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.
Stochastic Processes in the Environment
Structure of the Book
Theory of Probability
Probability Concepts
Probability Laws
Conditional Probability and Bayes' Theorem
Summary
Problems
Probability Models
Discrete Probability Models
Continuous Random Variables
Moments, Expected Value, and Central Tendency
Variance, Kurtosis, and Skewness
Analysis of Observed Data
Summary
Problems
Bernoulli Processes
Conditions for Bernoulli Process
Development of Model
Binomial Distribution
Applications to Environmental Problems
Computation of B(n,p)
Problems
Poisson Processes
Conditions for Poisson Process
Development of Model
Poisson Distribution
Examples
Applications to Environmental Problems
Computation of P(l,t)
Problems
Diffusion and Dispersion of Pollutants
Wedge Machine
Particle Frame Machine
Plume Model
Summary and Conclusions
Problems
Normal Processes
Conditions for Normal Process
Development of Model
Confidence Intervals
Applications to Environmental Problems
Computation of N(m,s)
Problems
Dilution of Pollutants
Deterministic Dilution
Stochastic Dilution
Applications to Environmental Problems
Summary and Conclusions
Problems
Lognormal Processes
Conditions for Lognormal Process
Development of Model
Lognormal Probability Model
Estimating Parameters of the Lognormal Distribution
Three-Parameter Lognormal Model
Statistical Theory of Rollback
Applications to Environmental Problems
Summary and Conclusions
Problems
Index
Biography
Wayne R. Ott
"... provides a lucid explanation of how environmental processes can yield observations realized from various probability models, and hence gives better justification for their choice than empirical fit."
-Journal of the American Statistical Association
