This article describes a bivariate data set that is interesting to students. Indeed, this particular data set, which involves twins and IQ, has sparked more student interest than any other set that I have presented. Specific uses of the data set are presented.

Regression analysis is an important aspect of most introductory statistics courses in sociology but is often presented in contexts divorced from the central concerns that bring students into the discipline. Consequently, we present five lesson ideas that emerge from a regression analysis of income inequality and mortality in the USA and Canada.

This article presents activities appropriate for the first half of a general introductory statistics course. All activities revolve around the same data set collected early in the course. The activities require students to make decisions about how they should proceed. (Contains 2 tables and 5 figures.)

The correlation coefficient is commonly used to indicate the quality of fit in regression. This practice is questionable.

This article describes a simulation activity that can be used to help students see that the estimator "S" is a biased estimator of [sigma]. The activity can be implemented using either a statistical package such as R, Minitab, or a Web applet. In the activity, the students investigate and compare the bias of "S" when sampling from different…

Very little explanatory power is required in order for regressions to exhibit statistical significance. This article discusses some of the causes and implications. (Contains 2 tables.)

A simple way is given to create residual plots that are words or pictures. Three illustrative examples are presented.

We explain how to simulate both univariate and bivariate raw data sets having specified values for common summary statistics. The first example illustrates how to "construct" a data set having prescribed values for the mean and the standard deviation--for a one-sample t test with a specified outcome. The second shows how to create a bivariate data…

In spite of the name, simple linear regression presents a number of conceptual difficulties, particularly for introductory students. This article describes a simulation tool that provides a hands-on method for illuminating the relationship between parameters and sample statistics.

This article illustrates a method for fitting straight lines to data that is resistant to outliers and might therefore sometimes be preferred to the customary least squares procedure.