Experiments, statistically designed

The use of statistically designed experiments in the development of both PS and RF foams has led to very significant progress, where critical problems were identified and solved quickly and where previously unexplored ranges of variables led to substantially improved materials. We shall continue to employ it in our development of low-density foams, and we strongly recommend it to other researchers. 

This paper does not aim at educating the reader in how to use the concepts of statistically designed experiments, but rather at showing how computers can make the task relatively rapid and convenient. It is assumed that the reader has at least a passing knowledge... 

Table I is a list of physical properties of materials which were of special concern, along with target values felt to indicate useful levels in a particular application. From the beginning it was predicted that one of the biggest problems would be to balance Properties A and E, usually considered mutually exclusive. It was also assumed that Properties B and E were highly correlated. Statistically designed experiments and data analysis were chosen to determine most efficiently the formulations which would give the best combination of all the target properties.

Because of the complexities involved in understanding cause-effect relationships, an alternative approach to control the thin film microstructure has been pursued by some investigators—the use of statistically designed experiments to identify key processing parameters.114115 In these approaches, as illustrated in Table 2.6 for a Plackett-Burman screening study,114 limiting values for various experimental parameters are chosen. Films are then prepared from solutions synthesized under these conditions, and resulting film... 

Role of Statistically Designed Experiments in the Development of Efficient Downstream Processes... 

This paper will focus on the use of statistically designed experiments to develop effective purification processes in the most time and cost efficient fashion. Downstream processing and the recovery of proteins by severd different techniques have been discussed in other articles (1-3) and will not be discussed here. 

Statistically designed experiments were carried out to assess the reproducibility of the method as applied to both solid isolates and broth samples. 

High-level radioactive defense waste solutions, originating from plutonium recovery and waste processing operations at the U.S. Department of Energy s Hanford Site, currently are stored in mild steel-lined concrete tanks located in thick sedimentary beds of sand and gravel. Statistically designed experiments were used to identify the effects of 12 major chemical components of Hanford waste solution on radionuclide solubility and sorption. 

In such statistically designed experiments one wants to exclude the random effects of a limited number of features by varying them systematically, i.e. by variation of the so-called factors. At the same time the order in which the experiments are performed should be randomized to avoid systematic errors in experimentation. In another basic type of experiment, sequential experiments, the set-up of an experiment depends on the results obtained from previous experiments. For help in deciding which design is preferable, see Section 3.6. In principle, statistical design is one recommendation of how to perform the experiments. The design should always be based on an exact question or on a working hypothesis. These in turn are often based on models. 

The precision of an instrument must be considered. Many typical measurements, for example, in atomic spectroscopy, are recorded to only two significant figures. Consider a dataset in which about 95 % of the readings were recorded between 0.10 and 0.30 absorbance units, yet a statistically designed experiment tries to estimate 64 effects. The /-test provides information on the significance of each effect. However, statistical tests assume that the data are recorded to indefinite accuracy, and will not take this lack of numerical precision into account. For the obvious effects, chemo-metrics will not be necessary, but for less obvious effects, the statistical conclusions will be invalidated because of the low numerical accuracy in the raw data. 

Rege, B. D., Gawel, J., and Kou, J. FI. (2002), Identification of critical process variables for coating actives onto tablets via statistically designed experiments, Int. J. Pharm.,2 >1, 87-94. 

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