Design statistical methods

Dean, B.J. Johnstone, A. (1977) Dominant lethal assays in male mice evaluation of experimental design, statistical methods and the sensitivity of Charles River (CDI) mice. Mutat. Res.. 42. 269-278... 

The mixture design statistical method is one of the most important additions to the literature of solvent optimization in the past 15 years. There are many reports on the use of this method in HPLC as well as in TLC. The method has great potential for optimizing the separation of complex mixtures in TLC,... 

Minocycline, 468-473 Mixed sorbent layers, 17 Mixture design statistical method of optimization, 88-91... 

To understand the recommendations for structure descriptors in order to be able to apply them in QSAR or drug design in conjunction with statistical methods or machine learning techniques. 

The data analysis module of ELECTRAS is twofold. One part was designed for general statistical data analysis of numerical data. The second part offers a module For analyzing chemical data. The difference between the two modules is that the module for mere statistics applies the stati.stical methods or rieural networks directly to the input data while the module for chemical data analysis also contains methods for the calculation ol descriptors for chemical structures (cl. Chapter 8) Descriptors, and thus structure codes, are calculated for the input structures and then the statistical methods and neural networks can be applied to the codes. 

Cropley made general recommendations to develop kinetic models for compUcated rate expressions. His approach includes first formulating a hyperbolic non-linear model in dimensionless form by linear statistical methods. This way, essential terms are identified and others are rejected, to reduce the number of unknown parameters. Only toward the end when model is reduced to the essential parts is non-linear estimation of parameters involved. His ten steps are summarized below. Their basis is a set of rate data measured in a recycle reactor using a sixteen experiment fractional factorial experimental design at two levels in five variables, with additional three repeated centerpoints. To these are added two outlier... 

A problem may lie in the knowledge that is required as an essential input to such approaches, being both statistical in nature and from the authors own experiences, often diificult to obtain and interpret generally. Unfortunately, experienced designers will not use statistical methodology, although statistical methods should play an important role in the design and manufacture of reliable products (Amster and... 

The development of the probabilistic design approach, as already touched on, includes elements of probability theory and statistics. The introductory statistical methods discussed in Appendix I provide a useful background for some of the more advanced topics covered next. Wherever possible, the application of the statistical methods is done so through the use of realistic examples, and in some cases with the aid of computer software. 

Statistical methods for probabilistic design 4.2.1 Modelling data using statistical distributions... 

Fisher, R.A., 1990. Statistical Methods, Experimental Design and Scientific Inference. Oxford University Press. 

Control laboratories in the canned food industry are usually divorced from the research organization to a lesser degree than is the case in the chemical and allied industries. For this reason, a closer relationship exists between the problems of the control laboratory and the research laboratory. Although from a research standpoint this condition is often considered undesirable, it has considerable merit in the case of the canned food industry, in which production may be seasonal and often of rather short duration. The collection of control data in many instances may also serve for research purposes—for example, in the case of soil analyses, which may be correlated with agricultural research designed to improve crop yields. Because the variables which affect the quality of canned foods must usually be investigated rather extensively, and often over a period of more than one year, the application of statistical methods to data collected for control purposes can conceivably make a substantial contribution to a research program. 

Statistical benefit Using statistical methods in the design of experiments and data analy-... 

When the reaction was performed in the microreactor, the maximum conversion of 97.0 % was attained when the flow rate of Boc-AMP solution was 9 ml/min and the molar equivalents of KOH to Boc-AMP was 13 as shown in Fig. 1. Optimum operating conditions were obtained from a statistical method by using factorial design [6]. The yield decreased over the KOH equivalency of 13 in Fig. 1, since the phase separation between the t-Boc20 and the aqueous phase was observed due to the increased water content with increasing KOH equivalency. As the heat transfer performance of the microreactor was greatly improved compared with conventional reactors, higher reaction temperature could be admissible. 

In this paper, we discuss studies based on comparison with background measurements that may have a skew distribution. We discuss below the design of such a study. The design is intended to insure that the model for the comparison is valid and that the amount of skewness is minimized. Subsequently, we present a statistical method for the comparison of the background measurements with the largest of the measurements from the suspected region. This method, which is based on the use of power transformations to achieve normality, is original in that it takes into account estimation of the transformation from the data. 

Piovoso, M. J., and Kosanovich, K. A., Applications of multivariate statistical methods to process monitoring and controller design, Int. J. Control 59(3), 743-765 (1994). 

In this chapter, we consider nonideal flow, as distinct from ideal flow (Chapter 13), of which BMF, PF, and LF are examples. By its nature, nonideal flow cannot be described exactly, but the statistical methods introduced in Chapter 13, particularly for residence time distribution (RTD), provide useful approximations both to characterize the flow and ultimately to help assess the performance of a reactor. We focus on the former here, and defer the latter to Chapter 20. However, even at this stage, it is important to realize that ignorance of the details of nonideal flow and inability to predict accurately its effect on reactor performance are major reasons for having to do physical scale-up (bench —> pilot plant - semi-works -> commercial scale) in the design of a new reactor. This is in contrast to most other types of process equipment. 

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