Resource Library

This applet shows a scatterplot of height versus foot length. Users can add or delete points and then guess the regression line by clicking "Your Line" and moving the blue regression line. By clicking "Regression Line" users can see the actual regression line. The applet also shows the correlation and R-square for the data as well as the residuals and squared residuals for the guessed regression line and the actual regression line.

This scatterplot lets users plot a number of demographic variables and see the log transformation of those variables for numerous countries and income groups. Users can also see the information for any year from 1975 to 2004.

Through this activity students will gain an understanding of the effect of the sample size on experiments and simulations. A Microsoft Excel program file and handouts are provided.

This Java Applet of a scatterplot allows you to enter data, move data points, define the range of values, and view graph value information.

This dataset contains a number of variables like birth rate, death rate, life expectancy, and Gross National Product for 97 countries. Suggested activities are geared toward non-mathematicians and include exploratory graphical analyses to answer several central questions. Key words: boxplot, scatterplot, population growth

This article presents data from 1997 Big Ten Conference men's basketball games involving the University of Iowa Hawkeyes. The data can be used to demonstrate bivariate statistical inference techniques such as confidence regions, paired comparisons, and simultaneous confidence intervals. Key Word: Bivariate data; Scatterplot.

This article presents a dataset containing actual monthly data on computer usage in Best Buy stores from August 1996 to July 2000. This dataset can be used to illustrate time-series forecasting, causal forecasting, simple linear regression, unequal error variances, and variable transformation. Key Words: Model-building; Seasonal Variation.

This section of an online textbook discusses the correlation coefficient and illustrated it visually through graphs. It explains calculations as well as how scatter plots can describe data. It covers significance tests for relationships, the Spearman rank correlation and the regression equation. Exercises and answers are included.

This site provides numerous datasets for graphical display topics including linear, exponential, logistic, power rule, periodic, and other bivariate scatterplots, histograms, and other univariate data. Each data set is accompanied with a description, file format options, and a sample graph.

Residual plots and other diagnostics are important to deciding whether or not linear regression is appropriate for a set of data. Many students might believe that if the correlation coefficient is strong enough, these diagnostic checks are not important. The data set included in this activity was created to lure students into a situation that looks on the surface to be appropriate for the use of linear regression but is instead based (loosely) on a quadratic function. Key words: regression, residuals