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|a Ofungwu, Joseph.
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|a Statistical applications for environmental analysis and risk assessment /
|c Joseph Ofungwu.
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|a First edition.
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264 |
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1 |
|a Hoboken, NJ :
|b Wiley,
|c 2014.
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|a "Statistical Applications for Environmental Analysis and Risk Assessment stresses and explains the importance of a basic knowledge of statistics and statistical analysis in the environmental sciences. Emphasizing applications in such areas as hydrology, hydrogeology, contaminant hydrogeology, and water and air quality, this general-purpose environmental statistics book for students and practitioners does not assume prior familiarity with the subject. The text provides a carefully researched, readable account of statistical applications that encourages current and future environmental scientists to be more comfortable with statistics and to perform basic statistical analyses with confidence"--
|c Provided by publisher
|
520 |
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|a "The text provides a carefully researched, readable account of statistical applications that encourages current and future environmental scientists to be more comfortable with statistics and to perform basic statistical analyses with confidence"--
|c Provided by publisher
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504 |
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|a Includes bibliographical references and index.
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|a Print version record and CIP data provided by publisher.
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|a 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.
|
505 |
8 |
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|a 2.5 Kendall-Thiel Robust Line2.6 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.
|
505 |
8 |
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|a 4.3 The Concept of a Distribution4.3.1 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).
|
505 |
8 |
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|a 4.4.3.1 Student's t Distribution4.4.3.2 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.
|
505 |
8 |
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|a 5.3.1 Quantile-Quantile Plots for Comparing Two Univariate Data Samples5.3.2 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.
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546 |
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|a English.
|
590 |
|
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|a O'Reilly
|b O'Reilly Online Learning: Academic/Public Library Edition
|
650 |
|
0 |
|a Environmental risk assessment
|x Statistical methods.
|
650 |
|
6 |
|a Environnement
|x Évaluation du risque
|x Méthodes statistiques.
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650 |
|
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|a SOCIAL SCIENCE
|x Statistics.
|2 bisacsh
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650 |
|
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|a SCIENCE
|x Environmental Science.
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|a BUSINESS & ECONOMICS
|x Environmental Economics.
|2 bisacsh
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650 |
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7 |
|a Environmental risk assessment
|x Statistical methods
|2 fast
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8 |
|i Print version:
|a Ofungwu, Joseph.
|t Statistical applications for environmental analysis and risk assessment.
|b First edition.
|d Hoboken, NJ : Wiley, 2014
|z 9781118634530
|w (DLC) 2013047836
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830 |
|
0 |
|a Statistics in practice.
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