368 pages | 139 B/W Illus.
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.
Designing a Study
Counting Responses for Single Variable
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
Set Theory and Venn Diagrams
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
Chi Square Distribution
Sample Size for Mean Distribution
Probability Plots and Percentiles
Gamma Distribution and Reliability
Hypothesis Testing and OC Curves
Least Squares and Regression Analysis
Taylor Series Expansion