An R Companion for Applied Statistics I: Basic Bivariate Techniques breaks the language of the R software down into manageable chunks in order to help students learn how to use it. R is a powerful, flexible, and free tool. However, the flexibility—which eventually becomes a great asset—can make the initial learning curve appear steep. This book introduces a few key aspects of the R tool. As readers become comfortable with these aspects, they develop a foundation from which to more thoroughly explore R and the packages available for it. This introduction does not explain every possible way to analyze data or perform a specific type of analysis. Rather, it focuses on the analyses that are traditionally included in an undergraduate statistics course and provides one or two ways to run these analyses in R. Datasets and scripts to run the examples are provided on an accompanying website. The book has been designed to be an R companion to Warner's Applied Statistics I, Third Edition, and includes end-of-chapter instructions for replicating the examples from that book in R. However, this text can also be used as a stand-alone R guide, without reference to the Warner text.
Communications for Statistical Applications and Methods, 22, 665–674. https://doi .org/10.5351/CSAM.2015.22.6.665 Kim, S. (2015b). ppcor: Partial ... An R companion for applied statistics II: Multivariable and multivariate techniques.
The authors have substantially updated both the car and effects packages for R for this new edition, and include coverage of RStudio and R Markdown.
This work presents content/techniques by progressively building upon earlier introduced ideas on using R as new ideas are introduced. The emphasis is on computational steps and coding strategies associated with methods presented.
Boston: Pearson. Howell, D.C. (1992). ... Kiely, E., & Robertson, L. (2016, November 18). How to spot fake news. FactCheck.org. ... Models and statistical inference: The controversy between Fisher and Neyman-Pearson.
This bundle includes Rebecca M. Warner′s Applied Statistics II 3e (Paperback) and Danney Rasco′s An R Companion for Applied Statistics II (Paperback)
Multivariable and Multivariate Techniques Rebecca M. Warner. satisfy variables (see Pampel, 2000). Thus, scores on L i the assumptions for a linear model, and we can set up a linear equation to predict Li scores from scores for one or ...
At the end of the book, there are several projects that require the use of multiple statistical techniques that could be used as a take-home final exam or final project for a class.
An extensive investigation of Larrea tridentata (creosote bush) distributions in the Mojave and Sonoran deserts, however, found that this convention was probably an oversimplification (Phillips and MacMahon 1981).
Drawing on real world data to showcase different techniques, this practical book helps you use R for data analysis in your own research.
"This book fits right into a needed niche: rigorous enough to give full explanation of the power of the S language, yet accessible enough to assign to social science graduate students without fear of intimidation.