Statistical Thinking


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Statistical Thinking


Statistical Thinking

Author: Roger W. Hoerl

language: en

Publisher: John Wiley & Sons

Release Date: 2012-04-09


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How statistical thinking and methodology can help you make crucial business decisions Straightforward and insightful, Statistical Thinking: Improving Business Performance, Second Edition, prepares you for business leadership by developing your capacity to apply statistical thinking to improve business processes. Unique and compelling, this book shows you how to derive actionable conclusions from data analysis, solve real problems, and improve real processes. Here, you'll discover how to implement statistical thinking and methodology in your work to improve business performance. Explores why statistical thinking is necessary and helpful Provides case studies that illustrate how to integrate several statistical tools into the decision-making process Facilitates and encourages an experiential learning environment to enable you to apply material to actual problems With an in-depth discussion of JMP® software, the new edition of this important book focuses on skills to improve business processes, including collecting data appropriate for a specified purpose, recognizing limitations in existing data, and understanding the limitations of statistical analyses.

Regression Modeling Strategies


Regression Modeling Strategies

Author: Frank E. Harrell

language: en

Publisher: Springer Science & Business Media

Release Date: 2013-03-09


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Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining".

Flaws and Fallacies in Statistical Thinking


Flaws and Fallacies in Statistical Thinking

Author: Stephen K. Campbell

language: en

Publisher: Courier Corporation

Release Date: 2004-01-01


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Nontechnical survey helps to improve the ability to judge the quality of statistical evidence and to make better-informed decisions. Discusses common statistical pitfalls: unrealistic estimates, improper comparisons, premature conclusions, and faulty thinking about probability. Playful in tone, accurate in nature; valuable in and out of the classroom. 1974 edition.


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