- The website for our textbook can be found at http://www.pearsonhighered.com/gould1einfo/.
It contains an introduction to the book, table of contents, a look
inside the book, info about Stat Crunch, and biographies on the
authors. I would suggest using it to get to know the book better.
- R can be downloaded for free from the web. I wrote some directions
for installing R.
Ignore the Emacs directions, as they aren't necessary for you.
- You may want to install the Rcmdr package for R. It is a nice
interface to many of the commands and capabilities of R. I wrote
some directions to install Rcmdr
is a nice tutorial for R.
- Here is a nice short
reference card for R.
- Official R documentation can be found at http://cran.r-project.org/manuals.html.
- Loads of other documentation can be found at http://cran.r-project.org/doc/contrib/
- Histogram Applet. This applet is designed to teach students
how bin widths and the number of bins affects the appearance of
a histogram. By R. Webster West, Univ. of South Carolina.
- Graphs of Distributions can be found here.
This will give you a feel for how the probablility density
function or the probablility mass function differs by changing
- More graphs of Distributions can be found
Central Limit Theorem, Law of Large Numbers, Sampling
Distributions. Try these examples to get a better feel for
sampling distributions and long term probability:
David Lane's Sampling Distribution demo. Start with normal,
uniform, skewed or custom distribution in the
population. Choose sample size of n = 2 to 25, and see how
samples vary. What is the distribution of the sample means?
Central Limit Theorem Demo using n = 1 die, 2, 3, 4, or 5
dice. (n doesn't have to get very big in this case because of
the simple starting distribution.) by R. Todd Ogden, Dept. of
Statistics, Univ. of South Carolina (The applet is at the
bottom of the page)
Histograms compared to a normal density shows the effect of
larger sample size in smoothing the histogram. (from The Shodor
Education Foundation, Inc.)
Sampling Distribution of the Sample Mean, Sample Sum, and
Sample Variance. This applet illustrates the concept of
the sampling distribution of a statistic by simulating the
sampling distribution of four common statistics: the sample
sum, the sample mean, the sample variance, and the
Chi-Squared statistic. (by Philip B, Stark, Univeristy of
- The Central Limit Theorem. This implements an example of
the central limit theorem through rolls of 2 or more dice. (by
Charles Staton, University of Wisconsin-Madison)
Last modified: Wed 08 Jan 2020 01:50:09 PM HST