Free PDF Statistics (The Easier Way) with R: an informal text on applied statisticsBy Nicole M. Radziwill
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Statistics (The Easier Way) with R: an informal text on applied statisticsBy Nicole M. Radziwill
Free PDF Statistics (The Easier Way) with R: an informal text on applied statisticsBy Nicole M. Radziwill
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Want to learn R, statistics, or data analysis? Become successful fast with this unique book, which takes the approach of solving all problems analytically (using equations) before solving them using the R statistical software. (INSTRUCTORS: Course Materials are available.) This combined approach is embedded within a 12-step process for hypothesis testing, which will make statistical inference clearer than ever, while giving you a structured approach for presenting your research results.Most books about applied statistics just focus on the math and theory. Most books about the R statistical software package just focus on executing tests. But in Statistics (The Easier Way) with R, you'll see exactly what computations R is doing under the surface, helping you become a more skilled investigator and data analyst.In addition to being a great introductory text for learning applied statistics or R, Statistics (The Easier Way) with R is also suitable as a supplement for any undergraduate course in introductory or applied statistics, high school AP statistics, or Six Sigma Green Belt and Six Sigma Black Belt candidates. Errata and other notes from first printing are available at qualityandinnovation.com/errata/.
- Sales Rank: #54624 in Books
- Published on: 2015-03-21
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x 1.21" w x 7.50" l, 2.01 pounds
- Binding: Paperback
- 536 pages
From the Inside Flap
4th Update: December 5, 2015
From the Back Cover
Designed for beginning and intermediate data scientists, graduate students starting research, undergraduate students taking a first or second applied statistics class, quality improvement professionals, and consultants, this unique book provides an integrated treatment of statistical inference techniques in data analysis. Each example is solved analytically (using equations), and then also in the R software so that readers can see exactly how the computations are performed. Each technique is framed within an easy-to-apply 12-step methodology that will make planning and presenting research a breeze. If you're new to statistics, data science, or R, this book will help get you started. If you have some experience already, this book will make you more productive and enhance your understanding of foundational statistical concepts.
About the Author
Nicole Radziwill is an Associate Professor in the Department of Integrated Science and Technology at James Madison University (JMU), where she has been on the faculty since 2009. Nicole is also a Fellow of the American Society for Quality (ASQ), Certified by ASQ as a Manager of Quality and Organizational Excellence (CMQ/OE), and an ASQ Certified Six Sigma Black Belt (CSSBB). She served as a national Examiner for the Malcolm Baldrige National Quality Award (MBNQA) in 2009 and 2010, and has a PhD in Technology Management and Quality Systems from Indiana State University. She also has an MBA and a BS degree in Meteorology from Penn State. Nicole was recognized by Quality Progress in 2011 as one of the 40 New Voices of Quality, and serves as one of ASQ’s Influential Voices bloggers.
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