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Statistical Applications for Environmental Analysis and Risk Assessment

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

    1. Cover
    2. Wiley Series in Statistics in Practice
    3. Title Page
    4. Copyright
    5. Dedication
    6. Preface
    7. Acknowledgments
    8. Chapter 1: Introduction
      1. 1.1 Introduction and Overview
      2. 1.2 The Aim of the Book: Get Involved!
      3. 1.3 The Approach and Style: Clarity, Clarity, Clarity
    9. Part I: Basic Statistical Measures and Concepts
      1. Chapter 2: Introduction to Software Packages Used in This Book
        1. 2.1 R
        2. 2.2 ProUCL
        3. 2.3 Visual Sample Plan
        4. 2.4 DATAPLOT
        5. 2.5 Kendall–Thiel Robust Line
        6. 2.6 Minitab®
        7. 2.7 Microsoft Excel
      2. Chapter 3: Laboratory Detection Limits, Nondetects, and Data Analysis
        1. 3.1 Introduction and Overview
        2. 3.2 Types of Laboratory Data Detection Limits
        3. 3.3 Problems with Nondetects in Statistical Data Samples
        4. 3.4 Options for Addressing Nondetects in Data Analysis
      3. Chapter 4: Data Sample, Data Population, and Data Distribution
        1. 4.1 Introduction and Overview
        2. 4.2 Data Sample Versus Data Population or Universe
        3. 4.3 The Concept of a Distribution
        4. 4.4 Types of Distributions
        5. Exercises
      4. Chapter 5: Graphics for Data Analysis and Presentation
        1. 5.1 Introduction and Overview
        2. 5.2 Graphics for Single Univariate Data Samples
        3. 5.3 Graphics for two or More Univariate Data Samples
        4. 5.4 Graphics for Bivariate and Multivariate Data Samples
        5. 5.5 Graphics for Data Presentation
        6. 5.6 Data Smoothing
        7. Exercises
      5. Chapter 6: Basic Statistical Measures: Descriptive or Summary Statistics
        1. 6.1 Introduction And Overview
        2. 6.2 Arithmetic Mean and Weighted Mean
        3. 6.3 Median and Other Robust Measures of Central Tendency
        4. 6.4 Standard Deviation, Variance, and Other Measures of Dispersion or Spread
        5. 6.5 Skewness and Other Measures of Shape
        6. 6.6 Outliers
        7. 6.7 Data Transformations
        8. Exercises
    10. Part II: Statistical Procedures for Mostly Univariate Data
      1. Chapter 7: Statistical Intervals: Confidence, Tolerance, and Prediction Intervals
        1. 7.1 Introduction and Overview
        2. 7.2 Confidence Intervals
        3. 7.3 Tolerance Intervals
        4. 7.4 Prediction Intervals
        5. 7.5 Control Charts
        6. Exercises
      2. Chapter 8: Tests of Hypothesis and Decision Making
        1. 8.1 Introduction and Overview
        2. 8.2 Basic Terminology and Procedures for Tests of Hypothesis
        3. 8.3 Type I and Type II Decision Errors, Statistical Power, and Interrelationships
        4. 8.4 The Problem with Multiple Tests or Comparisons: Site-Wide False Positive Error Rates
        5. 8.5 Tests for Equality of Variance
        6. Exercises
      3. Chapter 9: Applications of Hypothesis Tests: Comparing Populations, Analysis of Variance
        1. 9.1 Introduction and Overview
        2. 9.2 Single Sample Tests
        3. 9.3 Two-Sample Tests
        4. 9.4 Comparing Three or More Populations: Parametric Anova and Nonparametric Kruskal–Wallis Tests
        5. Exercises
      4. Chapter 10: Trends, Autocorrelation, and Temporal Dependence
        1. 10.1 Introduction and Overview
        2. 10.2 Tests for Autocorrelation and Temporal Effects
        3. 10.3 Tests for Trend
        4. 10.4 Correcting Seasonality and Temporal Effects in the Data
        5. 10.5 Effects of Exogenous Variables on Trend Tests
        6. Exercises
    11. Part III: Statistical Procedures for Mostly Multivariate Data
      1. Chapter 11: Correlation, Covariance, Geostatistics
        1. 11.1 Introduction and Overview
        2. 11.2 Correlation and Covariance
        3. 11.3 Introduction to Geostatistics
        4. Exercises
      2. Chapter 12: Simple Linear Regression
        1. 12.1 Introduction and Overview
        2. 12.2 The Simple Linear Regression Model
