Probability and Statistics Applications for Environmental Science

Probability and Statistics Applications for Environmental Science
Title Probability and Statistics Applications for Environmental Science PDF eBook
Author Stacey J Shaefer
Publisher CRC Press
Pages 394
Release 2007-02-22
Genre Mathematics
ISBN 0849375614

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Simple, clear, and to the point, Probability and Statistics Applications for Environmental Science delineates the fundamentals of statistics, imparting a basic understanding of the theory and mechanics of the calculations. User-friendliness, uncomplicated explanations, and coverage of example applications in the environmental field set this book apart from other textbooks on the same subject. Striking a balance between theory and applied mathematics, the material is divided into three parts. Part I sets the stage with coverage of principles and fundamentals, such as set notation, probability distributions, and the estimation of the mean and variance. Part II discusses traditional statistics applications, centering around the uses of probability distributions, including how they relate to reliability and failure theory. The authors elucidate many of the important distributions, Monte Carlo methods, and fault and event trees. Part III delves into what some have come to define as contemporary statistics. It covers hypothesis testing, Student's t and chi-square tests, regression analysis, analysis of variance (ANOVA), and nonparametric tests. The book's coverage is thorough, its presentation logical and geared to student's needs. It includes problems and solutions within the text and tables, a glossary of terms, and an introduction to design of experiments in the appendices. The authors, known for their meticulously accurate, articulate, and practical writing style, master the difficult task of explaining very complicated subject matter in a way that is easily understood. The book features a clear, concise presentation that makes both teaching and learning easier.

Probability and Statistics Applications for Environmental Science

Probability and Statistics Applications for Environmental Science
Title Probability and Statistics Applications for Environmental Science PDF eBook
Author Stacey J Shaefer
Publisher CRC Press
Pages 394
Release 2007-02-22
Genre Mathematics
ISBN 1000654060

Download Probability and Statistics Applications for Environmental Science Book in PDF, Epub and Kindle

Simple, clear, and to the point, Probability and Statistics Applications for Environmental Science delineates the fundamentals of statistics, imparting a basic understanding of the theory and mechanics of the calculations. User-friendliness, uncomplicated explanations, and coverage of example applications in the environmental field set this book ap

Environmental Risk Analysis

Environmental Risk Analysis
Title Environmental Risk Analysis PDF eBook
Author Louis Theodore
Publisher CRC Press
Pages 390
Release 2015-09-25
Genre Mathematics
ISBN 1498714404

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A Training Tool for the Environmental Risk ProfessionalEnvironmental Risk Analysis: Probability Distribution Calculations defines the role that probability distributions play in risk analysis, and gives direction on how to measure and compare the magnitude of risk more efficiently. Designed for the practicing environmental engineer and scientist, t

Statistics for Earth and Environmental Scientists

Statistics for Earth and Environmental Scientists
Title Statistics for Earth and Environmental Scientists PDF eBook
Author John H. Schuenemeyer
Publisher John Wiley & Sons
Pages 341
Release 2011-04-12
Genre Mathematics
ISBN 1118102215

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A comprehensive treatment of statistical applications for solving real-world environmental problems A host of complex problems face today's earth science community, such as evaluating the supply of remaining non-renewable energy resources, assessing the impact of people on the environment, understanding climate change, and managing the use of water. Proper collection and analysis of data using statistical techniques contributes significantly toward the solution of these problems. Statistics for Earth and Environmental Scientists presents important statistical concepts through data analytic tools and shows readers how to apply them to real-world problems. The authors present several different statistical approaches to the environmental sciences, including Bayesian and nonparametric methodologies. The book begins with an introduction to types of data, evaluation of data, modeling and estimation, random variation, and sampling—all of which are explored through case studies that use real data from earth science applications. Subsequent chapters focus on principles of modeling and the key methods and techniques for analyzing scientific data, including: Interval estimation and Methods for analyzinghypothesis testing of means time series data Spatial statistics Multivariate analysis Discrete distributions Experimental design Most statistical models are introduced by concept and application, given as equations, and then accompanied by heuristic justification rather than a formal proof. Data analysis, model building, and statistical inference are stressed throughout, and readers are encouraged to collect their own data to incorporate into the exercises at the end of each chapter. Most data sets, graphs, and analyses are computed using R, but can be worked with using any statistical computing software. A related website features additional data sets, answers to selected exercises, and R code for the book's examples. Statistics for Earth and Environmental Scientists is an excellent book for courses on quantitative methods in geology, geography, natural resources, and environmental sciences at the upper-undergraduate and graduate levels. It is also a valuable reference for earth scientists, geologists, hydrologists, and environmental statisticians who collect and analyze data in their everyday work.

