This vignette describes related resources and materials useful for teaching statistics with a focus on modeling and computation.
Package Vignettes
The mosaic package includes a number of vignettes. These are available from within R or from cran.r-project.org/package=mosaic.
Minimal R describes a minimal set of R commands for use in Introductory Statistics and discusses why it is important to keep the set of commands small;
Less Volume, More Creativity, based on slides from an ICOTS 2014 workshop, introduces the mosaic package and related tools and describes some of the philosophy behind the design choices made in the mosaic package.
Graphics with with the mosaic package is gallery of plots made using tools from the mosaic package.
Resampling methods in R demonstrates how to use the mosaic package to compute p-values for randomization tests and bootstrap confidence intervals in a number of common situations. The examples are based on the ``resamping bake off’’ at USCOTS 2013.
Project MOSAIC Little Books
The following longer documents are available at github.com/ProjectMOSAIC/LittleBooks.
Start Teaching Statistics Using R includes some strategies for teaching beginners, and introduction to the mosaic package, and some additional things that instructors should know about using R.
A Student’s Guide to R provides a brief introduction to the R commands needed for all the basic statistical procedures in an Intro Stats course.
Start R in Calculus highlights features of R and the mosaic package that can be used to teach calculus with R.
Start Modeling in R (coming soon).
Textbook Related
Statistical Modeling: A Fresh Approach (DT Kaplan, second edition)] is an introduction to statistics embracing a modeling approach and employing resampling methods. The mosaic package is used throughout.
Foundations and Applications of Statistics: An Introduction Using R (R Pruim) is an R-infused probability and mathematical statistics text that emphasizes connections between probability and statistics. The book predates the mosaic package, but much of the code originally in the fastR package has been moved into the mosaic package.
The Statistical Sleuth in R (NJ Horton) describes how to undertake analyses in R for the examples in the Third Edition of the Statistical Sleuth: A Course in Methods of Data Analysis (2013), by Fred Ramsey and Dan Schafer.
Introduction to the Practice of Statistics in R (NJ Horton and BS Baumer) describes how to undertake analyses in R that are introduced as examples in the first chapters of the Sixth Edition of Introduction to the Practice of Statistics (2007), by David Moore, George McCabe, and Bruce Craig.
Statistics: Unlocking the Power of Data (Lock, Lock, Lock, Lock, and Lock) is an introductory statistics textbook that embraces a resampling approach.
An annotated companion to the examples in the book implemented using R can be found at
and the Lock5withR R package provides all the data sets used in the text.
Additional information about the book and the approach used there can be found at
Stats: Data and Models (NJ Horton) describes how to undertake analyses in R for the examples in the Fourth Edition of the Stats: Data and Models (2015), by Dick De Veaux, Paul Velleman, and Dave Bock.
Intro Stats (P Frenett and NJ Horton) describes how to undertake analyses in R for the examples in the Fourth Edition of the Intro Stats (2013), by Dick De Veaux, Paul Velleman, and Dave Bock.
Introduction to Statistical Investigations (Tintle et al) is another introductory statistics textbook that embraces a resampling approach.
An annotated companion to the examples in the book implemented using R can be found at
and the ISIwithR R package provides all the data sets used in the text.
Additional information about the book and the approach used there can be found at
Open Intro Stats
OpenIntro Stats now has versions of their labs designed for use with the mosaic package.
The mosaic labs were adapted by Ben Baumer and Galen Long of Smith College.
Articles
GW Cobb, “The introductory statistics course: a Ptolemaic curriculum?”, Technology Innovations in Statistics Education, 2007, 1(1), www.escholarship.org/uc/item/6hb3k0nz.
NJ Horton, BS Baumer, and H Wickham, “Teaching precursors to data science in introductory and second courses in statistics,” CHANCE, 2015, 28(2):40-50, www.amherst.edu/~nhorton/precursors
NJ Horton, and J Hardin, “Teaching the next generation of statistics students to”Think With Data“: special issue on statistics and the undergraduate curriculum,” TAS, 2015, 69(4):259-265, http://amstat.tandfonline.com/doi/full/10.1080/00031305.2015.1094283
D Nolan and D Temple Lang, “Computing in the statistics curricula”, The American Statistician, 2010, 64(2), www.stat.berkeley.edu/~statcur/Preprints/ComputingCurric3.pdf.