This vignette describes related resources and materials useful for teaching statistics with a focus on modeling and computation.
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;
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 ``resampling bake off’’ at USCOTS 2011.
ggformula/lattice conversion examples compares the lattice and ggformula formula interfaces for creating graphics.
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
Graphics with the mosaic package is gallery of plots made using tools from the
Some features of the mosaic package are provided through auxiliary packages. These include:
Install these packages using
Pruim R, Kaplan DT and Horton NJ (2017). The mosaic Package: Helping Students to ‘Think with Data’ Using R. The R Journal, 9(1), pp. 77-102. https://journal.r-project.org/archive/2017/RJ-2017-024/index.html.
Abstract: The mosaic package provides a simplified and systematic introduction to the core functionality related to descriptive statistics, visualization, modeling, and simulation-based inference required in first and second courses in statistics. This introduction to the package describes some of the guiding principles behind the design of the package and provides illustrative examples of several of the most important functions it implements. These can be combined to help students ‘think with data’ using R in their early course work, starting with simple, yet powerful, declarative commands.
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 spanish language translation can be found at https://github.com/fjaraavilaa/MOSAIC-LittleBooks-Spanish.)
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.
(A spanish language translation can be found at https://github.com/fjaraavilaa/MOSAIC-LittleBooks-Spanish.)
GW Cobb, “The introductory statistics course: a Ptolemaic curriculum?”, Technology Innovations in Statistics Education, 2007, 1(1), escholarship.org/uc/item/6hb3k0nz.
Fieberg JR, Vitense K, Johnson DH. 2020. Resampling-based methods for biologists. PeerJ 8:e9089 https://doi.org/10.7717/peerj.9089
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, nhorton.people.amherst.edu/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, https://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.