Table Of ContentEnvironmental 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
SynopsisStatistics 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.