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Statistical applications for environmental analysis and risk assessment /
"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, a...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, NJ :
Wiley,
2014.
|
| Edición: | First edition. |
| Colección: | Statistics in practice.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- 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 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.
- 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).
- 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.
- 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.