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If these two aberrant points are moved to the left to (X, Y ) = (1, 24), the signi cance levels are now 23, 20, and 165 All three of these correlation coef cients offer protection against outliers among X values; they do the same for the Y values, but none of them take into account the overall structure of the data That is, the power of all three methods can be affected by unusual points that are not outliers among the X values (ignoring the Y values), nor outliers among the Y values (ignoring X), yet they are outliers among the scatter plot of points There are methods for detecting outliers that take into account the overall structure of the data, but the better known methods (eg.

114 3. 4.

, Rousseeuw & van Zomeren, 1990) can eliminate too many points, resulting in a poor re ection of how the bulk of the observations are associated (Wilcox, in press) It seems that no method is perfect in all situations, but a technique (called the MGV regression estimator) that addresses this issue and that seems to have practical value can be found in Wilcox (in press)..

Choose the Functions category in the Actions toolbox, and double-click a function name to add to a script.

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the systems traditionally known as DDBMSs, multidatabases (MDBs), and federated databases (FDBs). Examples of MDB and FDB experimental systems include DDTS [Devor82][Dwyer87], Mermaid [Templeton86], SDD-1 [Bernstein80], and Multibase ([Landers82], [Landers86]). Without further quali cation, the terms DBE, CDBE, and DDBE do not imply anything speci c about the architecture or implementation. Similarly, we cannot determine to which taxonomic group (taxon) the system belongs from just these abstract terms. In the next sections, we will apply our taxonomy to three speci c, traditional architectures: the DDBMS architecture, the MDBs architecture, and the FDBs architecture.

12

Inflows of Working Capital Net income (after tax) Outflows of Working Capital Cash dividends declared and paid to stockholders Net Change, Increase to Working Capital

Normally, the large data sets and images are stored in the original raw form that keeps all the measurements or all pixels In the case of image information, it is usually stored as respective BMP or JPG le, where each pixel is represented by three integer numbers within the range [0,255] that correspond to the intensity of the three basic colors Red, Green and Blue Thus the whole image information is saved as a three-dimensional RGB data set xi xi1 ; xi2 ; ; xiK ; i 1; 2; ; M, where K 3 is the dimension number and M represents the total number of pixels in this image However, it is obvious that directly using the original large raw RGB data set for further processing is not the best idea from the point of view of ef cient computation.

A good example of this is the behavior of a magnetic compass indoors. Because it measures the magnetic eld, all metallic elements are bound to disturb the measurements, and of course, this happens. Try approaching a metallic desk with an electronic compass in your hand and you will experience this very obviously.

involves compulsively pulling out one s hair) may be unable to find enough subjects for group-designed research conditions in a timely manner. Because many patients experiencing psychological distress have several diagnoses, comorbidity (two or more diagnoses) is likely to be the rule rather than the exception. Thus, it is often difficult to find people who experience only the specific disorder under investigation. For example, many children with attention deficit disorders also experience depression, oppositional defiant disorder, learning disabilities, low self-esteem, and other problems. An experimenter might think that he or she is treating the attention deficit disorder, whereas the personal contact between child and therapist might actually be treating the depression. In addition to true experimental designs that include randomization, there are quasiexperimental designs; between, within, and mixed group designs; analogue designs; case studies; correlational methods; epidemiological methods; and longitudinal and crosssectional designs. Many of these designs are not mutually exclusive. For example, correlational designs can be either longitudinal or cross-sectional, or both. A study can include both between and within group designs. The experimental and quasi-experimental approaches can also use between, within, and mixed group designs. A brief review of each of these alternative designs follows.

Therefore, we propose here another idea, according to the general concept of information granulation in [3] It is to replace the original large number of raw data with a smaller number of information granules, by an appropriate unsupervised learning algorithm [4 7] The reduced set of information granules (called also neurons in this chapter) is obtained by using the appropriate unsupervised learning algorithm, as will be shown in the next sections As a result of this learning, the set of neurons that is produced should resemble as much as possible the density distribution, shape, and the volume of the original set of RGB pixels in the three-dimensional space This process is also called information compression and the trained set of neurons is regarded as a kind of compressed information model (CIM) or granulation model (GM) of the original (raw) data set.

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