• #### Textbook Website

1. 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 Resources

1. 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.
2. 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
3. SimpleR is a nice tutorial for R.
4. Here is a nice short reference card for R.
5. Official R documentation can be found at http://cran.r-project.org/manuals.html.
6. Loads of other documentation can be found at http://cran.r-project.org/doc/contrib/

• #### Histograms

1. 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.

• #### Probabilitiy Distributions

1. 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 the parameters.
2. More graphs of Distributions can be found here.

• #### Central Limit Theorem, Law of Large Numbers, Sampling Distributions.

Try these examples to get a better feel for sampling distributions and long term probability:
1. 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?
2. 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)
3. Histograms compared to a normal density shows the effect of larger sample size in smoothing the histogram. (from The Shodor Education Foundation, Inc.)
4. 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 California, Berkeley)
5. 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)

S. Hyde