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R Functions for teaching statistics and statistical modeling

Detailed examples of the use of the statisticalModeling package are contained in the package vignettes. This document is directed to instructors to explain the motivation behind statisticalModeling.

This package reflects my evolving thinking about how to teach statistics and the importance of integrating modeling into how students think about statistics. Many of the basic ideas have been expressed in my book Statistical Modeling: A Fresh Approach (2/e, 2011):

  1. make statistics about explaining variation rather than comparing means
  2. place covariation at the center, since almost all modern studies, including those in the news, contain some adjustment for covariates, often signalled by the phrase "after adjusting for ...."
  3. use modern computation to establish a conceptual framework for thinking about modeling, not merely to make easier traditional calculations, such as means, standard deviations, and table lookups.

This package is about (3).

Teaching about statistical modeling often starts with linear regression. I think there is an advantage to introducing other modeling techniques at the same time or even before linear regression. Why?

R provides an infrastructure to support teaching about linear regression. This includes, of course, the lm() function, but also supporting functions for inference and graphics, e.g.

This statisticalModeling package provides an alternative interface that generalizes to many different statistical modeling types, both regression and classification. It includes:

In terms of graphics

Installations from CRAN are done in the usual way. The development version of the package is here on GitHub. To install it, use the following commands in your R system.

# Install devtools if necessary

# Install statisticalModeling