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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 |c©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
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
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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