This vignette describes related resources and materials useful for teaching statistics with a focus on modeling and computation.
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;
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.
ggformula/lattice conversion examples compares the lattice and ggformula formula interfaces for creating graphics.
The following vignette-like documents are available via github
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 the mosaic package
is gallery of plots made using tools from the mosaic
package.
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 functional ity 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/jarochoeltrocho/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/jarochoeltrocho/MOSAIC-LittleBooks-Spanish.)
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).
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.
Modern Data Science with R (BS Baumer, DT Kaplan, and NJ Horton) is an R-infused data science text that emphasizes conceptual understanding and computation.
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.
Stats: Data and Models (NJ Horton) describes how to undertake analyses in R for the examples in the Fourth Edition of 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
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
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.
GW Cobb, "The introductory statistics course: a Ptolemaic curriculum?", Technology Innovations in Statistics Education, 2007, 1(1), 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, 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, 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.
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