# Statistics for Environmental Engineers

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When one is confronted with a new problem, a two-part question of crucial importance is, “How will using statistics help solve this problem and which techniques should be used?” Many different substantive problems arise and many different statistical techniques exist, ranging from making simple plots of data to iterative model building and parameter estimation.

Some problems can be solved by subjecting the available data to a particular analytical method. More often the analysis must be stepwise. As Sir Ronald Fisher said, . .a statistician ought to strive above all to acquire versatility and resourcefulness, based on a repertoire of tried procedures, always aware that the next case he wants to deal with may not fit any particular recipe.”

Doing statistics on environmental problems can be like coaxing a stubborn animal. Sometimes small steps, often separated by intervals of frustration, are the only way to progress at all. Even when the data contains bountiful information, it may be discovered in bits and at intervals.

The goal of statistics is to make that discovery process efficient. Analyzing data is part science, part craft, and part art. Skills and talent help, experience counts, and tools are necessary. This book illustrates some of the statistical tools that we have found useful; they will vary from problem to problem. We hope this book provides some useful tools and encourages environmental engineers to develop the necessary craft and art.

### Statistics and Environmental Law

Environmental laws and regulations are about toxic chemicals, water quality criteria, air quality criteria, and so on, but they are also about statistics because they are laced with statistical terminology and concepts. For example, the limit of detection is a statistical concept used by chemists. In environmental biology, acute and chronic toxicity criteria are developed from complex data collection and statistical estimation procedures, safe and adverse conditions are differentiated through statistical comparison of control and exposed populations, and cancer potency factors are estimated by extrapolating models that have been fitted to dose-response data.

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