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Author Ofungwu, Joseph
Title Statistical Applications for Environmental Analysis and Risk Assessment
Imprint New York : John Wiley & Sons, Incorporated, 2014
©2014
book jacket
Edition 1st ed
Descript 1 online resource (648 pages)
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
Series Statistics in Practice Ser
Statistics in Practice Ser
Note Statistical Applications for Environmental Analysis and Risk Assessment -- Contents -- Preface -- Acknowledgements -- 1. Introduction -- 1.1 Introduction and Overview -- 1.2 The Aim of the Book: Get Involved! -- 1.3 The Approach and Style: Clarity, Clarity, Clarity -- Part I: Basic Statistical Measures and Concepts -- 2. Introduction to Software Packages used in this Book -- 2.1 R -- 2.1.1 Helpful R Tips -- 2.1.2 Disadvantages of R -- 2.2 ProUCL -- 2.2.1 Helpful ProUCL Tips -- 2.2.2 Potential Deficiencies of ProUCL -- 2.3 Visual Sample Plan -- 2.4 DATAPLOT -- 2.4.1 Helpful Tips for Running DATAPLOT in Batch Mode -- 2.5 Kendall-Thiel Robust Line -- 2.6 Minitab® -- 2.7 Microsoft Excel -- 3. Laboratory Detection Limits, Non-Detects and Data Analysis -- 3.1 Introduction and Overview -- 3.2 Types of Laboratory Data Detection Limits -- 3.3 Problems with Nondetects in Statistical Data Samples -- 3.4 Options for Addressing Nondetects in Data Analysis -- 3.4.1 Kaplan-Meier Estimation -- 3.4.2 Robust Regression on Order Statistics -- 3.4.3 Maximum Likelihood Estimation -- 4. Data Sample, Data Population and Data Distribution -- 4.1 Introduction and Overview -- 4.2 Data Sample Versus Data Population or Universe -- 4.3 The Concept of a Distribution -- 4.3.1 The Concept of a Probability Distribution Function -- 4.3.2 Cumulative Probability Distribution and Empirical Cumulative Distribution Functions -- 4.4 Types of Distributions -- 4.4.1 Normal Distribution -- 4.4.1.1 Goodness-of-Fit (GOF) Tests for the Normal Distribution -- 4.4.1.2 Central Limit Theorem -- 4.4.2 Lognormal, Gamma, and Other Continuous Distributions -- 4.4.2.1 Gamma Distribution -- 4.4.2.2 Logistic Distribution -- 4.4.2.3 Other Continuous Distributions -- 4.4.3 Distributions Used in Inferential Statistics (Student's t, Chi-Square, F) -- 4.4.3.1 Student's t Distribution
4.4.3.2 Chi-Square Distribution -- 4.4.3.3 F Distribution -- 4.4.4 Discrete Distributions -- 4.4.4.1 Binomial Distribution -- 4.4.4.2 Poisson Distribution -- Exercises -- 5. Graphics for Data Analysis and Presentation -- 5.1 Introduction and Overview -- 5.2 Graphics for Single Univariate Data Samples -- 5.2.1 Box and Whiskers Plot -- 5.2.2 Probability Plots (i.e., Quantile-Quantile Plots for Comparing a Data Sample to a Theoretical Distribution) -- 5.2.3 Quantile Plots -- 5.2.4 Histograms and Kernel Density Plots -- 5.3 Graphics for Two or More Univariate Data Samples -- 5.3.1 Quantile-Quantile Plots for Comparing Two Univariate Data Samples -- 5.3.2 Side-by-Side Box Plots -- 5.4 Graphics for Bivariate and Multivariate Data Samples -- 5.4.1 Graphical Data Analysis for Bivariate Data Samples -- 5.4.2 Graphical Data Analysis for Multivariate Data Samples -- 5.5 Graphics for Data Presentation -- 5.6 Data Smoothing -- 5.6.1 Moving Average and Moving Median Smoothing -- 5.6.2 Locally Weighted Scatterplot Smoothing (LOWESS or LOESS) -- 5.6.2.1 Smoothness Factor and the Degree of the Local Regression -- 5.6.2.2 Basic and Robust LOWESS Weighting Functions -- 5.6.2.3 LOESS Scatterplot Smoothing for Data with Multiple Variables -- Exercises -- 6. Basic Statistical Measures: Descriptive or Summary Statistics -- 6.1 Introduction and Overview -- 6.2 Arithmetic Mean and Weighted Mean -- 6.3 Median and Other Robust Measures of Central Tendency -- 6.4 Standard Deviation, Variance, and Other Measures of Dispersion or Spread -- 6.4.1 Quantiles (Including Percentiles) -- 6.4.2 Robust Measures of Spread: Interquartile Range and Median Absolute Deviation -- 6.5 Skewness and Other Measures of Shape -- 6.6 Outliers -- 6.6.1 Tests for Outliers -- 6.7 Data Transformations -- Exercises -- Part II: Statistical Procedures for Univariate Data
7. Statistical Intervals: Confidence, Tolerance and Prediction Intervals -- 7.1 Introduction and Overview -- 7.2 Confidence Intervals -- 7.2.1 Parametric Confidence Intervals -- 7.2.1.1 Parametric Confidence Interval around the Arithmetic Mean or Median for Normally Distributed Data -- 7.2.1.2 Lognormal and Other Parametric Confidence Intervals -- 7.2.2 Nonparametric Confidence Intervals Around the Mean, Median, and Other Percentiles -- 7.2.3 Parametric Confidence Band Around a Trend Line -- 7.2.4 Nonparametric Confidence Band Around a Trend Line -- 7.3 Tolerance Intervals -- 7.3.1 Parametric Tolerance Intervals -- 7.3.2 Nonparametric Tolerance Intervals -- 7.4 Prediction Intervals -- 7.4.1 Parametric Prediction Intervals for Future Individual Values and Future Means -- 7.4.2 Nonparametric Prediction Intervals for Future Individual Values and Future Medians -- 7.5 Control Charts -- Exercises -- 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 -- 9. Applications of Hypothesis Tests: Comparing Populations, Analysis of Variance -- 9.1 Introduction and Overview -- 9.2 Single Sample Tests -- 9.2.1 Parametric Single-Sample Tests: One-Sample t-Test and One-Sample Proportion Test -- 9.2.2 Nonparametric Single-Sample Tests: One-Sample Sign Test and One-Sample Wilcoxon Signed Rank Test -- 9.2.2.1 Nonparametric One-Sample Sign Test -- 9.2.2.2 Nonparametric One-Sample Wilcoxon Signed Rank Test -- 9.3 Two-Sample Tests -- 9.3.1 Parametric Two-Sample Tests -- 9.3.1.1 Parametric Two-Sample t-Test for Independent Populations
