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Environmental Statistics with S-PLUS

  • Follows a sequential progression from elementary ideas about sampling and data to more advanced methods of estimation and testing as applied to environmental data
  • Provides instructions throughout on how to use S-PLUS
  • Offers an in-depth discussion of Monte Carlo simulation and quantitative risk assessment
  • Presents a variety of data sets to show how to solve different types of environmental problems, such as testing for contaminated groundwater or soil and air quality monitoring

Series: Chapman & Hall/CRC Applied Environmental Statistics

By: Steven P Millard (Author), Nagaraj K Neerchal (Author)

848 pages, 110 tables

Chapman & Hall

Hardback | Sep 2000 | #206831 | ISBN-13: 9780849371684
Availability: Usually dispatched within 1-2 weeks Details
NHBS Price: £92.99 $116/€110 approx

About this book

A clear, comprehensive treatment of the subject, Environmental Statistics with S-PLUS surveys the vast array of statistical methods used to collect and analyze environmental data. Environmental Statistics with S-PLUS explains what these methods are, how to use them, and where to find references to them. In addition, it provides insight into what to think about before you collect environmental data, how to collect the data, and how to make sense of it after collection.

A unique and powerful feature of the book is its integration with the commercially available software package S-Plus and the add-on modules EnvironmentalStats for S-PLUS, S+SpatialStats, and S-PLUS for ArcView. Environmental Statistics with S-PLUS presents data sets to explain statistical methods, and then shows how to implement these methods by providing the commands for and the results from the software. This survey of statistical methods, definitions, and concepts helps you collect and effectively analyze data for environmental pollution problems.

Using the S-PLUS software in conjunction with this text will no doubt increase understanding of the methods.

" [...] a comprehensive resource for environmental scientists, engineers, regulators, and students who need to collect and make sense of environmental data. [...] A strong feature of the text is the companion information and datasets posted on the book's website. [...] The book is a good general text in statistical methodology that addresses the use of S-Plus."
- JASA, September 2001

" [...] provides an exhaustive treatment of the statistical sampling and analysis issues that confront applied environmental statisticians and scientists. Drs. Millard and Neerchal have a rare talent for explaining and illustrating even the most difficult statistical procedures and principles in language that is clear and free of jargon. However, the most unique feature of the software and text is that they are seamlessly interwoven, each complimenting the other. The software illustrates the book and the book places the software in the context of the underlying environmental research questions. This is unprecedented. Together the software and the book clarify the DQO and DQA process to such an extent that it is hard for me to imagine applying these principles to my own work or teaching them to applied scientists without the two."
- Michael Riggs, RTI

"The combination of easy-to-use software with easy access to a description of statistical methods is done with thoroughness and skill."
- Richard Gilbert, Battelle Pacific NW Labs

"Environmental Statistics with S-PLUS is a textbook with a thorough coverage of basic data analysis, but it also ventures into more complex areas such as risk assessment and spatial statistics. Although the use of the relevant S-PLUS packages is described throughout, the book is basically about statistics, not software: It is statistics 'with' rather than 'in' or 'for' S-PLUS. It is intended for just about anyone: in practice it would be suitable for many people with a background in engineering or environmental health."
- Technometrics, Vol. 43, No. 4, November 2001


Intended Audience
Environmental Science, Regulations, and Statistics
Data Sets and Case Studies

The Basic Scientific Method
What is a Population and What Is a Sample?
Random vs. Judgment Sampling
The Hypothesis Testing Framework
Common Mistakes in Environmental Studies
The Data Quality Objectives Process
Sources of Variability and Independence
Methods of Random Sampling
Case Study

Summary Statistics
Graphs for a Single Variable
Graphs for Two or More Variables

What Is a Random Variable?
Discrete vs. Continuous Random Variable
What is a Probability Distribution?
Probability Density Function (PDF)
Cumulative Distribution Function (CDF)
Quantiles and Percentiles
Generating Random Numbers from Probability Distributions
Characteristics of Probability Distributions
Important Distributions in Environmental Statistics
Multivariate Probability Distributions

Methods for Estimating Distribution Parameters
Using EnvironmentalStats for S?Plus to Estimate Distribution Parameters
Comparing Different Estimators
Accuracy, Bias, Mean Square Error, Precision, Random Error, Systematic Error, and Variability
Parametric Confidence Intervals for Distribution Parameters
Nonparametric Confidence Intervals Based on Bootstrapping
Estimates and Confidence Intervals for Distribution Quantiles (Percentiles)
A Cautionary Note about Confidence Intervals

Prediction Intervals
Simultaneous Prediction Intervals
Tolerance Intervals
Control Charts

The Hypothesis Testing Framework
Overview of Univariate Hypothesis Tests
Goodness-of-Fit Tests
Test of a Single Proportion
Tests of Location
Tests on Percentiles
Tests on Variability
Comparing Locations between Two Groups: The Special Case of Paired Differences
Comparing Locations between Two Groups
Comparing Two Proportions
Comparing Variances between Two Groups
The Multiple Comparisons Problem
Comparing Locations between Several Groups
Comparing Proportions between Several Groups
Comparing Variability between Several Groups

Designs Based on Confidence Intervals
Designs Based on Nonparametric Confidence, Prediction, and Tolerance Intervals
Designs Based on Hypothesis Tests
Optimizing a Design Based on Cost Considerations

Covariance and Correlation
Simple Linear Regression
Regression Diagnostics
Calibration, Inverse Regression, and Detection Limits
Multiple Regression
Dose-Response Models: Regression for Binary Outcomes
Other Topics in Regression

Classification of Censored Data
Graphical Assessment of Censored Data
Estimating Distribution Parameters
Estimating Distribution Quantiles
Prediction and Tolerance Intervals
Hypothesis Tests
A Note about Zero-Modified Distributions

Creating and Plotting Time Series Data
Dealing with Autocorrelation
More Complicated Models: Autoregressive and Moving Average Processes
Estimating and Testing for Trend

Overview: Types of Spatial Data
The Benthic Data
Models for Geostatistical Data
Modeling Spatial Correlation
Prediction for Geostatistical Data
Using S-Plus for ArcView GIS

Monte Carlo Simulation
Generating Random Numbers
Uncertainty and Sensitivity Analysis
Risk Assessment



Summaries and Exercises appear at the end of each chapter.

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