Six Sigma Distribution Modeling / Edition 1
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Useful when it is equally likely that readings will fall above or below the average. When a sample of several random measurements is averaged, distribution of such repeated sample averages tends to be normally distributed regardless of the distribution of the measurements being averaged.
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Can be an approximation to binomial distribution when p is equal to or less than 0. This distribution is a special case of gamma distribution with a failure rate of 2, and degrees of freedom equal to 2 divided by the number of degrees of freedom for the corresponding chi square distribution. See All Customer Reviews. Shop Textbooks. Add to Wishlist.
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Sleeper provides six sigma practitioners with the tools which will allow them to stand out from your competitors by using advanced statistical and modeling tools for more in-depth analysis. Understanding and properly utilizing statistical data distributions is one of the most important and difficult skills for a six sigma practitioner to possess.
Sleeper provides six sigma practitioners with a road map for selecting and using distributions for more precise outcomes. With the added value of Crystal Ball Modeling software, this book becomes a powerful tool for analyzing and modeling difficult data quickly and efficiently. Since , he has worked with product development teams as an engineer, statistician, project manager, Six Sigma Black Belt, and consultant.