---
title: "Resources Related to the mosaic Package"
author: "Randall Pruim, Daniel T. Kaplan, Nicholas J. Horton"
date: "`r Sys.Date()`"

  
vignette: >
  %\VignetteIndexEntry{Resources Related to the mosaic Package}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---



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](http://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](https://github.com/ProjectMOSAIC/LittleBooks/blob/master/README.md).

 * *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.

    * [www.mosaic-web.org/StatisticalModeling](http://www.mosaic-web.org/StatisticalModeling)

 * *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.

    * [www.ams.org/publications/authors/books/postpub/amstext-13](http://www.ams.org/publications/authors/books/postpub/amstext-13)

 * *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.

    * [www.amherst.edu/~nhorton/sleuth](http://www.amherst.edu/~nhorton/sleuth)

		
 * *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. 
		
    * [www.amherst.edu/~nhorton/ips6e](http://www.amherst.edu/~nhorton/ips6e) 

 * *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 
		
    * [github.com/rpruim/Lock5withR/](https://github.com/rpruim/Lock5withR/blob/master/README.md)
    
    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
		
    * [lock5stat.com](http://lock5stat.com) 
		

 * *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.

    * [www.amherst.edu/~nhorton/sdm4](http://www.amherst.edu/~nhorton/sdm4)

 * *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.

    * [www.amherst.edu/~nhorton/is4](http://www.amherst.edu/~nhorton/is4)


 * *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 
		
    * [github.com/rpruim/ISIwithR/](https://github.com/rpruim/ISIwithR/blob/master/README.md)
    
    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
		
    * [math.hope.edu/isi/](http://math.hope.edu/isi/)
		
  * [Open Intro Stats](https://www.openintro.org/stat/labs.php)
  
    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](http://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](http://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](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](http://www.stat.berkeley.edu/~statcur/Preprints/ComputingCurric3.pdf).