9.3.1.2 Parametric Two-Sample t-Test for Paired Populations -- 9.3.2 Nonparametric Two-Sample Tests -- 9.3.2.1 Nonparametric Wilcoxon Rank Sum Test for Two Independent Populations -- 9.3.2.2 Nonparametric Gehan Test for Two Independent Populations -- 9.3.2.3 Nonparametric Quantile Test for Two Independent Populations -- 9.3.2.4 Nonparametric Two-Sample Paired Sign Test and Paired Wilcoxon Signed Rank Test -- 9.4 Comparing Three or More Populations: Parametric ANOVA and Nonparametric Kruskal-Wallis Tests -- 9.4.1 Parametric One-Way ANOVA -- 9.4.1.1 Computation of Parametric One-Way ANOVA -- 9.4.2 Nonparametric One-Way ANOVA (Kruskal-Wallis Test) -- 9.4.3 Follow-Up or Post Hoc Comparisons After Parametric and Nonparametric One-Way ANOVA -- 9.4.4 ParametricandNonparametricTwo-WayandMultifactorANOVA -- Exercises -- 10. Trends, Autocorrelation and Temporal Dependence -- 10.1 Introduction and Overview -- 10.2 Tests for Autocorrelation and Temporal Effects -- 10.2.1 Test for Autocorrelation Using the Sample Autocorrelation Function -- 10.2.2 Test for Autocorrelation Using the Rank Von Neumann Ratio Method -- 10.2.3 An Example on Site-Wide Temporal Effects -- 10.3 Tests for Trend -- 10.3.1 Parametric Test for Trends-Simple Linear Regression -- 10.3.2 Nonparametric Test for Trends-Mann-Kendall Test and Seasonal Mann-Kendall Test -- 10.3.3 Nonparametric Test for Trends-Theil-Sen Trend Test -- 10.4 Correcting Seasonality and Temporal Effects in the Data -- 10.4.1 Correcting Seasonality for a Single Data Series -- 10.4.2 Simultaneously Correcting Temporal Dependence for Multiple Data Sets -- 10.5 Effects of Exogenous Variables on Trend Tests -- Exercises -- Part III: Statistical Procedures for Mostly Multivariate Data -- 11. Correlation, Covariance, Geostatistics -- 11.1 Introduction and Overview -- 11.2 Correlation and Covariance
11.2.1 Pearson's Correlation Coefficient -- 11.2.2 Spearman's and Kendall's Correlation Coefficients -- 11.3 Introduction to Geostatistics -- 11.3.1 The Variogram or Covariogram -- 11.3.2 Kriging -- 11.3.3 A Note on Data Sample Size and Lag Distance Requirements -- Exercises -- 12. Simple Linear Regression -- 12.1 Introduction and Overview -- 12.2 The Simple Linear Regression Model -- 12.2.1 The True or Population X-Y Relationship -- 12.2.2 The Estimated X-Y Relationship Based on a Data Sample -- 12.3 Basic Applications of Simple Linear Regression -- 12.3.1 Description and Graphical Review of the Data Sample for Regression -- 12.3.1.1 Computing the Regression -- 12.3.1.2 Interpreting the Regression Results -- 12.4 Verify Compliance with the Assumptions of Conventional Linear Regression -- 12.4.1 Assumptions of Linearity and Homoscedasticity -- 12.4.2 Assumption of Independence -- 12.4.3 Exogeneity Assumption, Normality of the Y Errors, and Absence of Outliers -- 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 -- 13. Data Transformation versus Generalized Linear Model -- 13.1 Introduction and Overview -- 13.2 Data Transformation -- 13.2.1 General Approach for Data Transformations -- 13.2.2 The Ladder of Powers -- 13.2.3 The Bulging Rule and Data Transformations for Regression Analysis -- 13.2.4 Facilitating Data Transformations Using Box-Cox Methods -- 13.2.5 Back-Transformation Bias and Other Issues with Data Transformation -- 13.2.5.1 Logarithmic Transformations -- 13.2.5.2 Other Transformations -- 13.2.6 Transformation Bias Correction -- 13.3 The Generalized Linear Model (GLM) and Applications for Regression -- 13.3.1 Components of the Generalized Linear Model and Inherent Limitations
13.3.2 Estimation and Hypothesis Tests of Significance for GLM Parameters
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
Description based on publisher supplied metadata and other sources
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2020. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries
Link Print version: Ofungwu, Joseph Statistical Applications for Environmental Analysis and Risk Assessment New York : John Wiley & Sons, Incorporated,c2014 9781118634530
Subject Environmental risk assessment -- Statistical methods
Electronic books
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