| Description |
1 online resource (xiii, 275 pages) |
| Series |
Environmental and ecological statistics |
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Environmental and ecological statistics.
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| Contents |
Note continued: 5.3. Minimization of the Expected Relative Cost -- 5.4. Discussion -- 6. Inference on Mean and Variance -- 6.1. Introduction -- 6.2. Notation and Basic Results -- 6.2.1. Notation -- 6.2.2. Basic Results -- 6.3. Estimation Without Measurement Error -- 6.4. Estimation in the Presence of Measurement Error -- 6.5. Maintaining Precision While Reducing Cost -- 6.6. Estimation of & sigma; 2 x and & sigma; 2 & epsilon; -- 6.7. Estimation of Population Variance -- 6.8. Confidence Interval for the Population Mean -- 6.9. Tests of Hypotheses in the Population Mean -- 6.9.1. One-Sample Tests -- 6.9.2. Two-Sample Tests -- 6.10. Applications -- 6.10.1. Comparison of Arithmetic Averages of Soil pH Values with the pH Values of Composite Samples -- 6.10.2. Comparison of Random and Composite Sampling Methods for the Estimation of Fat Contents of Bulk Milk Supplies -- 6.10.3. Optimization of Sampling for the Determination of Mean Radium-226 Concentration in Surface Soil -- 7. Composite Sampling with Random Weights -- 7.1. Introduction -- 7.2. Expected Value, Variance, and Covariance of Bilinear Random Forms -- 7.3. Models for the Weights -- 7.3.1. Assumptions on the First Two Moments -- 7.3.2. Distributional Assumptions -- 7.4. Model for Composite Sample Measurements -- 7.4.1. Subsampling a Composite Sample -- 7.4.2. Several Composite Samples -- 7.4.3. Subsampling of Several Composite Samples -- 7.4.4. Measurement Error -- 7.5. Applications -- 7.5.1. Sampling Frequency and Comparison of Grab and Composite Sampling Programs for Effluents -- 7.5.2. Theoretical Comparison of Grab and Composite Sampling Programs -- 7.5.3. Grab vs. Composite Sampling: A Primer for the Manager and Engineer -- 7.5.4. Composite Samples Overestimate Waste Loads -- 7.5.5. Composite Samples for Foliar Analysis |
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Note continued: 7.5.6. Lateral Variability of Forest Floor Properties Under Second-Growth Douglas-Fir Stands and the Usefulness of Composite Sampling Techniques -- 8. Linear Model for Estimation with Composite Sample Data -- 8.1. Introduction -- 8.2. Motivation for a Unified Model -- 8.3. Model -- 8.4. Discussion of the Assumptions -- 8.4.1. Structural/Sampling Submodel -- 8.4.2. Compositing/Subsampling Submodel -- 8.4.3. Structure of the Matrices W, Mw, and & Sigma;w -- 8.5. Moments of x and y -- 8.6. Complex Sampling Schemes Before Compositing -- 8.6.1. Segmented Populations -- 8.6.2. Estimating the Mean in Segmented Populations -- 8.6.3. Estimating Variance Components in Segmented Populations -- 8.7. Estimating the Effect of a Binary Factor -- 8.7.1. Fully Segregated Composites -- 8.7.2. Fully Confounded Composites -- 8.8. Elementary Matrices and Kronecker Products -- 8.8.1. Decomposition of Block Matrices -- 8.9. Expectation and Dispersion Matrix When Both W and x Are Random -- 8.9.1. Expectation of Wx -- 8.9.2. Variance/Covariance Matrix of Wx -- 9. Composite Sampling for Site Characterization and Cleanup Evaluation -- 9.1. Data Quality Objectives -- 9.2. Optimal Composite Designs -- 9.2.1. Cost of a Sampling Program -- 9.2.2. Optimal Allocation of Resources -- 9.2.3. Power of a Test and Determination of Sample Size -- 9.2.4. Algorithms for Determination of Sample Size -- 10. Spatial Structures of Site Characteristics and Composite Sampling -- 10.1. Introduction -- 10.2. Models for Spatial Processes -- 10.2.1. Composite Sampling -- 10.3. Application to Two Superfund Sites -- 10.3.1. Two Sites -- 10.3.2. Methods -- 10.3.3. Results -- 10.3.4. Discussion -- 10.4. Compositing by Spatial Contiguity -- 10.4.1. Introduction -- 10.4.2. Retesting Strategies -- 10.4.3. Composite Sample-Forming Schemes |
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Note continued: 10.5. Compositing of Ranked Set Samples -- 10.5.1. Ranked Set Sampling -- 10.5.2. Relative Precision of the RSS Estimator of a Population Mean Relative to Its SRS Estimator -- 10.5.3. Unequal Allocation of Sample Sizes -- 10.5.4. Formation of Homogeneous Composite Samples -- 11. Composite Sampling of Soils and Sediments -- 11.1. Detection of Contamination -- 11.1.1. Detecting PCB Spills -- 11.1.2. Compositing Strategy for Analysis of Samples -- 11.2. Estimation of the Average Level of Contamination -- 11.2.1. Estimation of the Average PCB Concentration on the Spill Area -- 11.2.2. Onsite Surface Soil Sampling for PCB at the Armagh Site -- 11.2.3. Armagh Site -- 11.2.4. Simulating Composite Samples -- 11.2.5. Locating Individual Samples with High PCB Concentrations -- 11.3. Estimation of Trace Metal Storage in Lake St. Clair Post-settlement Sediments Using Composite Samples -- 12. Composite Sampling of Liquids and Fluids -- 12.1. Comparison of Random and Composite Sampling Methods for the Estimation of Fat Content of Bulk Milk Supplies -- 12.1.1. Experiment -- 12.1.2. Estimation Methods -- 12.1.3. Results -- 12.1.4. Composite Compared with Yield-Weighted Estimate of Fat Percentage -- 12.2. Composite Sampling of Highway Runoff -- 12.3. Composite Samples Overestimate Waste Loads -- 13. Composite Sampling and Indoor Air Pollution -- 13.1. Household Dust Samples -- 14. Composite Sampling and Bioaccumulation -- 14.1. Example: National Human Adipose Tissue Survey -- 14.2. Results from the Analysis of 1987 NHATS Data |
| Summary |
Sampling consists of selection, acquisition, and quantification of a part of the population. While selection and acquisition apply to physical sampling units of the population, quantification pertains only to the variable of interest, which is a particular characteristic of the sampling units. A sampling procedure is expected to provide a sample that is representative with respect to some specified criteria. Composite sampling, under idealized conditions, incurs no loss of information for estimating the population means. But an important limitation to the method has been the loss of informatio |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Print version record |
| Subject |
Environmental sampling -- Statistical methods
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Environmental sciences -- Statistical methods
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POLITICAL SCIENCE -- Public Policy -- Environmental Policy.
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SCIENCE -- Environmental Science.
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Environnement.
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Climat.
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Environmental sampling -- Statistical methods
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Environmental sciences -- Statistical methods
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| Form |
Electronic book
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| Author |
Gore, Sharad D.
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Taillie, C.
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| ISBN |
9781441976284 |
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1441976280 |
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