Experimental Design and Data Analysis for Biologists PDF Download

Author: Gerald Peter Quinn
Publisher: Cambridge University Press
ISBN: 9780521009768
Category : Mathematics
Languages : en
Pages : 560

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Book Description
Regression, analysis of variance, correlation, graphical.

Author: George W. Mayeske
Publisher:
ISBN:
Category : Education
Languages : en
Pages : 918

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Author: Daniel Theodore Kaplan
Publisher:
ISBN: 9780983965879
Category : Mathematical statistics
Languages : en
Pages : 388

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Book Description
"Statistical Modeling: A Fresh Approach introduces and illuminates the statistical reasoning used in modern research throughout the natural and social sciences, medicine, government, and commerce. It emphasizes the use of models to untangle and quantify variation in observed data. By a deft and concise use of computing coupled with an innovative geometrical presentation of the relationship among variables. A Fresh Approach reveals the logic of statistical inference and empowers the reader to use and understand techniques such as analysis of covariance that appear widely in published research but are hardly ever found in introductory texts."-- book cover

Author: Sébastien Da Veiga
Publisher: SIAM
ISBN: 1611976693
Category : Mathematics
Languages : en
Pages : 307

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Book Description
This book provides an overview of global sensitivity analysis methods and algorithms, including their theoretical basis and mathematical properties. The authors use a practical point of view and real case studies as well as numerous examples, and applications of the different approaches are illustrated throughout using R code to explain their usage and usefulness in practice. Basics and Trends in Sensitivity Analysis: Theory and Practice in R covers a lot of material, including theoretical aspects of Sobol’ indices as well as sampling-based formulas, spectral methods, and metamodel-based approaches for estimation purposes; screening techniques devoted to identifying influential and noninfluential inputs; variance-based measures when model inputs are statistically dependent (and several other approaches that go beyond variance-based sensitivity measures); and a case study in R related to a COVID-19 epidemic model where the full workflow of sensitivity analysis combining several techniques is presented. This book is intended for engineers, researchers, and undergraduate students who use complex numerical models and have an interest in sensitivity analysis techniques and is appropriate for anyone with a solid mathematical background in basic statistical and probability theories who develops and uses numerical models in all scientific and engineering domains.

Author: Roger Fouquet
Publisher: Edward Elgar Publishing
ISBN: 0857933698
Category : Technology & Engineering
Languages : en
Pages : 753

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Book Description
ÔSome of us have spent our professional lives on energy and climate change but any new researcher or policy maker must find it daunting to even approach the subject. If so, this encyclopedic Handbook provides a wonderful and necessary introduction. It is creative and up to date, yet also takes the reader by the hand and introduces one topic after another while also providing much of the historical context that is so necessary to a deeper understanding.Õ Ð Thomas Sterner, Environmental Defense Fund This timely Handbook reviews many key issues in the economics of energy and climate change, raising new questions and offering solutions that might help to minimize the threat of energy-induced climate change. Constructed around the objectives of displaying some of the best of current thinking in the economics of energy and climate change, this groundbreaking volume brings together many of the worldÕs leading and most innovative minds in the field to cover issues related to: ¥ fossil fuel and electricity markets ¥ environment-related energy policy ¥ international climate agreements ¥ carbon mitigation policies ¥ low carbon behaviour, growth and governance. Serving as an indispensable guide to one of the fastest growing fields of economics, this invaluable resource will strongly appeal to students, academics and policy makers interested in energy, environmental and climate change issues.

