Beyond Basic Statistics: Tips, Tricks, and Techniques Every Data Analyst Should Know 1st Edition by Kristin H. Jarman 2013

 Beyond Basic Statistics: Tips, Tricks, and Techniques Every Data Analyst Should Know 1st Edition by Kristin H. Jarman 2013
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Beyond Basic Statistics: Tips, Tricks, and Techniques Every Data Analyst Should Know 1st Edition by Kristin H. Jarman 2013

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.

Editorial Reviews Review

¡°This is a consistently entertaining and instructive book. The stories are interspersed with serious background material, and the combination works very well. This would be entirely suitable as supplementary reading for a statistics course or for an independent reading project.¡± (MAA Reviews, 20 September 2015)

From the Back Cover

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.