Statistics (the Easier Way) with R, 2nd Edition

Statistics (the Easier Way) with R, 2nd Edition

Lapis Lucera presents the statistics textbook you always dreamed of having. We were looking for a book to use in classes for undergraduate sophomores and juniors, but none of the textbooks we looked at (and we looked at over 100 books!) had all of the things we really, really wanted. So we wrote one ourselves.

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This book includes:

  • An integrated treatment of theory and practice
    All of our stats textbooks had a lot of formulas, and no information about how to do what the formulas do in the R statistical software. All of our R textbooks had a lot of information about how to run the commands, but not really much information about what formulas were being used. We wanted a book that would show how to solve problems analytically (using the equations), and then show how they’re done in R. If there were discrepancies between the stats textbook answers and the R answers, we wanted to know why. A lot of times, the developers of R packages use very sophisticated adjustments and corrections, which we only became aware of because our analytical solutions didn’t match the R output. At first, we thought we were wrong. But later, we realized we were right, and R was right: we were just doing different things. we wanted students to know what was going on under the hood, and have an awareness of exactly which methods R was using at every moment.
  • An easy way to develop research questions for observational studies and organize the presentation of results
    We always do small research projects in our classes, and in our opinion, this is the best way for students to get a strong grasp of the fundamental statistical concepts. But they always have the same questions: Which statistical test should I use? How should I phrase my research question? What should I include in my report? We wanted a book that made developing statistical research questions easy. In fact, we know a lot of PhD students that would have loved to have this book while they are proposing, conducting, and defending their dissertations.
  • A confidence interval cookbook
    This is probably one of the most important things we want our students to leave the class remembering: that from whatever sample you collect, you can construct a confidence interval that will give you an idea of what the true population parameter should be. You don’t even need to do a hypothesis test! but it can be difficult to remember which formula to use… so we wanted an easy reference where we’d be able to look things up, and find out really easily how to use R to construct those confidence intervals. Furthermore, some of the confidence intervals that everyone is taught in an introductory statistics course are wildly inaccurate – and statisticians know this. But they hesitate to scare away novice data analysts with long, scary looking equations, and so students keep learning those inaccurate methods and believing they’re good. Since so many people never get beyond introductory statistics and still turn into researchers in other fields, we thought this was horrible. We want to make sure our students know the best way to do each confidence interval in their first class… even if the equations are not as friendly.
  • An inference test cookbook
    We wanted a book that stepped through each of the primary parametric inference tests analytically (using the equations), and then showed how it was done in R. If there were discrepancies, we wanted to know why. We wanted an easy way to remember the assumptions for each test, and when to use a pooled standard deviation versus an unpooled one. There’s a lot to keep track of! We wanted a reference that it would make it easy to keep track of all of it: assumptions, tests for assumptions, equations, R code, and diagnostic plots.
  • No step left behind
    It’s really frustrating how so many R books assume you can do a psychic fill-in-the-blank for missing code. Since we’ve been using R for several years now, we’ve gotten to the point where our psychic abilities are pretty good, and at least 60% of the time we can figure out the missing pieces. But wow, what a waste of time! So we wanted a book that had all of the steps for each example. Even if it was a little repetitive. We may have missed this in a few places, but think beginners will have a much easier time with this book. Also, we put all of the data and functions on GitHub for people to run the examples with. We're growing this slowly, but we don’t want people to be left in the lurch.
  • An easy way to produce any of the charts and graphs in the book
    One of our pet peeves about R books is that the authors generate beautiful charts and graphs, and then you’re reading through the book and say “Yes!! Yes!! That’s the chart I need for my report… I want to do that… how did they do that?” and they don’t tell you anywhere how they did it. We did not want there to be any secrets in this book. If we generated a page of interesting-looking simulated distributions, we wanted you to know how we did it (just in case you want to do it later).

Errata

We know that no book is perfect, and that technology books, in particular, tend to have sections that grow outdated quickly. Any errors and omissions from earlier printings (which have been taken care of in later printings) are being recorded at the errata page.

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 RadziwillNicole Radziwill is an Associate Professor (as of Fall 2015) 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. Nicole is a co-founder of Lapis Lucera.

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