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The first plain-English guide to solving real-world problems with Six Sigma

So you're ready to improve your processes and products and satisfy your customers through Six Sigma--but you're not looking forward to navigating complicated statistics in order to get results. Now, Warren Brussee, a veteran Six Sigma manager who helped his teams generate millions of dollars in savings, explains how to use the powerful statistical tools of Six Sigma in easy-to-understand language.

In this step-by-step guide, you get a thorough overview of the Six Sigma methodology and techniques for successful implementation, as well as a clear explanation of DMAIC--the problem solving method used by Six Sigma Greenbelts for projects and process improvements. You'll see how to plot, interpret, and validate data for a Six Sigma project. You will use Excel to make Six Sigma problem-solving calculations in a wide range of areas, from sales and marketing to manufacturing, process work, equipment design, and more. Each chapter also features a brief review of what you've learned. Plus, you get:

• A simplified form of the most common Six Sigma tools
• All the basic Six Sigma formulas and tables
• Dozens of Six Sigma statistical problem-solving case studies
• A matrix for finding the right statistical tool to meet your needs
• Basic Greenbelt training in one concise reference

Best of all, no background in statistics is required--you can start improving quality and initiating cost-saving improvements right away. With all these benefits and authoritative guidance, Statistics for Six Sigma Made Easy is the only reference you need to facilitate real-world application of Six Sigma tools.

The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.

This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.

Coming soon:

*For students, worked solutions to a subset of exercises available on a public web site (for exercises marked "www" in the text)

*For instructors, worked solutions to remaining exercises from the Springer web site

*Lecture slides to accompany each chapter

*Data sets available for download

The perfect book for anyone who has ever dreamed of having that little place in the country, Barnyard in Your Backyard offers tried-and-true, expert advice on raising healthy, happy, productive farm animals: chickens, geese, ducks, rabbits, goats, sheep, and dairy cows.

Each chapter focuses on a different animal, discussing the pros and cons of raising the animal, housing and land requirements, feeding guidelines, health concerns, and a schedule for routine care. Species that are easy to raise, hardy, and companionable are profiled. First-time farmers will discover simple, clear instructions for caring for animals throughout the year, as well as guidelines for processing barnyard products such as milk, wool, and eggs. Combining practical advice from real experts, easy-to-use checklists and charts, a seasonal care calendar, and detailed black-and-white illustrations, Barnyard in Your Backyard offers a comprehensive review of the ins and outs - the tribulations and triumphs - of living with and caring for a small barnyard.

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