Book description
Statistical Applications for Environmental Analysis and Risk Assessment guides readers through realworld situations and the best statistical methods used to determine the nature and extent of the problem, evaluate the potential human health and ecological risks, and design and implement remedial systems as necessary. Featuring numerous worked examples using actual data and "readymade" software scripts, Statistical Applications for Environmental Analysis and Risk Assessment also includes:
Descriptions of basic statistical concepts and principles in an informal style that does not presume prior familiarity with the subject
Detailed illustrations of statistical applications in the environmental and related water resources fields using realworld data in the contexts that would typically be encountered by practitioners
Software scripts using the highpowered statistical software system, R, and supplemented by USEPA's ProUCL and USDOE's VSP software packages, which are all freely available
Coverage of frequent data sample issues such as nondetects, outliers, skewness, sustained and cyclical trend that habitually plague environmental data samples
Clear demonstrations of the crucial, but often overlooked, role of statistics in environmental sampling design and subsequent exposure risk assessment.
Table of contents
 Cover
 Wiley Series in Statistics in Practice
 Title Page
 Copyright
 Dedication
 Preface
 Acknowledgments
 Chapter 1: Introduction

Part I: Basic Statistical Measures and Concepts
 Chapter 2: Introduction to Software Packages Used in This Book
 Chapter 3: Laboratory Detection Limits, Nondetects, and Data Analysis
 Chapter 4: Data Sample, Data Population, and Data Distribution
 Chapter 5: Graphics for Data Analysis and Presentation
 Chapter 6: Basic Statistical Measures: Descriptive or Summary Statistics

Part II: Statistical Procedures for Mostly Univariate Data
 Chapter 7: Statistical Intervals: Confidence, Tolerance, and Prediction Intervals

Chapter 8: Tests of Hypothesis and Decision Making
 8.1 Introduction and Overview
 8.2 Basic Terminology and Procedures for Tests of Hypothesis
 8.3 Type I and Type II Decision Errors, Statistical Power, and Interrelationships
 8.4 The Problem with Multiple Tests or Comparisons: SiteWide False Positive Error Rates
 8.5 Tests for Equality of Variance
 Exercises
 Chapter 9: Applications of Hypothesis Tests: Comparing Populations, Analysis of Variance
 Chapter 10: Trends, Autocorrelation, and Temporal Dependence

Part III: Statistical Procedures for Mostly Multivariate Data
 Chapter 11: Correlation, Covariance, Geostatistics

Chapter 12: Simple Linear Regression
 12.1 Introduction and Overview
 12.2 The Simple Linear Regression Model
 12.3 Basic Applications of Simple Linear Regression
 12.4 Verify Compliance with the Assumptions of Conventional Linear Regression
 12.5 Check the Regression Diagnostics for the Presence of Influential Data Points
 12.6 Confidence Intervals for the Predicted Y Values
 12.7 Regression for LeftCensored Data (NonDetects)
 Exercises
 Chapter 13: Data Transformation versus Generalized Linear Model
 Chapter 14: Robust Regression

Chapter 15: Multiple Linear Regression
 15.1 Introduction and Overview
 15.2 The Need for Multiple Regression
 15.3 The Multiple Linear Regression (MLR) Model
 15.4 The Estimated Multivariable X–Y Relationship Based on a Data Sample
 15.5 Assumptions of Multiple Linear Regression
 15.6 Hypothesis Tests for Reliability of the MLR Model
 15.7 Confidence Intervals for the Regression Coefficients and Predicted Y Values
 15.8 Coefficient of Multiple Correlation (R), Multiple Determination (R2), Adjusted R2, and Partial Correlation Coefficients
 15.9 Regression Diagnostics
 15.10 Model Interactions and Multiplicative Effects
 Exercises

Chapter 16: Categorical Data Analysis
 16.1 Introduction and Overview
 16.2 Types of Variables and Associated Data
 16.3 OneWay Analysis of Variance Regression Model
 16.4 TwoWay Analysis of Variance Regression Model With no Interactions
 16.5 TwoWay Analysis of Variance Regression Model With Interactions
 16.6 Analysis of Covariance Regression Model
 Exercises
 Chapter 17: Model Building: Stepwise Regression and Best Subsets Regression
 Chapter 18: Nonlinear Regression

Part IV: Statistics in Environmental Sampling Design and Risk Assessment
 Chapter 19: Data Quality Objectives and Environmental Sampling Design

Chapter 20: Determination of Background and Applications in Risk Assessment
 20.1 Introduction and Overview
 20.2 When Background Sampling is Required and When it is Not
 20.3 Background Sampling Plans
 20.4 Graphical and Quantitative Data Analysis for Site Versus Background Data Comparisons
 20.5 Determination of Exposure Point Concentration and Contaminants of Potential Concern
 Exercises
 Chapter 21: Statistics in Conventional and Probabilistic Risk Assessment
 Appendix A: Software Scripts
 Appendix B: Datasets
 References
 Answers for Exercises
 Index
 End User License Agreement
Product information
 Title: Statistical Applications for Environmental Analysis and Risk Assessment
 Author(s):
 Release date: May 2014
 Publisher(s): Wiley
 ISBN: 9781118634530
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