Author: Timothy Z. Keith
Publisher: Routledge
ISBN: 1351667939
Category : Education
Languages : en
Pages : 640

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Book Description
Companion Website materials: https://tzkeith.com/ Multiple Regression and Beyond offers a conceptually-oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely. This book: • Covers both MR and SEM, while explaining their relevance to one another • Includes path analysis, confirmatory factor analysis, and latent growth modeling • Makes extensive use of real-world research examples in the chapters and in the end-of-chapter exercises • Extensive use of figures and tables providing examples and illustrating key concepts and techniques New to this edition: • New chapter on mediation, moderation, and common cause • New chapter on the analysis of interactions with latent variables and multilevel SEM • Expanded coverage of advanced SEM techniques in chapters 18 through 22 • International case studies and examples • Updated instructor and student online resources

Author: Alvin C. Rencher
Publisher: John Wiley & Sons
ISBN: 0470192607
Category : Mathematics
Languages : en
Pages : 690

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Book Description
The essential introduction to the theory and application of linear models—now in a valuable new edition Since most advanced statistical tools are generalizations of the linear model, it is neces-sary to first master the linear model in order to move forward to more advanced concepts. The linear model remains the main tool of the applied statistician and is central to the training of any statistician regardless of whether the focus is applied or theoretical. This completely revised and updated new edition successfully develops the basic theory of linear models for regression, analysis of variance, analysis of covariance, and linear mixed models. Recent advances in the methodology related to linear mixed models, generalized linear models, and the Bayesian linear model are also addressed. Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, two-way models with empty cells, geometry of least squares, vector-matrix calculus, simultaneous inference, and logistic and nonlinear regression. Algebraic, geometrical, frequentist, and Bayesian approaches to both the inference of linear models and the analysis of variance are also illustrated. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models. This modern Second Edition features: New chapters on Bayesian linear models as well as random and mixed linear models Expanded discussion of two-way models with empty cells Additional sections on the geometry of least squares Updated coverage of simultaneous inference The book is complemented with easy-to-read proofs, real data sets, and an extensive bibliography. A thorough review of the requisite matrix algebra has been addedfor transitional purposes, and numerous theoretical and applied problems have been incorporated with selected answers provided at the end of the book. A related Web site includes additional data sets and SAS® code for all numerical examples. Linear Model in Statistics, Second Edition is a must-have book for courses in statistics, biostatistics, and mathematics at the upper-undergraduate and graduate levels. It is also an invaluable reference for researchers who need to gain a better understanding of regression and analysis of variance.

Author: Thomas A. Severini
Publisher: CRC Press
ISBN: 1482237016
Category : Mathematics
Languages : en
Pages : 258

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Book Description
The Most Useful Techniques for Analyzing Sports Data One of the greatest changes in the sports world in the past 20 years has been the use of mathematical methods to analyze performances, recognize trends and patterns, and predict results. Analytic Methods in Sports: Using Mathematics and Statistics to Understand Data from Baseball, Football, Basketball, and Other Sports provides a concise yet thorough introduction to the analytic and statistical methods that are useful in studying sports. The book gives you all the tools necessary to answer key questions in sports analysis. It explains how to apply the methods to sports data and interpret the results, demonstrating that the analysis of sports data is often different from standard statistical analysis. Requiring familiarity with mathematics but no previous background in statistics, the book integrates a large number of motivating sports examples throughout and offers guidance on computation and suggestions for further reading in each chapter.

Author: Gerry P. Quinn
Publisher: Cambridge University Press
ISBN: 1009453858
Category : Science
Languages : en
Pages : 409

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Book Description
Requiring only introductory statistics and basic mathematics, this textbook avoids jargon and provides worked examples, data sets and R code, and review exercises. Designed for advanced undergraduates and postgraduates studying biostatistics and experiment design in biology-related fields, it applies statistical concepts to biological scenarios.

Author: Xin Yan
Publisher: World Scientific
ISBN: 9812834109
Category : Mathematics
Languages : en
Pages : 349

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Book Description
"This volume presents in detail the fundamental theories of linear regression analysis and diagnosis, as well as the relevant statistical computing techniques so that readers are able to actually model the data using the techniques described in the book. This book is suitable for graduate students who are either majoring in statistics/biostatistics or using linear regression analysis substantially in their subject area." --Book Jacket.