1st Edition

Six Sigma and Beyond Statistics and Probability, Volume III

By D.H. Stamatis Copyright 2002
    368 Pages 139 B/W Illustrations
    by CRC Press

    Researchers and professionals in all walks of life need to use the many tools offered by the statistical world, but often do not have the necessary experience in both concept and application. No matter what your profession, sooner or later numbers need to be crunched, and often you need to understand how to do it, and why it is important. Quality control is no different. Six Sigma and Beyond: Statistics and Probability covers the concepts of some useful statistical tools, appropriate formulae for specific tools, the connection of statistics to probability, and how to use them.

    This volume introduces the relationship of statistics, probability, and reliability as they apply to quality in general and to Six Sigma in particular. The author brings the theoretical into the practical by providing statistical techniques, tests, and methods that the reader can use in any organization. He reviews basic parametric and non-parametric statistics, probability concepts and applications, and addresses topics for both measurable and attribute characteristics. He delineates the importance of collecting, analyzing, and interpreting data not from an academic point of view but from a practical perspective.

    This is not a textbook but a guide for anyone interested in statistical, probability, and reliability to improve processes and profitability in their organizations. When you begin a study of something, you want to do it well. You want to design a good study, analyze the results properly, and prepare a cogent report that summarizes what you've found. Six Sigma and Beyond: Statistics and Probability shows you how to use statistical tools to improve your processes and give your organization the competitive edge.

    Statistical Concepts
    Designing a Study
    Counting Responses for Single Variable
    Summarizing Data
    Counting Responses for Combinations
    Changing the Coding Scheme
    Looking at Means
    Means from Samples
    Working with the Normal Distribution
    Testing Hypothesis - Two Independent Means
    Testing Hypothesis - Two Dependent Means
    Testing Hypothesis about Independence
    Comparing Several Means
    Plotting Data
    Probability Changes
    Set Theory and Venn Diagrams
    Probability Concepts
    Discrete and Random Variables
    Binomial and Poison Distributions
    Continuous and Uniform Distributions
    Normalizing Binomial and Central Limit Theorem
    Functions of Random Variables
    Exponential Distribution and Reliability
    Poison Process
    Chi Square Distribution
    T Distribution
    Sample Size for Mean Distribution
    Sampling Theory
    Probability Plots and Percentiles
    Reliability Concepts
    Failure Rates
    Reliability Rate
    ROCOF Plot
    Weibull Distribution
    Gamma Distribution and Reliability
    Hypothesis Testing and OC Curves
    Least Squares and Regression Analysis
    Taylor Series Expansion


    D.H. Stamatis