| Description |
1 online resource (xvi, 291 pages) : illustrations (some color) |
| Contents |
Machine generated contents note: 1. Getting Started -- 1.1. Introduction -- 1.2. What Is Environmental Statistics? -- 1.3. What Is EnvStats? -- 1.4. Intended Audience and Users -- 1.5. System Requirements -- 1.6. Installing EnvStats -- 1.7. Starting EnvStats -- 1.8. Getting Help and Using Companion Scripts -- 1.9. Note About Examples and Masking -- 1.10. Unloading EnvStats -- 1.11. Tutorial -- 1.11.1. TcCB Data -- 1.11.2. Computing Summary Statistics -- 1.11.3. Looking at the TcCB Data -- 1.11.4. Quantile (Empirical CDF) Plots -- 1.11.5. Assessing Goodness-of-Fit with Quantile-Quantile Plots -- 1.11.6. Estimating Distribution Parameters -- 1.11.7. Testing for Goodness of Fit -- 1.11.8. Estimating Quantiles and Computing Confidence Limits -- 1.11.9. Comparing Two Distributions Using Nonparametric Tests -- 1.12. Summary -- 2. Designing a Sampling Program -- 2.1. Introduction -- 2.2. Necessity of a Good Sampling Design -- 2.3. What Is a Population and What Is a Sample? -- 2.4. Random Versus Judgment Sampling -- 2.5. Common Mistakes in Environmental Studies -- 2.6. Data Quality Objectives Process -- 2.7. Power and Sample Size Calculations -- 2.8. Sample Size for Confidence Intervals -- 2.8.1. Confidence Interval for the Mean of a Normal Distribution -- 2.8.2. Confidence Interval for a Binomial Proportion -- 2.8.3. Nonparametric Confidence Interval for a Percentile -- 2.9. Sample Size for Prediction Intervals -- 2.9.1. Prediction Interval for a Normal Distribution -- 2.9.2. Nonparametric Prediction Interval -- 2.10. Sample Size for Tolerance Intervals -- 2.10.1. Tolerance Interval for a Normal Distribution -- 2.10.2. Nonparametric Tolerance Interval -- 2.11. Sample Size and Power for Hypothesis Tests -- 2.11.1. Testing the Mean of a Normal Distribution -- 2.11.2. Testing a Binomial Proportion -- 2.11.3. Testing Multiple Wells for Compliance with Simultaneous Prediction Intervals -- 2.12. Summary -- 3. Looking at Data -- 3.1. Introduction -- 3.2. EDA Using EnvStats -- 3.3. Summary Statistics -- 3.3.1. Summary Statistics for TcCB Concentrations -- 3.4. Strip Charts -- 3.5. Empirical PDF Plots -- 3.6. Quantile (Empirical CDF) Plots -- 3.6.1. Empirical CDFs for the TcCB Data -- 3.7. Probability Plots or Quantile-Quantile (Q-Q) Plots -- 3.7.1. Q-Q Plots for the Normal and Lognormal Distribution -- 3.7.2. Q-Q Plots for Other Distributions -- 3.7.3. Using Q-Q Plots to Compare Two Data Sets -- 3.7.4. Building an Internal Gestalt for Q-Q Plots -- 3.8. Box-Cox Data Transformations and Q-Q Plots -- 3.9. Summary -- 4. Probability Distributions -- 4.1. Introduction -- 4.2. Probability Density Function (PDF) -- 4.2.1. Probability Density Function for Lognormal Distribution -- 4.2.2. Probability Density Function for a Gamma Distribution -- 4.3. Cumulative Distribution Function (CDF) -- 4.3.1. Cumulative Distribution Function for Lognormal Distribution -- 4.4. Quantiles and Percentiles -- 4.4.1. Quantiles for Lognormal Distribution -- 4.5. Generating Random Numbers -- 4.5.1. Generating Random Numbers from a Univariate Distribution -- 4.5.2. Generating Multivariate Normal Random Numbers -- 4.5.3. Generating Multivariate Observations Based on Rank Correlations -- 4.6. Summary -- 5. Estimating Distribution Parameters and Quantiles -- 5.1. Introduction -- 5.2. Estimating Distribution Parameters -- 5.2.1. Estimating Parameters of a Normal Distribution -- 5.2.2. Estimating Parameters of a Lognormal Distribution -- 5.2.3. Estimating Parameters of a Gamma Distribution -- 5.2.4. Estimating the Parameter of a Binomial Distribution -- 5.3. Estimating Distribution Quantiles -- 5.3.1. Estimating Quantiles of a Normal Distribution -- 5.3.2. Estimating Quantiles of a Lognormal Distribution -- 5.3.3. Estimating Quantiles of a Gamma Distribution -- 5.3.4. Nonparametric Estimates of Quantiles -- 5.4. Summary -- 6. Prediction and Tolerance Intervals -- 6.1. Introduction -- 6.2. Prediction Intervals -- 6.2.1. Prediction Intervals for a Normal Distribution -- 6.2.2. Prediction Intervals for a Lognormal Distribution -- 6.2.3. Prediction Intervals for a Gamma Distribution -- 6.2.4. Nonparametric Prediction Intervals -- 6.3. Simultaneous Prediction Intervals -- 6.3.1. Simultaneous Prediction Intervals for a Normal Distribution -- 6.3.2. Simultaneous Prediction Intervals for a Lognormal Distribution -- 6.3.3. Simultaneous Prediction Intervals for a Gamma Distribution -- 6.3.4. Simultaneous Nonparametric Prediction Intervals -- 6.4. Tolerance Intervals -- 6.4.1. Tolerance Intervals for a Normal Distribution -- 6.4.2. Tolerance Intervals for a Lognormal Distribution -- 6.4.3. Tolerance