Statistics for Environmental Science and Management (Chapman & Hall/CRC Applied Environmental Statistics 2nd Revised edition)

Statistics for Environmental Science and Management (Chapman & Hall/CRC Applied Environmental Statistics 2nd Revised edition)

By: Bryan F. J. Manly (author), Richard L. Smith (series_editor)Hardback

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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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Western EcoSystem Technology, Inc., Laramie, Wyoming, USA University of North Carolina, Chapel Hill, USA


The Role of Statistics in Environmental Science Introduction Some Examples The Importance of Statistics in the Examples Chapter Summary Exercises Environmental Sampling Introduction Simple Random Sampling Estimation of Population Means Estimation of Population Totals Estimation of Proportions Sampling and Nonsampling Errors Stratified Random Sampling Post-Stratification Systematic Sampling Other Design Strategies Ratio Estimation Double Sampling0 Choosing Sample Sizes1 Unequal-Probability Sampling The Data Quality Objectives Process Chapter Summary Exercises Models for Data Statistical Models Discrete Statistical Distributions Continuous Statistical Distributions The Linear Regression Model8 Factorial Analysis of Variance Generalized Linear Models Chapter Summary Exercises Drawing Conclusions from Data Introduction Observational and Experimental Studies True Experiments and Quasi-Experiments Design-Based and Model-Based Inference Tests of Significance and Confidence Intervals Randomization Tests Bootstrapping Pseudoreplication Multiple Testing Meta-Analysis Bayesian Inference Chapter Summary Exercises Environmental Monitoring Introduction 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 Exercises Impact Assessment Introduction 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 Exercises Assessing Site Reclamation Introduction Problems with Tests of Significance The Concept of Bioequivalence Two-Sided Tests of Bioequivalence Chapter Summary Exercises Time Series Analysis Introduction Components of Time Series Serial Correlation Tests for Randomness Detection of Change Points and Trends More-Complicated Time Series Models Frequency Domain Analysis Forecasting Chapter Summary Exercises Spatial-Data Analysis Introduction 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 Kriging Correlation between Variables in Space Chapter Summary Exercises Censored Data Introduction Single Sample Estimation Estimation of Quantiles Comparing the Means of Two or More Samples Regression with Censored Data Chapter Summary Exercises Monte Carlo Risk Assessment Introduction Principles for Monte Carlo Risk Assessment Risk Analysis Using a Spreadsheet Chapter Summary Exercises Final Remarks Appendices References Index

Product Details

  • publication date: 26/09/2008
  • ISBN13: 9781420061475
  • Format: Hardback
  • Number Of Pages: 310
  • ID: 9781420061475
  • weight: 572
  • ISBN10: 142006147X
  • edition: 2nd Revised edition

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