Statistics for Environmental Science and Management / Edition 2

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More About This Textbook


Revised, expanded, and updated, this second edition of Statistics for Environmental Science and Management is that rare animal, a resource that works well as a text for graduate courses and a reference for appropriate statistical approaches to specific environmental problems. It is uncommon to find so many important environmental topics covered in one book. Its strength is author Bryan Manly’s ability to take a non-mathematical approach while keeping essential mathematical concepts intact. He clearly explains statistics without dwelling on heavy mathematical development.

The book begins by describing the important role statistics play in environmental science. It focuses on how to collect data, highlighting the importance of sampling and experimental design in conducting rigorous science. It presents a variety of key topics specifically related to environmental science such as monitoring, impact assessment, risk assessment, correlated and censored data analysis, to name just a few.

Revised, updated or expanded material on:

  • Data Quality Objectives
  • Generalized Linear Models
  • Spatial Data Analysis
  • Censored Data
  • Monte Carlo Risk Assessment

There are numerous books on environmental statistics; however, while some focus on multivariate methods and others on the basic components of probability distributions and how they can be used for modeling phenomenon, most do not include the material on sampling and experimental design that this one does. It is the variety of coverage, not sacrificing too much depth for breadth, that sets this book apart.

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Product Details

Table of Contents

The Role of Statistics in Environmental Science


Some Examples

The Importance of Statistics in the Examples

Chapter Summary


Environmental Sampling


Simple Random Sampling

Estimation of Population Means

Estimation of Population Totals

Estimation of Proportions

Sampling and Nonsampling Errors

Stratified Random Sampling


Systematic Sampling

Other Design Strategies

Ratio Estimation

Double Sampling0

Choosing Sample Sizes1

Unequal-Probability Sampling

The Data Quality Objectives Process

Chapter Summary


Models for Data

Statistical Models

Discrete Statistical Distributions

Continuous Statistical Distributions

The Linear Regression Model8

Factorial Analysis of Variance

Generalized Linear Models

Chapter Summary


Drawing Conclusions from Data


Observational and Experimental Studies

True Experiments and Quasi-Experiments

Design-Based and Model-Based Inference

Tests of Significance and Confidence Intervals

Randomization Tests



Multiple Testing


Bayesian Inference

Chapter Summary


Environmental Monitoring


Purposely Chosen Monitoring Sites

Two Special Monitoring Designs6

Designs Based on Optimization

Monitoring Designs Typically Used

Detection of Changes by Analysis of Variance

Detection of Changes Using Control Charts

Detection of Changes Using CUSUM Charts

Chi-Squared Tests for a Change in a Distribution

Chapter Summary


Impact Assessment


The Simple Difference Analysis with BACI Designs

Matched Pairs with a BACI Design.

Impact-Control Designs

Before–After Designs

Impact-Gradient Designs

Inferences from Impact Assessment Studies

Chapter Summary


Assessing Site Reclamation


Problems with Tests of Significance

The Concept of Bioequivalence

Two-Sided Tests of Bioequivalence

Chapter Summary


Time Series Analysis


Components of Time Series

Serial Correlation

Tests for Randomness

Detection of Change Points and Trends

More-Complicated Time Series Models

Frequency Domain Analysis


Chapter Summary


Spatial-Data Analysis


Types of Spatial Data

Spatial Patterns in Quadrat Counts

Correlation between Quadrat Counts

Randomness of Point Patterns

Correlation between Point Patterns

Mantel Tests for Autocorrelation

The Variogram


Correlation between Variables in Space

Chapter Summary


Censored Data


Single Sample Estimation

Estimation of Quantiles

Comparing the Means of Two or More Samples

Regression with Censored Data

Chapter Summary


Monte Carlo Risk Assessment


Principles for Monte Carlo Risk Assessment

Risk Analysis Using a Spreadsheet

Chapter Summary


Final Remarks




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