Continuous data

Scale-Up Based on Energy Since large mills are usually sized on the basis of power draft (see subsection Energy Laws ), it is appropriate to sc e up or convert from batch to continuous data by... 

In both electron post-ionization techniques mass analysis is performed by means of a quadrupole mass analyzer (Sect. 3.1.2.2), and pulse counting by means of a dynode multiplier. In contrast with a magnetic sector field, a quadrupole enables swift switching between mass settings, thus enabling continuous data acquisition for many elements even at high sputter rates within thin layers. 

Independently of the amount of data and the way they are acquired, the definition of the learning problem simulates a continuous data flow. Such... 

Obtaining Inferential Statistics from Continuous Data Analysis... 

A wide range of conttol systems is used in household appliances. A standard conttol loop consists of sensors, control units and actuators. The appliances become more powerful and efficient, as the technology is developed and integrated into microsystems. Matchbox-sized sensors can be equipped with wireless radio transceivers and their own miniature operating system to tiansmit continuous data to the facility manager. 

We believe that only large scale simulation studies can truly advance the discipline by helping us establish acceptable tolerance intervals. However, individual (parallel) simulations such as those just described can also be useful. These simulations can serve as suitability tests that is, they can tell the researcher whether a particular research data set can, in principle, answer the questions of interest. In other words, if taxonic and dimensional data are generated to simulate the research data and the researcher finds few differences between the simulated sets (e.g., they yielded the same number of taxonic plots), then there is little sense in analyzing the research data because it is unlikely to give a clear answer. With suitability testing, a modest simulation study (e.g., 20 taxonic and 20 continuous data sets) is preferred to individual simulations because it would yield clearer and more reliable results. 

Contrasted with these continuous data, however, we have discontinuous (or discrete) data, which can only assume certain fixed numerical values. In these cases our choice of statistical tools or tests is, as we will find later, more limited. 

The Cochran test should be used to compare two groups of continuous data when the variances (as indicated by the F test) are heterogeneous and the numbers of data within the groups are not equal (N N2). This is the situation, for example, when the data, though expected to be randomly distributed, were found not to be (Cochran and Cox, 1975, pp. 100-102). 

ANOVA is used for comparison of three or more groups of continuous data when the variances are homogeneous and the data are independent and normally distributed. 

Finally, there is the contour plot, which is used to depict the relationships in a three variable, continuous data system. That is, a contour plot visually portrays each contour as a locus of the values of two variables associated with a constant value of the third variable. An example would be a relief map that gives both latitude and longitude of constant altitude using contour lines. 

There is a hierarchy of usefulness of data, according to how well it can be statistically manipulated. The accepted order is continuous data > ordinal data > nominal data. 

The reason for these substantial errors is quite simply that in a discontinuous assay, the researcher assumes that product formation remains (approximately) linear for the duration of the incubation period. Although this may hold true for high substrate concentrations (in this example), it is clear from continuous data that deviation from linearity is substantial at lower substrate concentrations. Accordingly, what must be done prior to use of a discontinuous assay protocol is a timecourse assessment, in which the concentration of product formed from both low and high concentrations of substrate is determined at various time... 

For continuous data, there are still a number of outstanding issues regarding the benchmark including (Crump 2002) (1) definition of an adverse effect (2) whether to calculate the BMD from a continuous health outcome, or first convert the continuous response to a binary (yes/no) response (3) quantitative definition of the BMD, in particular in such a manner that BMD from continuous and binary data are commensurate (4) selection of a mathematical dose-response model for calculating a BMD (5) selection of the level of risk to which the BMD corresponds and (6) selection of a statistical methodology for implementing the calculation. 

Advantages of the NOAEL approach over the BMD approach are that the NOAEL approach is easy to understand, it is not dependent on a mathematical model being correct, and it can easily be applied on both discrete and continuous data. The major disadvantages are that the NOAEL approach only provides knowledge on the magnitude of risk at the dose levels of the particular study, and that the NOAEL is strongly dependent on study group sizes. 

Crump, K.S. 1984. A new method for determining aUowable daily intakes. Fundam. Appl. Toxicol. 4 854-871. Crump, K. 2002. Critical issues in benchmark calculations from continuous data. Crit. Rev. Toxicol. 32 133-153. 

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