Applied Statistical Methods and the Chemical Industry

The discipline of statistics is the study of effective methods of data collection, data summarization, and (data based, quantitative) inference making in a framework that explicitly recognizes the reality of nonnegligible variation in many real-world processes and measurements. 

The ultimate goal of the field is to provide tools for extracting the maximum amount of useful information about a noisy physical process from a given investment of data collection and analysis resources. It is clear that such a goal is relevant to the practice of industrial chemistry. The primary purposes of this chapter are to indicate in concrete terms the nature of some existing methods of applied statistics that are particularly appropriate to industrial chemistry, and to provide an entry into the large and detailed statistical literature for those readers who find in the discussion here reasons to believe that statistical tools can help them be effective in their work. A 

Statisticians have developed a variety of data summarization or description methods whose purpose is to make evident the main features of a data set. (Their use, of course, may be independent of whether or not the data collection process actually employed was in 

respectively, histogram, stem and leaf plot, dot plot, and box plot forms. 

Histograms are commonly and effectively used for final data presentation, but as working data analysis tools they suffer from several limitations. In one direction, their 

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We have tried in this chapter to give readers the flavor of modem applied statistical methods and to illustrate their usefulness in the chemical industry. Details of their implementation have of necessity been reserved for further more specialized reading, for which the interested reader is encouraged to consult the references given in this chapter. 

Additional chapters in the grouping broadly referred to as infrastructure include the new Recent History of the Chemical Industry 1973 to the Millennium and an update of the chapter titled Economic Aspects of the Chemical Industry, in which some of the material extends information provided in the former. Rounding out the infrastructure group are yet another new chapter Nanotechnology Principles and Applications, together with the earlier ones which cover such diverse and fundamental topics as process safety, emergency preparedness, and applied statistical methods. 

But chemical engineering is more than a group of basic industries or a pile of economic statistics. As an intellectual discipline, it is deeply involved in both basic and applied research. Chemical engineers bring a unique set of tools and methods to the study and solution of some of society s most pressing problems. 

Only applied originally in the pharmaceutical industry, automated synthetic methods have spread quickly into the research for new agrochemicals, other speciality chemicals, catalysts and new materials. It is also being used increasingly in process development laboratories coupled with statistical experimental design. 

It was evident that to apply tests of significance conveniently and economically the experiments had to be planned in appropriate forms. It is considered that the methods outlined should be as much a standard tool of the industrial experimenter as a chemical balance is of the laboratory experimenter. In carrying out an industrial experiment the choice is not between using a statistical design with the application of the appropriate tests of significance or the ordinary methods the choice is between correct or incorrect methods. Even the simplest experiment requires an estimate of the significance of its results. 

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