The standard way to proceed would be to fit the model to the data relative to each experimental unit, one at a time, thus obtaining a sample of parameter estimates, one for each experimental tumor observed. The sample mean and dispersion of these estimates would then constitute our estimate of the population mean and dispersion. By the same token, we could find the mean and dispersion in the Control and Treated subsamples. [Pg.96]

The table is not exhaustive, although it does include a majority of experimental designs that are used. One-at-a-time designs are the usual non-statistical type of experiments that are often carried out by scientists in all disciplines. Not included explicitly, however, are experimental designs that are generated from combinations of listed items. For example, a multi-factor experiment may have several levels of some of the factors but only two levels of other factors. [Pg.62]

Using experimental design such as Surface Response Method optimises the product formulation. This method is more satisfactory and effective than other methods such as classical one-at-a-time or mathematical methods because it can study many variables simultaneously with a low number of observations, saving time and costs [6]. Hence in this research, statistical experimental design or mixture design is used in this work in order to optimise the MUF resin formulation. [Pg.713]

Thus, when statisticians got into the act, there saw a need to retain the information that was not included in the one-at-a-time plans, while still keeping the total number of experiments manageable the birth of statistical experimental designs . Several types of statistical experimental designs have been developed over the years, with, of course, [Pg.91]

The experiments should be carefully set up so as to create a controlled situation in which one can make careful observations after altering the experimental parameters, preferably one at a time. The results must be reproducible (to within experimental error) and, as more and more experiments are conducted, a pattern should begin to emerge, from which a comparison to the current theory can be made. [Pg.14]

A three factor central composite design was used to study the volatiles formed as functions of the three independant variables temperature, rhamnose/proline ratio and pH. In this tjrpe of design the variables are changed both simultaneously and one-at-a-time. The specific experimental points are listed in Table I. [Pg.218]

In a laboratory environment it is possible to investigate a general deactivation phenomenon by changing variables one at a time. However, in an industrial plant the deactivation processes usually occur simultaneously and the relative and interrelated importance of the processes are more difficult to assess. In this publication we report on an experimental design that was used to investigate simultaneous deactivation processes in a pilot plant. [Pg.352]

The contribution of the triad to catalysis was quantified experimentally by protein engineering experiments on subtilisin.100 This is discussed in detail in Chapter 15, Part 2, section B. Replacement of the equivalent of Asp-102, His-57, and Ser-195 one at a time by alanine reduced the value of cat by factors of 3 X 104, 2 X 106, and 2 X 106, respectively. Converting all three to alanine also decreased activity by 2 X 106. [Pg.575]

A more interesting problem from both the experimental and theoretical point of view is the lateral diffusion of phospholipids in mixtures of lipids, when both solid and fluid phases coexist. At least three questions arise in connection with this problem. (1) What is the rate of lateral diffusion of phospholipids in solid solution domains (2) To what extent do solid solution domains act as obstacles to the lateral diffusion of lipid molecules in fluid domains (3) To what extent are there composition and density fluctuations present in fluid lipid bilayers, and to what extent do these fluctuations affect lateral diffusion Let us consider these questions one at a time, bearing in mind that these questions may to some extent be interrelated. [Pg.259]

Four experimental variables were assumed to influence the result and were studied by a two-level factorial design, see Table 14. In the original paper [70], the authors analyzed five derived responses which were calculated from the observed yields. The derived responses were analysed one at a time. [Pg.50]

Because variables in models are often highly correlated, when experimental data are collected, the xrx matrix in Equation 2.9 can be badly conditioned (see Appendix A), and thus the estimates of the values of the coefficients in a model can have considerable associated uncertainty. The method of factorial experimental design forces the data to be orthogonal and avoids this problem. This method allows you to determine the relative importance of each input variable and thus to develop a parsimonious model, one that includes only the most important variables and effects. Factorial experiments also represent efficient experimentation. You systematically plan and conduct experiments in which all of the variables are changed simultaneously rather than one at a time, thus reducing the number of experiments needed. [Pg.62]

The following table lists the parameters for FAAS, EAAS, and FAES, which are both dependent and independent. A yes in any column indicates that the listed parameter is appropriate for that technique. If an optimization is necessary when independent parameters are involved, it is important to use a systematic approach that permits one to vary all parameter values to develop the optimum for each. If the variables are simply varied one at a time, false optimum values and poor results will be obtained. Experimental design techniques are required for good results one of the best approaches is the SIMPLEX technique, which has been fully discussed in the literature.15 [Pg.510]

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