Environmental Statistics and Data Analysis

This easy-to-understand introduction emphasizes the areas of probability theory and statistic that are important in environmental monitoring, data analysis, research, environmental field surveys, and environmental decision making. It communicates basic statistical theory with very little abstract mathematical notation, without omitting important details and assumptions. Topics include Bayes'Theorem, geometric distribution, computer simulation, histograms and frequency plots, maximum likelihood estimation,the tail exponential method, Bernoulli processes, Poisson processes, diffusion and dispersion of pollutants, normal distribution, confidence intervals, and stochastic dilution; gamma, chi-square, and Weibull distribution; and the two-and three-parameter lognormal distributions.

This easy-to-understand introduction emphasizes the areas of probability theory and statistics that are important in environmental monitoring, data analysis, research, environmental field surveys, and environmental decision making. It communicates basic statistical theory with very little abstract mathematical notation, but without omitting important details and assumptions. Topics include Bayes' Theorem, geometric distribution, computer simulation, histograms and frequency plots, maximum likelihood estimation, the tail exponential method, Bernoulli processes, Poisson processes, diffusion and dispersion of pollutants, normal distribution, confidence intervals, and stochastic dilution; gamma, chi-square, and Weibull distributions; and the two- and three-parameter lognormal distributions. The author also presents the Statistical Theory of Rollback, which allows data analysts and regulatory officials to estimate the effect of different emission control strategies on environmental quality frequency distributions.Assuming only a basic knowledge of algebra and calculus, Environmental Statistics and Data Analysis provides an outstanding reference and collection of statistical procedures for analyzing environmental data and making accurate environmental predictions.

Random ProcessesStochastic Processes in the EnvironmentStructure of the BookTheory of ProbabilityProbability ConceptsProbability LawsConditional Probability and Bayes' TheoremSummaryProblemsProbability ModelsDiscrete Probability ModelsContinuous Random VariablesMoments, Expected Value, and Central TendencyVariance, Kurtosis, and SkewnessAnalysis of Observed DataSummaryProblemsBernoulli ProcessesConditions for Bernoulli ProcessDevelopment of ModelBinomial DistributionApplications to Environmental ProblemsComputation of B(n,p)ProblemsPoisson ProcessesConditions for Poisson ProcessDevelopment of ModelPoisson DistributionExamplesApplications to Environmental ProblemsComputation of P(l,t) ProblemsDiffusion and Dispersion of PollutantsWedge MachineParticle Frame MachinePlume ModelSummary and ConclusionsProblemsNormal ProcessesConditions for Normal ProcessDevelopment of ModelConfidence IntervalsApplications to Environmental ProblemsComputation of N(m,s) ProblemsDilution of PollutantsDeterministic DilutionStochastic DilutionApplications to Environmental ProblemsSummary and ConclusionsProblemsLognormal ProcessesConditions for Lognormal ProcessDevelopment of ModelLognormal Probability ModelEstimating Parameters of the Lognormal DistributionThree-Parameter Lognormal ModelStatistical Theory of RollbackApplications to Environmental ProblemsSummary and ConclusionsProblemsIndex

Wayne R. Ott