Statistics and probability
著者
書誌事項
Statistics and probability
（Six sigma and beyond, v. 3）
St. Lucie Press, c2003
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
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. Regression. 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. MTBF. MTBR. ROCOF Plot. Weibull Distribution. Gamma Distribution and Reliability. Hypothesis Testing and OC Curves. Least Squares and Regression Analysis. Taylor Series Expansion.
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