        3. 12.3 Basic Applications of Simple Linear Regression
        4. 12.4 Verify Compliance with the Assumptions of Conventional Linear Regression
        5. 12.5 Check the Regression Diagnostics for the Presence of Influential Data Points
        6. 12.6 Confidence Intervals for the Predicted Y Values
        7. 12.7 Regression for Left-Censored Data (Non-Detects)
        8. Exercises
      3. Chapter 13: Data Transformation versus Generalized Linear Model
        1. 13.1 Introduction and Overview
        2. 13.2 Data Transformation
        3. 13.3 The Generalized Linear Model (GLM) and Applications for Regression
        4. 13.4 Extension of Data Transformation and Generalized Linear Model to Multiple Regression
        5. Exercises
      4. Chapter 14: Robust Regression
        1. 14.1 Introduction and Overview
        2. 14.2 Kendall–Theil Robust Line
        3. 14.3 Weighted Least Squares Regression
        4. 14.4 Iteratively Reweighted Least Squares Regression
        5. 14.5 Other Robust Regression Alternatives: Bounded Influence Methods
        6. 14.6 Robust Regression Methods for Multiple-Variable Data
        7. Exercises
      5. Chapter 15: Multiple Linear Regression
        1. 15.1 Introduction and Overview
        2. 15.2 The Need for Multiple Regression
        3. 15.3 The Multiple Linear Regression (MLR) Model
        4. 15.4 The Estimated Multivariable X–Y Relationship Based on a Data Sample
        5. 15.5 Assumptions of Multiple Linear Regression
        6. 15.6 Hypothesis Tests for Reliability of the MLR Model
        7. 15.7 Confidence Intervals for the Regression Coefficients and Predicted Y Values
        8. 15.8 Coefficient of Multiple Correlation (R), Multiple Determination (R2), Adjusted R2, and Partial Correlation Coefficients
        9. 15.9 Regression Diagnostics
        10. 15.10 Model Interactions and Multiplicative Effects
        11. Exercises
      6. Chapter 16: Categorical Data Analysis
        1. 16.1 Introduction and Overview
        2. 16.2 Types of Variables and Associated Data
        3. 16.3 One-Way Analysis of Variance Regression Model
        4. 16.4 Two-Way Analysis of Variance Regression Model With no Interactions
        5. 16.5 Two-Way Analysis of Variance Regression Model With Interactions
        6. 16.6 Analysis of Covariance Regression Model
        7. Exercises
      7. Chapter 17: Model Building: Stepwise Regression and Best Subsets Regression
        1. 17.1 Introduction and Overview
        2. 17.2 Consequences of Inappropriate Variable Selection
        3. 17.3 Stepwise Regression Procedures
        4. 17.4 Subsets Regression
        5. Exercises
      8. Chapter 18: Nonlinear Regression
        1. 18.1 Introduction and Overview
        2. 18.2 The Nonlinear Regression Model
        3. 18.3 Assumptions of Nonlinear Least Squares Regression
        4. Discussion of Results
        5. Exercises
    12. Part IV: Statistics in Environmental Sampling Design and Risk Assessment
      1. Chapter 19: Data Quality Objectives and Environmental Sampling Design
        1. 19.1 Introduction and Overview
        2. 19.2 Sampling Design
        3. 19.3 Sampling Plans
        4. 19.4 Sample Size Determination
        5. Exercises
      2. Chapter 20: Determination of Background and Applications in Risk Assessment
        1. 20.1 Introduction and Overview
        2. 20.2 When Background Sampling is Required and When it is Not
        3. 20.3 Background Sampling Plans
        4. 20.4 Graphical and Quantitative Data Analysis for Site Versus Background Data Comparisons
        5. 20.5 Determination of Exposure Point Concentration and Contaminants of Potential Concern
        6. Exercises
      3. Chapter 21: Statistics in Conventional and Probabilistic Risk Assessment
        1. 21.1 Introduction and Overview
        2. 21.2 Conventional or Point Risk Estimation
        3. 21.3 Probabilistic Risk Assessment Using Monte Carlo Simulation
        4. Exercises
    13. Appendix A: Software Scripts
    14. Appendix B: Datasets
    15. References
    16. Answers for Exercises
    17. Index
    18. End User License Agreement