Statistics The Easier Way With R

"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."--Back cover

Covers exploratory data analysis using R, ggplot, and the tidyverse, with 10 parametric statistical inference tests, and examples using a 7 Step method blending estimation, analytical and computational solutions.

Covers exploratory data analysis, 10 parametric statistical inference tests, and examples using a 7 Step method that blends estimation, analytical solutions, and computational solutions.

"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."--Back cover

"Learning Statistics with R" covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com

The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you’ll find everything you need to begin using R effectively for statistical analysis. You’ll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data and performing statistical tests and modeling. You’ll even learn how to create impressive data visualizations with R’s basic graphics tools and contributed packages, like ggplot2 and ggvis, as well as interactive 3D visualizations using the rgl package. Dozens of hands-on exercises (with downloadable solutions) take you from theory to practice, as you learn: –The fundamentals of programming in R, including how to write data frames, create functions, and use variables, statements, and loops –Statistical concepts like exploratory data analysis, probabilities, hypothesis tests, and regression modeling, and how to execute them in R –How to access R’s thousands of functions, libraries, and data sets –How to draw valid and useful conclusions from your data –How to create publication-quality graphics of your results Combining detailed explanations with real-world examples and exercises, this book will provide you with a solid understanding of both statistics and the depth of R’s functionality. Make The Book of R your doorway into the growing world of data analysis.

Covers exploratory data analysis, 10 parametric statistical inference tests, and examples using a 7 Step method that blends estimation, analytical solutions, and computational solutions.

The second edition of a bestselling textbook, Using R for Introductory Statistics guides students through the basics of R, helping them overcome the sometimes steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular, while updating data, examples, and changes to R in line with the current version. See What’s New in the Second Edition: Increased emphasis on more idiomatic R provides a grounding in the functionality of base R. Discussions of the use of RStudio helps new R users avoid as many pitfalls as possible. Use of knitr package makes code easier to read and therefore easier to reason about. Additional information on computer-intensive approaches motivates the traditional approach. Updated examples and data make the information current and topical. The book has an accompanying package, UsingR, available from CRAN, R’s repository of user-contributed packages. The package contains the data sets mentioned in the text (data(package="UsingR")), answers to selected problems (answers()), a few demonstrations (demo()), the errata (errata()), and sample code from the text. The topics of this text line up closely with traditional teaching progression; however, the book also highlights computer-intensive approaches to motivate the more traditional approach. The authors emphasize realistic data and examples and rely on visualization techniques to gather insight. They introduce statistics and R seamlessly, giving students the tools they need to use R and the information they need to navigate the sometimes complex world of statistical computing.