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008    200713s2014    xx      o     ||||0 eng d 
020    9781118634462|q(electronic bk.) 
020    |z9781118634530 
035    (MiAaPQ)EBC1687058 
035    (Au-PeEL)EBL1687058 
035    (CaPaEBR)ebr10870841 
035    (CaONFJC)MIL608497 
035    (OCoLC)880058470 
040    MiAaPQ|beng|erda|epn|cMiAaPQ|dMiAaPQ 
050  4 GE145 -- .O38 2014eb 
082 0  363.702 
100 1  Ofungwu, Joseph 
245 10 Statistical Applications for Environmental Analysis and 
       Risk Assessment 
250    1st ed 
264  1 New York :|bJohn Wiley & Sons, Incorporated,|c2014 
264  4 |cĀ©2014 
300    1 online resource (648 pages) 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
490 1  Statistics in Practice Ser 
505 0  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 
       MinitabĀ® -- 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 
505 8  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 
505 8  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 
505 8  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 
505 8  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 
505 8  13.3.2 Estimation and Hypothesis Tests of Significance for
       GLM Parameters 
520    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 
588    Description based on publisher supplied metadata and other
       sources 
590    Electronic reproduction. Ann Arbor, Michigan : ProQuest 
       Ebook Central, 2020. Available via World Wide Web. Access 
       may be limited to ProQuest Ebook Central affiliated 
       libraries 
650  0 Environmental risk assessment -- Statistical methods 
655  4 Electronic books 
776 08 |iPrint version:|aOfungwu, Joseph|tStatistical 
       Applications for Environmental Analysis and Risk 
       Assessment|dNew York : John Wiley & Sons, Incorporated,
       c2014|z9781118634530 
830  0 Statistics in Practice Ser 
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