In academia, Weibull analysis has modeled such diverse phenomena as the length of labor strikes, AIDS mortality and earthquake probabilities.
Weibull analysis can make predictions about a product's life, compare the reliability of competing product designs, statistically establish warranty policies or proactively manage spare parts inventories, to name just a few common industrial applications. I predict readers in both groups will be glad they stuck around.įor the uninitiated, Weibull analysis is a method for modeling data sets containing values greater than zero, such as failure data. You haven't turned the page yet? Those of you who remain probably fall under one of two categories: those familiar with reliability data analysis, and Excel enthusiasts who are curious to learn one more way to exploit this versatile software. This article presents a how-to approach for one such advanced technique-Weibull analysis. And with a little guidance, users can employ more advanced statistical methods with Excel. But with some creativity, users can produce tools like control charts, Pareto charts and box-and-whisker plots (see "Using Excel for Data Analysis," Quality Digest, October 1997). Fewer still put these capabilities to work for process improvement, product improvement and profit.Most Excel users are aware of the common formulas and charts. Yet few people realize the extent of Excel's analytical capabilities. M any people use Microsoft Excel on a daily basis.
RELIABILITY DATA ANALYSIS WITH EXCEL AND MINITAB KENNETH DOWNLOAD
Netscape users may need to download this Zipped version instead. Is complemented by a comprehensive website providing an introduction to R, and installations of JMP scripts and MINITAB macros, including effective tutorials with introductory material: На нашем сайте можно скачать книгу "Modern Industrial Statistics. Provides over 40 data sets representing real-life case studies. Includes exercises at the end of each chapter to aid learning and test knowledge. Provides examples in R using a dedicated package called MISTAT, and also refers to MINITAB and JMP. Modern Industrial Statistics: With applications in R, MINITAB and JMP: Combines a practical approach with theoretical foundations and computational support. Graduate and post-graduate students in the areas of statistical quality and engineering, as well as industrial statisticians, researchers and practitioners in these fields will all benefit from the comprehensive combination of theoretical and practical information provided in this single volume. The book can be used for workshops or courses on acceptance sampling, statistical process control, design of experiments, and reliability. Topics addressed include computer-intensive data analysis, acceptance sampling, univariate and multivariate statistical process control, design of experiments, quality by design, and reliability using classical and Bayesian methods. with applications in R, MINITAB and JMP"įully revised and updated, this book combines a theoretical background with examples and references to R, MINITAB and JMP, enabling practitioners to find state-of-the-art material on both foundation and implementation tools to support their work.
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