Environmental Statistics: Methods and ApplicationsIn modern society, we are ever more aware of the environmentalissues we face, whether these relate to global warming, depletionof rivers and oceans, despoliation of forests, pollution of land,poor air quality, environmental health issues, etc. At the mostfundamental level it is necessary to monitor what is happening inthe environment - collecting data to describe the changingscene. More importantly, it is crucial to formally describe theenvironment with sound and validated models, and to analyse andinterpret the data we obtain in order to take action. Environmental Statistics provides a broad overview of thestatistical methodology used in the study of the environment,written in an accessible style by a leading authority on thesubject. It serves as both a textbook for students of environmentalstatistics, as well as a comprehensive source of reference foranyone working in statistical investigation of environmentalissues. * Provides broad coverage of the methodology used in thestatistical investigation of environmental issues. * Covers a wide range of key topics, including sampling, methodsfor extreme data, outliers and robustness, relationship models andmethods, time series, spatial analysis, and environmentalstandards. * Includes many detailed practical and worked examples thatillustrate the applications of statistical methods in environmentalissues. * Authored by a leading authority on environmentalstatistics. |
Contents
| 1 | |
| 21 | |
PART II COLLECTING ENVIRONMENTAL DATA SAMPLING AND MONITORING | 75 |
PART III EXAMINING ENVIRONMENTAL EFFECTS STIMULUSRESPONSE RELATIONSHIPS | 151 |
PART IV STANDARDS AND REGULATIONS | 189 |
PART V A MANYDIMENSIONAL ENVIRONMENT SPATIAL AND TEMPORAL PROCESSES | 203 |
References | 267 |
Index | 285 |
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Common terms and phrases
Analysis applications approach appropriate assumed Barnett and Lewis basic Chapter characteristics components composite sample consider contamination correlation covariance Cressie detail discussed distance dose effects efficiency Environmental Statistics error examine example exponential distribution extreme Figure finite population function independent inference interest kriging linear model linear regression matrix maximum likelihood measures models and methods multivariate normal distribution observations obtain order statistics outlier parameters plot point process Poisson process pollution levels principle probability problem proportion quantile random variable ranked-set sampling ratio estimator regression relationship relevant residuals response variable robust sample mean sample values Section simple random sample spatial specific standard stimulus Stochastic Stochastic Processes stratified sample structure sunspot Suppose SVIS test of discordancy time-series tion transect line transformation trend unbiased estimator univariate upper outlier variation variogram Weibull distribution Xk i¼1 yields
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References to this book
Organic Phosphorus in the Environment Benjamin L. Turner,Emmanuel Frossard,Darren S. Baldwin No preview available - 2005 |