Environmental Statistics and Data Analysis

Description

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. Abstract: 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 Content: Cover Title Page Copyright Page Dedication Acknowledgments Preface Table of Contents 1: RANDOM PROCESSES STOCHASTIC PROCESSES IN THE ENVIRONMENT STRUCTURE OF BOOK 2: THEORY OF PROBABILITY PROBABILITY CONCEPTS PROBABILITY LAWS CONDITIONAL PROBABILITY AND BAYES' THEOREM Bayes' Theorem SUMMARY PROBLEMS 3: PROBABILITY MODELS DISCRETE PROBABILITY MODELS Geometric Distribution CONTINUOUS RANDOM VARIABLES Uniform Distribution Computer Simulation Exponential Distribution MOMENTS, EXPECTED VALUE, AND CENTRAL TENDENCY VARIANCE, KURTOSIS, AND SKEWNESS ANALYSIS OF OBSERVED DATA Computing Statistics from DataHistograms and Frequency Plots Fitting Probability Models to Environmental Data Tail Exponential Method SUMMARY PROBLEMS 4: BERNOULLI PROCESSES CONDITIONS FOR BERNOULLI PROCESS DEVELOPMENT OF MODEL Example: Number of Persons Engaged in Cigarette Smoking Development of Model by Inductive Reasoning BINOMIAL DISTRIBUTION APPLICATIONS TO ENVIRONMENTAL PROBLEMS Probability Distribution for the Number of Exceedances Robustness of Statistical Assumptions COMPUTATION OF B(n, p) PROBLEMS 5: POISSON PROCESSES CONDITIONS FOR POISSON PROCESS APPLICATIONS TO ENVIRONMENTAL PHENOMENAAir Quality Indoor Air Quality Water Quality Concentrations in Soils, Plants, and Animals Concentrations in Foods and Human Tissue Ore Deposits SUMMARY AND CONCLUSIONS PROBLEMS 9: LOGNORMAL PROCESSES CONDITIONS FOR LOGNORMAL PROCESS DEVELOPMENT OF MODEL LOGNORMAL PROBABILITY MODEL Parameters of the Lognormal Distribution Plotting the Lognormal Distribution ESTIMATING PARAMETERS OF THE LOGNORMAL DISTRIBUTION FROM DATA Visual Estimation Method of Moments Method of Quantiles Maximum Likelihood Estimation (MLE)