Statistics for Environmental Engineers is a solution-oriented book that encourages environmental engineers to view statistics as a problem-solving tool. It presents a completely new approach to the practical use of statistics in environmental science and engineering. You will learn about extracting information from data and using efficient experimental designs to generate informative data. Written in an easy-to-understand style, Statistics for Environmental Engineers consists of more than 40 short chapters, each dealing with a particular environmental problem or statistical technique. Each chapter is organized around a specific case study, beginning with a brief discussion of the appropriate methodology, followed by an analysis of the case study example, and ending with a comment on the strengths and weaknesses of the approach. This one-of-a-kind book belongs in the personal library of every environmental scientist and engineer and in the classroom as a perfect textbook for students. Whether the topic is displaying data, t-tests, confidence intervals, regression, or experimental design, the context is always environmentally familiar. Case studies are drawn from censored data, detection limits, regulatory standards, treatment plant performance, sampling and measurement errors, hazardous waste, and much more.
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Environmental Problems and Statistics
A Brief Review of Statistics
Plotting Data
Smoothing Data
Seeing the Shape of a Distribution
External Reference Distributions
Using Transformations
Estimating Percentiles
Limit of Detection
Using Measurements below the Limit of Detection
Estimating the Mean of Censored Samples
Assessing Conformance with a Standard
Assessing the Average of Differences
Assessing the Difference of Two Averages
Assessing the Difference of Proportions
Multiple Paired Comparison of k Averages
Analysis of Variance to Compare k Averages
Estimating Variance Components in Experimental Measurements
Multiple Factor Analysis of Variance
Factorial Experimental Designs
Fractional Factorial Experimental Designs
Screening of Important Variables
Correlation Coefficients
Assessing Serial Correlation
Estimating Parameters Using the Method of Least Squares
The Precision of Estimated Parameters
Calibration
Empirical Model Building by Linear Regression
The Coefficient of Determination, R2
Regression Analysis with Categorical Variables
The Effect of Autocorrelation on Regression
The Iterative Approach to Modeling
Seeking Optimal Conditions by Response Surface Methodology
Designing Experiments to Estimate Parameters in Nonlinear Models
Why Linearization Can Bias Parameter Estimates
Fitting Models to Multiresponse Data
A Problem in Model Discrimination
Adjustment of Survey Data
How Measurement Errors Are Transmitted into Calculated Values
Using Simulations to Study Statistical Problems
Intervention Analysis
Appendix-Tables
Book by Brown Linfield C Berthouex Paul Mac
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