Revised, expanded, and updated, this second edition of Statistics for Environmental Science and Management is that rare animal, a resource that works well as a text for graduate courses and a reference for appropriate statistical approaches to specific environmental problems. It is uncommon to find so many important environmental topics covered in one book. Its strength is author Bryan Manly’s ability to take a non-mathematical approach while keeping essential mathematical concepts intact. He clearly explains statistics without dwelling on heavy mathematical development. The book begins by describing the important role statistics play in environmental science. It focuses on how to collect data, highlighting the importance of sampling and experimental design in conducting rigorous science. It presents a variety of key topics specifically related to environmental science such as monitoring, impact assessment, risk assessment, correlated and censored data analysis, to name just a few. Revised, updated or expanded material on: Data Quality Objectives Generalized Linear Models Spatial Data Analysis Censored Data Monte Carlo Risk Assessment There are numerous books on environmental statistics; however, while some focus on multivariate methods and others on the basic components of probability distributions and how they can be used for modeling phenomenon, most do not include the material on sampling and experimental design that this one does. It is the variety of coverage, not sacrificing too much depth for breadth, that sets this book apart.

Presenting a nonmathematical approach to this topic, Statistics for Environmental Science and Management introduces frequently used statistical methods and practical applications for the environmental field. This second edition features updated references and examples along with new and expanded material on data quality objectives, the generalized linear model, spatial data analysis, and Monte Carlo risk assessment. Additional topics covered include environmental monitoring, impact assessment, censored data, environmental sampling, the role of statistics in environmental science, assessing site reclamation, and drawing conclusions from data.

The need to understand and quantify change is fundamental throughout the environmental sciences. This might involve describing past variation, understanding the mechanisms underlying observed changes, making projections of possible future change, or monitoring the effect of intervening in some environmental system. This book provides an overview of modern statistical techniques that may be relevant in problems of this nature. Practitioners studying environmental change will be familiar with many classical statistical procedures for the detection and estimation of trends. However, the ever increasing capacity to collect and process vast amounts of environmental information has led to growing awareness that such procedures are limited in the insights that they can deliver. At the same time, significant developments in statistical methodology have often been widely dispersed in the statistical literature and have therefore received limited exposure in the environmental science community. This book aims to provide a thorough but accessible review of these developments. It is split into two parts: the first provides an introduction to this area and the second part presents a collection of case studies illustrating the practical application of modern statistical approaches to the analysis of trends in real studies. Key Features: Presents a thorough introduction to the practical application and methodology of trend analysis in environmental science. Explores non-parametric estimation and testing as well as parametric techniques. Methods are illustrated using case studies from a variety of environmental application areas. Looks at trends in all aspects of a process including mean, percentiles and extremes. Supported by an accompanying website featuring datasets and R code. The book is designed to be accessible to readers with some basic statistical training, but also contains sufficient detail to serve as a reference for practising statisticians. It will therefore be of use to postgraduate students and researchers both in the environmental sciences and in statistics.

Covers the most recent topics in the field of environmental management and provides a broad focus on the theoretical and methodological underpinnings of environmental management Provides an up-to-date survey of the field from the perspective of different disciplines Covers the topic of environmental management from multiple perspectives, namely, natural sciences, engineering, business, social sciences, and methods and tools perspectives Combines both academic rigor and practical approach through literature reviews and theories and examples and case studies from diverse geographic areas and policy domains Explores local and global issues of environmental management and analyzes the role of various contributors in the environmental management process Chapter contents are appropriately demonstrated with numerous pictures, charts, graphs, and tables, and accompanied by a detailed reference list for further readings

It also describes the effects of bulking on errors and the use of ancillary information and regression to improve estimates.

Applied Statistics for Environmental Science with R presents the theory and application of statistical techniques in environmental science and aids researchers in choosing the appropriate statistical technique for analyzing their data. Focusing on the use of univariate and multivariate statistical methods, this book acts as a step-by-step resource to facilitate understanding in the use of R statistical software for interpreting data in the field of environmental science. Researchers utilizing statistical analysis in environmental science and engineering will find this book to be essential in solving their day-to-day research problems. - Includes step-by-step tutorials to aid in understanding the process and implementation of unique data - Presents statistical theory in a simple way without complex mathematical proofs - Shows how to analyze data using R software and provides R scripts for all examples and figures

Emphasizing the inductive nature of statistical thinking, Environmental and Ecological Statistics with R connects applied statistics to the environmental and ecological fields. It follows the general approach to solving a statistical modeling problem, covering model specification, parameter estimation, and model evaluation. The author uses many exa

STATISTICS IN PRACTICE A practical exploration of alternative approaches to analyzing water-related environmental issues Written by an experienced environmentalist and recognized expert in the field, this text is designed to help water resource managers and scientists to formulate, implement, and interpret more effective methods of water quality management. After presenting the basic foundation for using statistical methods in water resource management, including the use of appropriate hypothesis test procedures and some rapid calculation procedures, the author offers a range of practical problems and solutions on environmental topics that often arise, but are not generally covered. These include: * Formulating water quality standards * Determining compliance with standards * MPNs and microbiology * Water-related, human health risk modeling * Trends, impacts, concordance, and detection limits In order to promote awareness of alternative approaches to analyzing data, both frequentist and Bayesian, statistical methods are contrasted in terms of their applicability to various environmental issues. Each chapter ends with a number of set problems for which full answers are provided. The book also encourages discussion between technical staff and management before embarking on statistical studies.

This is the first book to show the capabilities of Microsoft Excel to teach environmental sciences statistics effectively. It is a step-by-step exercise-driven guide for students and practitioners who need to master Excel to solve practical environmental sciences 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 environmental science courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. However, Excel 2010 for Environmental 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 environmental 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.