Book description
Statistical Applications for Environmental Analysis and Risk Assessment guides readers through real-world 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 "ready-made" 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 real-world data in the contexts that would typically be encountered by practitioners
Software scripts using the high-powered 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 non-detects, 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: Site-Wide 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 Left-Censored Data (Non-Detects)
- 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 One-Way Analysis of Variance Regression Model
- 16.4 Two-Way Analysis of Variance Regression Model With no Interactions
- 16.5 Two-Way 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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