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In statistics, homogeneity and its opposite, heterogeneity, arise in describing the properties of a dataset, or several datasets. They relate to the validity of the often convenient assumption that the statistical properties of any one part of an overall dataset are the same as any other part.
Template:Convert • Invokes the module; some parameters can be used to configure the module. Module:Convert • Should not be changed. If a change appears desirable, please discuss first. Module:Convert/data • Unit definitions—must not be changed (see below). Module:Convert/text • Text for options and messages—should be changed.
Open your document in Word, and "save as" an HTML file. Open the HTML file in a text editor and copy the HTML source code to the clipboard. Paste the HTML source into the large text box labeled "HTML markup:" on the html to wiki page. Click the blue Convert button at the bottom of the page.
Pertaining file extensions include:.docx – Word document.docm – Word macro-enabled document; same as docx, but may contain macros and scripts.dotx – Word template.dotm – Word macro-enabled template; same as dotx, but may contain macros and scripts; Other formats.pdf – PDF documents.wll – Word add-in.wwl – Word add-in
The output amount is passed to Template:Convert/round which determines the precision (using Template:Max/2 & Template:Ordomag) as passed to Template:Rnd which performs the rounding. To change results, based on a conversion involving feet, then parameters would need to be passed from T:Convert/ft, into T:Convert/LoffAoffDbSoff, then into T ...
The two wikidata modules are only needed if convert is used to access information from Wikidata.. If your Wikipedia is currently using a convert template, the new template should be copied to Template:Convert/sandbox.
Statistical testing for a non-zero heterogeneity variance is often done based on Cochran's Q [13] or related test procedures. This common procedure however is questionable for several reasons, namely, the low power of such tests [14] especially in the very common case of only few estimates being combined in the analysis, [15] [7] as well as the specification of homogeneity as the null ...
An implication of this is that where multiple random intercepts exist, the heterogeneity within certain combinations will be minimized. CMA can thus be used as a data mining method to identify the number of intercepts that may be present in the dataset by looking at which studies are included in the local minima that may be obtained through recombination.