first book to show the capabilities of Microsoft Excel to teach biological and life sciences statistics effectively. It is a step-by-step exercise-driven guide for students and practitioners who need to master Excel to solve practical science problems. If understanding statistics isn’t your strongest suit, you are not especially mathematically-inclined, or if you are wary of computers, this is the right book for you. Excel, a widely available computer program for students and managers, is also an effective teaching and learning tool for quantitative analyses in science courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. However, Excel 2007 for Biological and Life Sciences Statistics: A Guide to Solving Practical Problems is the first book to capitalize on these improvements by teaching students and managers how to apply Excel to statistical techniques necessary in their courses and work. Each chapter explains statistical formulas and directs the reader to use Excel commands to solve specific, easy-to-understand science problems. Practice problems are provided at the end of each chapter with their solutions in an appendix. Separately, there is a full Practice Test (with answers in an Appendix) that allows readers to test what they have learned.

The book introduces to the reader a number of cutting edge statistical methods which can e used for the analysis of genomic, proteomic and metabolomic data sets. In particular in the field of systems biology, researchers are trying to analyze as many data as possible in a given biological system (such as a cell or an organ). The appropriate statistical evaluation of these large scale data is critical for the correct interpretation and different experimental approaches require different approaches for the statistical analysis of these data. This book is written by biostatisticians and mathematicians but aimed as a valuable guide for the experimental researcher as well computational biologists who often lack an appropriate background in statistical analysis.

Is there a link between people’s heart rate and blood pressure? Does the lead in petrol fumes affect the growth of roadside plants? The ability to expertly analyse statistical data is a crucial skill in the biological sciences – it is fundamental to fully understanding what your experiments are actually telling you and so being able to answer your research questions. Statistical and Data Handling Skills in Biology gives you everything you need to understand and use statistical tests within your studies and future independent research. Written in a straight-forward and easy to understand style it presents all of the tests you will need throughout your studies, and shows you how to select the right tests to get the most out of your experiments. All of this is done in the context of biological examples so you can see just how relevant a skill this is, and how becoming fully proficient will make you a more rounded scientist. This 4th edition has been thoroughly updated throughout and now includes detailed coverage of the free statistical package R studio and a new chapter on how to write about and present statistics in papers, theses and reports. The first chapter has also been revised to introduce students to the need for and ideas behind statistical analysis. Features · Clear explanation with step by step detail of how to carry out a wide range of statistical analyses will help you to quickly gain understanding and confidence in this essential area. · Useful decision charts will help you to select the right statistical test and gain confidence in answering your research questions. · Real world examples in each chapter will help you to develop an applied understanding of the full range of statistical techniques · Self-assessment problems scenarios at the end of each chapter enable you to practice applying your understanding of a technique, thereby improving your confidence in using numbers. Guided answers allow you to check your understanding. Statistical and Data Handling Skills in Biology 4th edition is ideal for any biomedic or environmental scientist getting to grips with statistical analysis for use in class on as part of independent study.

Instant Notes in Mathematics and Statistics for Life Scientists is aimed at undergraduate life science students who need to improve or brush-up their mathematical and statistical skills to a level which will make the quantitative components of most undergraduate biological courses accessible.

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.