Resource Library

This tool provides individuals with opportunities to quiz themselves on levels of measurement in a game-like environment much like "Who Wants to be a Millionaire."

This applet is designed to allow users to explore the relationship between histograms and the most typical summary statistics. The user can choose from several types of histograms (uniform, normal, symmetric, skewed, etc.), or can create their own by manipulating the bars of the histogram. The statistics available for display are mean, median, mode, range, standard deviation, and interquartile range. Also available is a "Practice Guessing" option, in which the values of the statistics are hidden until the user has entered guesses for each value.

A cartoon to teach about comparing parametric versus non-parametric inference. Cartoon by John Landers (www.landers.co.uk) based on an idea from "Lower Bounds on Statistical Humor" by Alan H. Feiveson, Mark Eakin, and Richard Alldredge. Free to use in the classroom and on course web sites.

A cartoon to teach about using boxplots to summarize a distribution. Cartoon by John Landers (www.landers.co.uk) based on an idea from Dennis Pearl (The Ohio State University). Free to use in the classroom and on course web sites.

This NSF funded project provides worksheets and laboratories for introductory statistics. The overview page contains links to 9 worksheets that can be done without technology, which address the topics of obtaining data, summarizing data, probability, regression and correlation, sampling distributions, hypothesis testing and confidence intervals. The page also contains twelve laboratories that require the use of technology. Data sets are provided in Minitab format.

Using cooperative learning methods, this activity helps students develop a better intuitive understanding of what is meant by variability in statistics. Emphasis is placed on the standard deviation as a measure of variability. This lesson also helps students to discover that the standard deviation is a measure of the density of values about the mean of a distribution. As such, students become more aware of how clusters, gaps, and extreme values affect the standard deviation.

Using cooperative learning methods, this lesson introduces distributions for univariate data, emphasizing how distributions help us visualize central tendencies and variability. Students collect real data on head circumference and hand span, then describe the distributions in terms of shape, center, and spread. The lesson moves from informal to more technically appropriate descriptions of distributions.

This applet demonstrates the Binomial distribution by simulating Galton's Board, dropping balls through a triangular array of nails. When a ball hits a nail, it has a 50 percent chance of falling to the left or the right. Because Galton's Board consists of a series of experiments, the piles under the board are the sum of n random variables, where n is the number of rows of nails on the board.

This applet demonstrates the Central Limit Theorem. First, select a distribution (Normal, Uniform, Skewed, Custom) and add or delete data points by clicking on the graph. Then, sample from the parent population and the distribution of the sample mean is shown. Users can also choose to see the distribution of the median, standard deviation, variance, and range.

This applet generates dotplots for different data sets and allows users to guess the location of various measures of center and spread. Clicking "Resample" produces a dotplot of random data generated by the applet. A dotplot of user-input data can be generated by clicking "Edit Data" and typing or copy and pasting the data in the textbox. To guess the mean, median, standard deviation, and interquartile range (IQR) users check the "Guess Mean/Median", "Guess Deviation", or "Guess IQR" box and slide the relevant marker along the horizontal axis. When "Guess Deviation" is selected, users can also select "Show Percentages" to display the percentage of data points within the user's current guess for standard deviation. Clicking "Show Actual" displays the actual position of the selected measure on the dotplot. Clicking on an individual data point shows its value. Users can edit the data under "Edit Data" or by clicking and dragging the data points on the graph.