Questions and Answers on Statistics

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Answering Eight Questions Distinguish common variation and special variation. Provide specific examples The common cause variation and special cause variation are two original methods of variation process in statistical analysis. Common cause variation Variation is a necessary facet in statistical approaches of analyzing procedures and results. Common cause variation is characterized by phenomena that are active within a given system. Active phenomena restate significance separation or difference marked by the variation parameter. Within active phenomena, it is possible to have predictable variation. Study analysis that makes use of common cause variation has the tendency of reflecting data that can be predicted probably. The basement of a common cause variation is mired by irregularities of historical experience. The basement is not stable like that of the special cause variation. Moreover, significance is lacking with a common cause variation. This significance is relayed with individual high or low values within the measurements of any study. Examples of common cause variation are variability in settings, computer time responses, and quality control error (Kendall & Rollins, 2003). Special-cause variation This form of variation is new, previously neglected or emergent, and unanticipated within a system that makes up a study. It has no measure of predictability because not every happening within it is foreseeable. Most facets that could result to this form

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