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print qr code vb.net Statistical Equivalence in VB.NET
Statistical Equivalence Draw Code 39 Full ASCII In Visual Basic .NET Using Barcode maker for .NET framework Control to generate, create Code 3/9 image in Visual Studio .NET applications. www.OnBarcode.comUSS Code 39 Reader In Visual Basic .NET Using Barcode reader for VS .NET Control to read, scan read, scan image in Visual Studio .NET applications. www.OnBarcode.comThe method above for determining statistical significance actually is applying the principle of statistical equivalence. Essentially, the process outlined above for determining statistical significance could be restated as Given results data from multiple tests intended to be equivalent, the data from any one of those tests may be treated as statistically significant if that data is statistically equivalent to 80 percent or more of all the tests intended to be equivalent. Mathematical determination of equivalence using such formal methods as chisquared and ttests are not common on commercial software development projects. Rather, it is generally deemed acceptable to estimate equivalence by using charts similar to those used to determine statistical significance. Bar Code Generator In VB.NET Using Barcode generator for Visual Studio .NET Control to generate, create bar code image in .NET framework applications. www.OnBarcode.comBarcode Decoder In Visual Basic .NET Using Barcode recognizer for VS .NET Control to read, scan read, scan image in .NET applications. www.OnBarcode.comStatistical Outliers
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(http://www.statsoftinc.com) is the following: USS Code 39 Maker In .NET Using Barcode maker for VS .NET Control to generate, create Code39 image in .NET applications. www.OnBarcode.comPainting EAN13 Supplement 5 In VB.NET Using Barcode encoder for .NET Control to generate, create GS1  13 image in Visual Studio .NET applications. www.OnBarcode.comOutliers are atypical, infrequent observations: data points which do not appear to follow the distribution of the rest of the sample. These may represent consistent but rare traits, or be the result of measurement errors or other anomalies which should not be modeled. 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If the majority of the measurements are the same, except for the potential outliers, the results are likely to contain genuine outliers that can be disregarded. However, if the results show similar potential outliers, these are probably valid measurements that deserve consideration. After identifying that a dataset appears to contain outliers, the next question is, how many outliers can be dismissed as atypical infrequent observations There is no set number of outliers that can be unilaterally dismissed, but rather a maximum percentage of the total number of observations. Applying the spirit of the two definitions above, a reasonable conclusion would be that up to 1 percent of the total values for a particular measurement that are outside of three standard deviations from the mean are significantly atypical and infrequent enough to be considered outliers. In summary, in practice for commercially driven software development, it is generally acceptable to say that values representing less than 1 percent of all the measurements for a particular item that are at least three standard deviations off the mean are candidates for omission in results analysis if (and only if) identical values are not found in previous or subsequent tests. To express the same concept in a more colloquial way: obviously rare and strange data points that can t immediately be explained, account for a very small part of the results, and are not identical to any results from other tests are probably outliers. A note of caution: identifying a data point as an outlier and excluding it from results summaries does not imply ignoring the data point. Excluded outliers should be tracked in some manner appropriate to the project context in order to determine, as more tests are conducted, if a pattern of concern is identified in what by all indications are outliers for individual tests. 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