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Simple randomization

For all practical purposes, source testing can be considered as simple random sampling (2). The source may be considered to be composed of such a large population of samples that the populahon N is infinite. From this population, n units are selected in such a manner that each unit of the population has an equal chance of being chosen. For the sample, determine the sample mean, y ... [Pg.534]

The aims of sampling are to establish whether eontaminants are present, their distribution and eoneentrations. Commonly-used sampling regimes inelude square grid, stratified random or simple random teehniques. Evenly-spaeed sampling points may be appropriate if the eontamination is visible otherwise judgement is required based on whether the land slopes or is flat. Samples are also taken near to the point of release. [Pg.388]

H. van der Voet, Comparing the predictive accuracy of models using a simple randomization test. Chemom. Intell. Lab. Syst., 25 (1994) 313-323. [Pg.380]

Simple random sampling involves taking increments from the bulk material in such a way that any portion of the bulk has an equal probability of being sampled. This type of sampling is often used when little information is available about the material that is being sampled. It is also commonly used when... [Pg.33]

Unlike simple random variables that have no space or time dependence, the statistics of the random velocity field in homogeneous turbulence can be described at many different levels of complexity. For example, a probabilistic theory could be formulated in terms of the set of functions U(x, t) (x, t) e R3 x R However, from a CFD modeling perspective, such a theory would be of little practical use. Thus, we will consider only one-point and two-point formulations that describe a homogeneous turbulent flow by the velocity statistics at one or two fixed points in space and/or time. [Pg.48]

While randomization eliminates bias (as least in expectation), simple randomization of all animals may not be the optimal technique for producing a sensitive test. If there is another major source of variation (e.g., sex or batch of animals), it will be better to carry out stratified randomization (i.e., carry out separate randomizations within each level of the stratifying variable). [Pg.877]

In this relatively simple random walk model an ion (e.g., a cation) can move freely between two adjacent active centres on an electrode (e.g., cathode) with an equal probability A. The centres are separated by L characteristic length units. When the ion arrives at one of the centres, it will react (e.g., undergoes a cathodic reaction) and the random walk is terminated. The centres are, therefore absorbing states. For the sake of illustration, L = 4 is postulated, i.e., Si and s5 are the absorbing states, if 1 and 5 denote the positions of the active centres on the surface, and s2, s3, and s4 are intermediate states, or ion positions, LIA characteristic units apart. The transitional probabilities (n) = Pr[i-, —>, Sj in n steps] must add up to unity, but their individual values can be any number on the [0, 1] domain. [Pg.290]

Figure 2.7 Simple random walk in 3-D showing equal magnitude steps r, to //. The end-to-end vector is R... Figure 2.7 Simple random walk in 3-D showing equal magnitude steps r, to //. The end-to-end vector is R...
Following the same line of thought, we are led to propose that high temperature H iO as) has a simple random network structure derived from an ice I type lattice [i.e. like Ge(as) and Si(as)]. [Pg.190]

How would the properties of two polymers containing the same amounts of monomer A and B differ if one polymer is a simple random copolymer and the other polymer is a block copolymer ... [Pg.236]

To show the relationship between pn(m) expressing the probabilities of numbers and p x) describing a continuous spatial distribution of a quantity like concentration, we make use of the analogy between the integers n and m, which describe the simple random walk model shown in Fig. 18.1, and the time and space coordinates t and x, that is t = n At and x = m Ax. The incremental quantities, At and Ax, are characteristic for random motions the latter is the mean free path which is commonly denoted as X = Ax, the former is associated with the mean velocity ux= Ax/At = XIAt. Thus, we get the following substitution rules ... [Pg.783]

Individual steps of restricted walks are no longer independent, since the restriction introduces a correlation. However, this correlation is short range in character and falls off exponentially with increasing separation of steps. The short range correlation is insufficient to change the characteristic features of the walk from those of a simple random walk. [Pg.233]

Internally plasticized systems consisting of simple random copolymers, designed for use in flexible plastic articles, generally have an unsatisfactorily narrow use temperature range, since they soften more sharply than analogous externally plasticized systems or polyblends (mixtures of two or more polymers). [Pg.9]

Simple random sampling. Simple random sampling is performed directly on the whole population (area or section) under investigation. Any increment taken from the parent population has an equal chance of being selected. In practice, the problem is that the sample has to be taken in space or time after random number generation, not haphazardly. [Pg.122]

Simple random sampling should, therefore, generally be used either in conjunction with other sampling methods or in cases involving only small study populations [SPRINGER and McCLURE, 1988],... [Pg.123]

Stratified random sampling, which is a variation of simple random sampling, is used for media that are stratified with respect to their chemical and physical properties. Each stratum is identified and randomly sampled. The number of grab samples and the sampling point selection depend on the nature of contaminant distribution within each stratum. Stratified random sampling is used for the characterization of multiphase liquid wastes or process waste batches that undergo stratification over time and/or space. [Pg.64]

The underlying assumptions of the Student s t-test include simple random and systematic sampling and a normal distribution of the sample mean. The upper limit of the confidence interval for the mean concentration is compared to the action level to determine whether solid waste contains a contaminant of concern at a hazardous level. (The calculation is conducted according to Equation 10, Appendix 1.) A contaminant of concern is not considered to be present at a hazardous level, if the upper limit of the confidence interval is below the action level. Otherwise, the opposite conclusion is reached. Example 5.13 demonstrates the application of this test for deciding whether the waste is hazardous or not. [Pg.293]

Sample mean for simple random sampling and systematic random sampling ... [Pg.299]


See other pages where Simple randomization is mentioned: [Pg.185]    [Pg.1905]    [Pg.562]    [Pg.87]    [Pg.87]    [Pg.87]    [Pg.140]    [Pg.175]    [Pg.512]    [Pg.444]    [Pg.30]    [Pg.30]    [Pg.34]    [Pg.144]    [Pg.365]    [Pg.194]    [Pg.123]    [Pg.20]    [Pg.79]    [Pg.180]    [Pg.265]    [Pg.245]    [Pg.102]    [Pg.239]    [Pg.123]    [Pg.64]    [Pg.64]    [Pg.115]   
See also in sourсe #XX -- [ Pg.37 ]




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