Beyond Basic Statistics: Tips, Tricks, and Techniques Every Data Analyst Should Know - Softcover

Über die Autorin bzw. den Autor

Kristin H. Jarman, PhD, is Senior Research Scientist at the Pacific Northwest National Laboratory in Richland, Washington, where she applies basic and not-so-basic statistical analyses to her own set of research challenges. With over 20 years of experience, she has successfully presented results to those with little or no mathematical sophistication. Dr. Jarman is the author of numerous peer-reviewed journal articles as well as The Art of Data Analysis: How to Answer Almost Any Question Using Basic Statistics, also published by Wiley.

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Features basic statistical concepts as a tool for thinking critically, wading through large quantities of information, and answering practical, everyday questions

Written in an engaging and inviting manner, Beyond Basic Statistics: Tips, Tricks, and Techniques Every Data Analyst Should Know presents the more subjective side of statistics―the art of data analytics. Each chapter explores a different question using fun, common sense examples that illustrate the concepts, methods, and applications of statistical techniques.

Without going into the specifics of theorems, propositions, or formulas, the book effectively demonstrates statistics as a useful problem-solving tool. In addition, the author demonstrates how statistics is a tool for thinking critically, wading through large volumes of information, and answering life’s important questions.

Beyond Basic Statistics: Tips, Tricks, and Techniques Every Data Analyst Should Know also features:

• Plentiful examples throughout aimed to strengthen readers’ understanding of the statistical concepts and methods
• A step-by-step approach to elementary statistical topics such as sampling, hypothesis tests, outlier detection, normality tests, robust statistics, and multiple regression
• A case study in each chapter that illustrates the use of the presented techniques
• Highlights of well-known shortcomings that can lead to false conclusions
• An introduction to advanced techniques such as validation and bootstrapping

Featuring examples that are engaging and non-application specific, the book appeals to a broad audience of students and professionals alike, specifically students of undergraduate statistics, managers, medical professionals, and anyone who has to make decisions based on raw data or compiled results.

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Aus dem Klappentext

Features basic statistical concepts as a tool for thinking critically, wading through large quantities of information, and answering practical, everyday questions

Written in an engaging and inviting manner, Beyond Basic Statistics: Tips, Tricks, and Techniques Every Data Analyst Should Know presents the more subjective side of statistics – the art of data analytics. Each chapter explores a different question using fun, common sense examples that illustrate the concepts, methods, and applications of statistical techniques.

Without going into the specifics of theorems, propositions, or formulas, the book effectively demonstrates statistics as a useful problem-solving tool. In addition, the author demonstrates how statistics is a tool for thinking critically, wading through large volumes of information, and answering life's important questions. Beyond Basic Statistics: Tips, Tricks, and Techniques Every Data Analyst Should Know also features:

• Plentiful examples throughout aimed to strengthen readers understanding  of the statistical concepts and methods
• A step-by-step approach to elementary statistical topics such as sampling, hypothesis tests, outlier detection, normality tests, robust statistics, and multiple regression
• A case study in each chapter illustrating the use of these techniques
• Highlights of well-known shortcomings that can lead to false conclusions
• An introduction to advanced techniques such as validation and bootstrapping

Featuring examples that are engaging and non-application specific, this book appeals to a broad audience of students and professionals alike, specifically students of undergraduate statistics, managers, medical professionals, and anyone who has to make decisions based on raw data or compiled results.