EnvStats : an R package for environmental statistics /

EnvStats : an R package for environmental statistics / Steven P. Millard.

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 me...

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Bibliographic Details
Online Access: Full Text (via Springer)
Main Author: Millard, Steven P. (Author)
Format: eBook
Language:English
Published: New York, NY : Springer, 2013.
Subjects:
Environmental sciences > Statistical methods.
R (Computer program language)
Table of 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.

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