Data acquisition and preparation commonly consume approximately 75% of the effort and time of total data analysis. JMP provides many visual, intuitive, and even innovative data-preparation capabilities that enable you to make the most of your organization's data.
Preparing Data for Analysis with JMP® is organized within a framework of statistical investigations and model-building and illustrates the new data-handling features in JMP, such as the Query Builder. Useful to students and programmers with little or no JMP experience, or those looking to learn the new data-management features and techniques, it uses a practical approach to getting started with plenty of examples. Using step-by-step demonstrations and screenshots, this book walks you through the most commonly used data-management techniques that also include lots of tips on how to avoid common problems.
With this book, you will learn how to:
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Robert Carver is Professor of Business Administration at Stonehill College in Easton, Massachusetts, and Senior Lecturer at the International Business School at Brandeis University in Waltham, Massachusetts. At both institutions, he teaches courses on business analytics in addition to general management courses. He is the author of Practical Data Analysis with JMP ®, Second Edition,. His primary research interest is statistics education, and he is an Associate Editor for the Journal of Statistics Education. A JMP user since 2006, Carver holds an A.B. in political science from Amherst College in Amherst, Massachusetts, and an M.P.P. and Ph.D. in public policy from the University of Michigan at Ann Arbor.
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Librería: World of Books Inc, Montgomery, IL, Estados Unidos de America
Paperback. Condición: Very Good. An introduction on how to use JMP to manage data for analysis. The book is organized within a framework of statistical investigations and model-building (where data acquisition and prep commonly eat up something like 75% of the effort and time) and in doing so illustrates the new data handling features in JMP, such as Query Builder. Nº de ref. del artículo: CIN1629604186VG
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