Statistics for Environmental Engineers

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It is incorrect to imagine that one data point provides one piece of information and therefore the information content of a data set is determined entirely by the number of measurements. The amount of information available from a fixed number of measurements increases dramatically if each observation contributes to estimating more than one parameter (mean, factor effect, variance, etc.). An exciting application of statistical experimental design is to make each observation do double duty or even triple or heavier duty. However, any valid statistical analysis can only extract the information existing in the data at hand. This content is largely determined by the experimental design and cannot be altered by the statistical analysis.

This chapter discusses an experimental design that was used to efficiently evaluate four factors that were expected to be important in an air quality monitoring program. The experiment is based on a factorial design (but not the two-level design discussed in Chapter 27). The method of computing the results is not discussed because this can be done by commercial computer programs. Instead, discussion focuses on how the four-factor analysis of variance is interpreted. References are given for the reader who wishes to know how such experiments are designed and how the calculations are done (Scheffe, 1959).

Case Study: Sampling Dioxin and Furan Emissions from an Incinerator

Emission of dioxins and furans from waste incinerators has been under investigation in many countries. It is important to learn whether different samplers (perhaps used at different incinerators or in different cities or countries) affect the amount of dioxin or furan measured. It is also important to assess whether differences, if any, are independent of other factors (such as incinerator loading rate and feed materials which change from one sampling period to another).

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