This webinar explains what it means to be ?normally distributed?, how to assess normality, how to test for normality, and how to transform non-normal data into normal data. Normality Tests and normality transformations are a combination of graphical and numerical methods that have been in use for many decades. These methods are essential to apply whenever a statistical test or method is used whose fundamental assumption is that the inputted data is normally distributed. Normality ?testing? involves creating a ?normal probability plot? and calculating simple statistics for comparison to critical values in published tables. A normality ?transformation? involves making simple changes to each of the raw-data values, such that the resulting values are more normally distributed than the original raw data. Evaluation of the results of ?tests? and ?transformations? involves some objective and some subjective decisions; this webinar provides guidance on both types of decision making. Areas Covered in the Session : Regulatory requirements Binomial distribution Historical origin of the Normal distribution Normal distribution formula, histogram, and curve Validity of Normality transformations Necessity for transformation to Normality How to use Normality transformations Normal Probability Plot How to evaluate Normality of raw data and transformed data Significance tests for Normality Evaluating the results of a Normality test Recommendations for implementation Recommended reference textbooks Who Will Benefit: QA/QC Departments Process Engineering Departments Manufacturing Engineering Departments QC/QC Technicians Manufacturing Technicians Research & Development Engineers
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