Intervals for a Gamma Distribution -- 6.4.4. Nonparametric Tolerance Intervals -- 6.5. Summary -- 7. Hypothesis Tests -- 7.1. Introduction -- 7.2. Goodness-of-Fit Tests -- 7.2.1. One-Sample Goodness-of-Fit Tests for Normality -- 7.2.2. Testing Several Groups for Normality -- 7.2.3. One-Sample Goodness-of-Fit Tests for Other Distributions -- 7.2.4. Two-Sample Goodness-of-Fit Test to Compare Samples -- 7.3. One-, Two-, and k-Sample Comparison Tests -- 7.3.1. Two- and k-Sample Comparisons for Location -- 7.3.2. Chen's Modified One-Sample t-Test for Skewed Data -- 7.3.3. Two-Sample Linear Rank Tests and the Quantile Test -- 7.4. Testing for Serial Correlation -- 7.5. Testing for Trend -- 7.5.1. Testing for Trend in the Presence of Seasons -- 7.6. Summary -- 8. Censored Data -- 8.1. Introduction -- 8.2. Classification of Censored Data -- 8.3. Functions for Censored Data -- 8.4. Graphical Assessment of Censored Data -- 8.4.1. Quantile (Empirical CDF) Plots for Censored Data -- 8.4.2. Comparing an Empirical and Hypothesized CDF -- 8.4.3. Comparing Two Empirical CDFs -- 8.4.4. Q-Q Plots for Censored Data -- 8.4.5. Box-Cox Transformations for Censored Data -- 8.5. Estimating Distribution Parameters -- 8.5.1. Normal and Lognormal Distribution -- 8.5.2. Lognormal Distribution (Original Scale) -- 8.5.3. Gamma Distribution -- 8.5.4. Estimating the Mean Nonparametrically -- 8.6. Estimating Distribution Quantiles -- 8.6.1. Parametric Estimates of Quantiles -- 8.6.2. Nonparametric Estimates of Quantiles -- 8.7. Prediction Intervals -- 8.7.1. Parametric Prediction Intervals -- 8.7.2. Nonparametric Prediction Intervals -- 8.8. Tolerance Intervals -- 8.8.1. Parametric Tolerance Intervals -- 8.8.2. Nonparametric Tolerance Intervals -- 8.9. Hypothesis Tests -- 8.9.1. Goodness-of-Fit Tests -- 8.9.2. Nonparametric Tests to Compare Two Groups -- 8.10. Summary -- 9. Monte Carlo Simulation and Risk Assessment -- 9.1. Introduction -- 9.2. Overview -- 9.3. Monte Carlo Simulation -- 9.3.1. Simulating the Distribution of the Sum of Two Normal Random Variables -- 9.4. Generating Random Numbers -- 9.4.1. Generating Random Numbers from a Uniform Distribution -- 9.4.2. Generating Random Numbers from an Arbitrary Distribution -- 9.4.3. Latin Hypercube Sampling -- 9.4.4. Example of Simple Random Sampling versus Latin Hypercube Sampling -- 9.4.5. Properties of Latin Hypercube Sampling -- 9.4.6. Generating Correlated Multivariate Random Numbers -- 9.5. Uncertainty and Sensitivity Analysis -- 9.5.1. Important Versus Sensitive Parameters -- 9.5.2. Uncertainty Versus Variability -- 9.5.3. Sensitivity Analysis Methods -- 9.5.4. Uncertainty Analysis Methods -- 9.5.5. Caveat -- 9.6. Risk Assessment -- 9.6.1. Definitions -- 9.6.2. Building a Risk Assessment Model -- 9.6.3. Example: Quantifying Variability and Parameter Uncertainty -- 9.7. Summary |
| Summary |
This book describes EnvStats, a new comprehensive R package for environmental statistics. EnvStats and R provide an open-source set of powerful functions for performing graphical and statistical analyses of environmental data, along with an extensive hypertext help system that explains what these methods do, how to use them, and where to find them in the environmental statistics literature. EnvStats also includes numerous built-in data sets from regulatory guidance documents, state and federal databases, and the literature |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource; title from PDF title page (SpringerLink, viewed October 21, 2013) |
| Subject |
Environmental sciences -- Statistical methods
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R (Computer program language)
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Ecology -- statistics & numerical data
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Statistics as Topic -- methods
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BUSINESS & ECONOMICS -- Real Estate -- General.
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Ciencias medioambientales -- Métodos estadísticos
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R (Lenguaje de programación)
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Environmental sciences -- Statistical methods
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R (Computer program language)
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| Form |
Electronic book
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| ISBN |
9781461484561 |
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1461484561 |
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9781306164207 |
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1306164206 |
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