Statistical transformations for normality can be very important in the tracking of high-level business metrics from a process-output point of view. If this is not done, common cause variation of a process can be considered as special cause events that require immediate attention; i.e., firefighting common-cause variation as though there were special causes. The Integrated Enterprise Excellence (IEE) business management system addresses this issue structurally within its framework, which offers predictive scorecard reporting.
Statistical Transformations for Normality: NOT Transforming The Data Can Be Fatal To Your Analysis
There is debate whether, in statistical process control (SPC), a data transformation should be considered when constructing an individuals chart. This article shows, using real data, why an appropriate data transformation is very important to determine the best action or non-action to take in both manufacturing and transactional processes at any point in time.
Described in the PDF article link below is an enhancement to traditional process control charting methodology. The described statistical business performance charting (SBPC) system can, for example, reduce firefighting when the approach replaces organizational goal-setting red-yellow-green scorecards, which often have no structured plan for making improvements.
In addition, the methodology provides predictive performance statements. Donald Wheeler and Forrest have a difference of opinion about the need to transform data when a transformation makes physical sense. The reason for writing this article is to provide information on the reasoning for Forrest’s position. Hopefully this supplemental explanation will provide readers with enough insight so that they can make the best logical decision relative to considering data transformations or not.
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