EnvStats

EnvStats
Title EnvStats PDF eBook
Author Steven P. Millard
Publisher Springer Science & Business Media
Pages 305
Release 2013-10-16
Genre Computers
ISBN 1461484561

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This book describes EnvStats, a new comprehensive R package for environmental statistics and the successor to the S-PLUS module EnvironmentalStats for S-PLUS (first released in 1997). EnvStats and R provide an open-source set of powerful functions for performing graphical and statistical analyses of environmental data, bringing major environmental statistical methods found in the literature and regulatory guidance documents into one statistical package, along with an extensive hypertext help system that explains what these methods do, how to use these methods, and where to find them in the environmental statistics literature. EnvStats also includes numerous built-in data sets from regulatory guidance documents and the environmental statistics literature. This book shows how to use EnvStats and R to easily: * graphically display environmental data * plot probability distributions * estimate distribution parameters and construct confidence intervals on the original scale for commonly used distributions such as the lognormal and gamma, as well as do this nonparametrically * estimate and construct confidence intervals for distribution percentiles or do this nonparametrically (e.g., to compare to an environmental protection standard) * perform and plot the results of goodness-of-fit tests * compute optimal Box-Cox data transformations * compute prediction limits and simultaneous prediction limits (e.g., to assess compliance at multiple sites for multiple constituents) * perform nonparametric estimation and test for seasonal trend (even in the presence of correlated observations) * perform power and sample size computations and create companion plots for sampling designs based on confidence intervals, hypothesis tests, prediction intervals, and tolerance intervals * deal with non-detect (censored) data * perform Monte Carlo simulation and probabilistic risk assessment * reproduce specific examples in EPA guidance documents EnvStats combined with other R packages (e.g., for spatial analysis) provides the environmental scientist, statistician, researcher, and technician with tools to “get the job done!”

Statistical Methods for Climate Scientists

Statistical Methods for Climate Scientists
Title Statistical Methods for Climate Scientists PDF eBook
Author Timothy DelSole
Publisher Cambridge University Press
Pages 545
Release 2022-02-24
Genre Mathematics
ISBN 1108472419

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An accessible introduction to statistical methods for students in the climate sciences.

Nonparametric Statistics with Applications to Science and Engineering

Nonparametric Statistics with Applications to Science and Engineering
Title Nonparametric Statistics with Applications to Science and Engineering PDF eBook
Author Paul H. Kvam
Publisher John Wiley & Sons
Pages 448
Release 2007-08-24
Genre Mathematics
ISBN 9780470168691

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A thorough and definitive book that fully addresses traditional and modern-day topics of nonparametric statistics This book presents a practical approach to nonparametric statistical analysis and provides comprehensive coverage of both established and newly developed methods. With the use of MATLAB, the authors present information on theorems and rank tests in an applied fashion, with an emphasis on modern methods in regression and curve fitting, bootstrap confidence intervals, splines, wavelets, empirical likelihood, and goodness-of-fit testing. Nonparametric Statistics with Applications to Science and Engineering begins with succinct coverage of basic results for order statistics, methods of categorical data analysis, nonparametric regression, and curve fitting methods. The authors then focus on nonparametric procedures that are becoming more relevant to engineering researchers and practitioners. The important fundamental materials needed to effectively learn and apply the discussed methods are also provided throughout the book. Complete with exercise sets, chapter reviews, and a related Web site that features downloadable MATLAB applications, this book is an essential textbook for graduate courses in engineering and the physical sciences and also serves as a valuable reference for researchers who seek a more comprehensive understanding of modern nonparametric statistical methods.