Six Sigma Distribution Modeling

Capitalize on Distribution Models to Achieve Accuracy and Precision in Six Sigma AnalysisThere's no better way for Six Sigma practitioners to improve their statistical skills than withSix Sigma Distribution Modeling.This expert guide shows them how to apply distribution models and statistical modeling tools to analyze systems with accuracy and precisionand stand out from their competitors.With the added value of Crystal Ball simulation software, this unique reference enables users to quickly select models to represent random processes...develop dynamic simulation tools...evaluate multiple strategies and outcomes in one easy procedure...and understand which inputs control the variability of their forecasts.Six Sigma Distribution Modelingalso helps technical professionals to identify and reduce their risks in the planning stage of projects, prior to costly implementation, and to graphically communicate statistical information to clients, managers, and peers. This vital statistical modeling reference features:Clear, concise guidance on selecting distribution models Goodness-of-fit testing Step-by-step calculation methods Detailed explanations of numerous distribution families Free 140-day trial of Crystal Ball softwareFilled with over 120 helpful illustrations,Six Sigma Distribution Modelingnow equips technical professionals with a full array of advanced tools for modeling, simulating, and optimizing any system in a Six Sigma project.Six Sigma Distribution Modelingnow equips Six Sigma professionals with a detailed road map for selecting and implementing distribution models for more accurate outcome projections. With the added value of Crystal Ball simulation software, this skills-building book is a powerful resource for analyzing and modeling complex systems quickly and easily.Six Sigma Distribution Modelingincludes a wealth of guidance on distribution model selection...goodness-of-fit testing...step-by-step calculation methods...and detailed explanations of numerous distribution families. This landmark reference offers expert coverage of:Selecting Distribution Models Applying Nonnormal Distributions in Six Sigma Environments Case Studies of Modeling and Simulation Terminology for Describing Distribution Models Bernoulli Distribution Beta Distribution Binomial Distribution Chi-Squared Distribution -- including chi and noncentral versions Discrete Uniform Distribution Exponential Distribution Extreme Value (Gumbel) Distribution F Distribution -- including noncentral F Gamma Distribution -- including Erlang Geometric Distribution Hypergeometric Distribution Laplace Distribution Logistic Distribution -- including loglogistic Lognormal Distribution Negative Binomial Distribution -- including Pascal Normal Distribution -- including half-normal and truncated normal Poisson Distribution Rayleigh Distribution T Distribution -- including noncentral T Triangular Distribution Uniform Distribution Weibull Distribution ReferencesWith the state-of-the-art guidance in this focused guide, Six Sigma professionals will be able to quickly convert their existing models into dynamic simulation tools, as well as evaluate multiple strategies and outcomes in one easy process. The book will also help them understand which inputs control the variability of their forecasts...reduce risks in the planning stage, prior to a costly implementation...and graphically communicate statistical information to clients, managers, and peers.Designed for real-world application in today's complex business and engineering environments,Six Sigma Distribution Modelingwill be invaluable to all practitioners who must select and apply advanced statistical modeling tools for accurate and precise Six Sigma analysis.Sleeper, Andrew is the author of 'Six Sigma Distribution Modeling', published 2006 under ISBN 9780071482783 and ISBN 0071482784.