Millard S.P., Neerchal N.K. Environmental Statistics with S-PLUS

CRC Press LLC, 2001. - 822 р. - ISBN10: 0-8493-7168-6.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. The book 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. The book 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.Intended Audience
Environmental Science, Regulations, and Statistics
Overview
Data Sets and Case Studies
SoftwareExercisesDesigning a Sampling Program
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 StudyExercisesLooking at Data
Summary Statistics
Graphs for a Single Variable
Graphs for Two or More VariablesExercisesProbability Distributions
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 DistributionsExercisesEstimating Distribution Parameters and Quantiles
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 IntervalsExercisesPrediction Intervals, Tolerance Intervals, and Control Charts
Prediction Intervals
Simultaneous Prediction Intervals
Tolerance Intervals
Control ChartsExercisesHypothesis Tests
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 GroupsExercisesDesigning a Sampling Program
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 ConsiderationsExercisesLinear Models
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 RegressionExercisesCensored Data
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 DistributionsExercisesTime Series Analysis
Creating and Plotting Time Series Data
Autocorrelation
Dealing with Autocorrelation
More Complicated Models:
Autoregressive and Moving Average Processes
Estimating and Testing for TrendExercisesSpatial Statistics
Overview: Types of Spatial Data
The Benthic Data
Models for Geostatistical Data
Modeling Spatial Correlation
Prediction for Geostatistical Data
Using S-PLUS for ArcView GISExercisesMonte Carlo Simulation and Risk Assessment
Overview
Monte Carlo Simulation
Generating Random Numbers
Uncertainty and Sensitivity Analysis
Risk AssessmentExercises
Referenc