Statistics for environmental science and management

Title:

Personal Author:

Manly, Bryan F. J.

Series:

Applied environmental statistics

Edition:

2nd ed.

Publication Information:

Boca Raton, FL : Chapman & Hall, 2009

Physical Description:

xiv, 295 p. : ill. ; 25 cm.

ISBN:

9781420061475

Subject Term:

Environmental sciences -- Statistical methods

Environmental management -- Statistical methods

Available:*

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010203667	GE45.S73 M36 2009	Open Access Book	Book	Searching... Unknown
Searching... SPACE KL Main Library	30000010117582	GE45.S73 M36 2009	Open Access Book	Book	Searching... Unknown

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.

Preface to the Second Edition	p. xi
Preface to the First Edition	p. xiii
1 The Role of Statistics in Environmental Science	p. 1
1.1 Introduction	p. 1
1.2 Some Examples	p. 1
1.3 The Importance of Statistics in the Examples	p. 19
1.4 Chapter Summary	p. 19
Exercises	p. 20
2 Environmental Sampling	p. 23
2.1 Introduction	p. 23
2.2 Simple Random Sampling	p. 24
2.3 Estimation of Population Means	p. 24
2.4 Estimation of Population Totals	p. 29
2.5 Estimation of Proportions	p. 30
2.6 Sampling and Nonsampling Errors	p. 32
2.7 Stratified Random Sampling	p. 33
2.8 Post-Stratification	p. 38
2.9 Systematic Sampling	p. 39
2.10 Other Design Strategies	p. 44
2.11 Ratio Estimation	p. 46
2.12 Double Sampling	p. 50
2.13 Choosing Sample Sizes	p. 51
2.14 Unequal-Probability Sampling	p. 53
2.15 The Data Quality Objectives Process	p. 55
2.16 Chapter Summary	p. 56
Exercises	p. 58
3 Models for Data	p. 61
3.1 Statistical Models	p. 61
3.2 Discrete Statistical Distributions	p. 61
3.2.1 The Hypergeometric Distribution	p. 62
3.2.2 The Binomial Distribution	p. 63
3.2.3 The Poisson Distribution	p. 64
3.3 Continuous Statistical Distributions	p. 65
3.3.1 The Exponential Distribution	p. 66
3.3.2 The Normal or Gaussian Distribution	p. 67
3.3.3 The Lognormal Distribution	p. 67
3.4 The Linear Regression Model	p. 68
3.5 Factorial Analysis of Variance	p. 74
3.5.1 One-Factor Analysis of Variance	p. 76
3.5.2 Two-Factor Analysis of Variance	p. 76
3.5.3 Three-Factor Analysis of Variance	p. 78
3.5.4 Repeated-Measures Designs	p. 82
3.5.5 Multiple Comparisons and Contrasts	p. 83
3.6 Generalized Linear Models	p. 84
3.7 Chapter Summary	p. 90
Exercises	p. 91
4 Drawing Conclusions from Data	p. 97
4.1 Introduction	p. 97
4.2 Observational and Experimental Studies	p. 97
4.3 True Experiments and Quasi-Experiments	p. 99
4.4 Design-Based and Model-Based Inference	p. 101
4.5 Tests of Significance and Confidence Intervals	p. 103
4.6 Randomization Tests	p. 105
4.7 Bootstrapping	p. 108
4.8 Pseudoreplication	p. 110
4.9 Multiple Testing	p. 112
4.10 Meta-Analysis	p. 114
4.11 Bayesian Inference	p. 119
4.12 Chapter Summary	p. 120
Exercises	p. 122
5 Environmental Monitoring	p. 125
5.1 Introduction	p. 125
5.2 Purposely Chosen Monitoring Sites	p. 126
5.3 Two Special Monitoring Designs	p. 126
5.4 Designs Based on Optimization	p. 129
5.5 Monitoring Designs Typically Used	p. 129
5.6 Detection of Changes by Analysis of Variance	p. 131
5.7 Detection of Changes Using Control Charts	p. 133
5.8 Detection of Changes Using CUSUM Charts	p. 140
5.9 Chi-Squared Tests for a Change in a Distribution	p. 145
5.10 Chapter Summary	p. 149
Exercises	p. 150
6 Impact Assessment	p. 153
6.1 Introduction	p. 153
6.2 The Simple Difference Analysis with BACI Designs	p. 155
6.3 Matched Pairs with a BACI Design	p. 158
6.4 Impact-Control Designs	p. 161
6.5 Before-After Designs	p. 162
6.6 Impact-Gradient Designs	p. 163
6.7 Inferences from Impact Assessment Studies	p. 163
6.8 Chapter Summary	p. 164
Exercises	p. 165
7 Assessing Site Reclamation	p. 167
7.1 Introduction	p. 167
7.2 Problems with Tests of Significance	p. 167
7.3 The Concept of Bioequivalence	p. 168
7.4 Two-Sided Tests of Bioequivalence	p. 171
7.5 Chapter Summary	p. 176
Exercises	p. 177
8 Time Series Analysis	p. 179
8.1 Introduction	p. 179
8.2 Components of Time Series	p. 180
8.3 Serial Correlation	p. 182
8.4 Tests for Randomness	p. 186
8.5 Detection of Change Points and Trends	p. 190
8.6 More-Complicated Time Series Models	p. 194
8.7 Frequency Domain Analysis	p. 201
8.8 Forecasting	p. 202
8.9 Chapter Summary	p. 203
Exercises	p. 204
9 Spatial-Data Analysis	p. 207
9.1 Introduction	p. 207
9.2 Types of Spatial Data	p. 207
9.3 Spatial Patterns in Quadrat Counts	p. 211
9.4 Correlation between Quadrat Counts	p. 217
9.5 Randomness of Point Patterns	p. 219
9.6 Correlation between Point Patterns	p. 221
9.7 Mantel Tests for Autocorrelation	p. 222
9.8 The Variogram	p. 224
9.9 Kriging	p. 228
9.10 Correlation between Variables in Space	p. 230
9.11 Chapter Summary	p. 231
Exercises	p. 233
10 Censored Data	p. 237
10.1 Introduction	p. 237
10.2 Single Sample Estimation	p. 237
10.3 Estimation of Quantiles	p. 244
10.4 Comparing the Means of Two or More Samples	p. 244
10.5 Regression with Censored Data	p. 247
10.6 Chapter Summary	p. 247
Exercises	p. 248
11 Monte Carlo Risk Assessment	p. 249
11.1 Introduction	p. 249
11.2 Principles for Monte Carlo Risk Assessment	p. 250
11.3 Risk Analysis Using a Spreadsheet	p. 251
11.4 Chapter Summary	p. 253
Exercises	p. 253
12 Final Remarks	p. 255
Appendices	p. 257
References	p. 279
Index	p. 291

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