Cary Roseth

This page is part of a collection of profiles of people involved in SERC-hosted projects The profiles include an automatically generated list of each individual's involvement in the projects. If you are a community member you may view your page and add a bio and photo by visiting your account page

Materials Contributed through SERC-hosted Projects

Activities (3)

Body Measures: Exploring Distributions and Graphs Using Cooperative Learning part of CAUSE Teaching Methods:Cooperative Learning:Examples
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.

Understanding the standard deviation: What makes it larger or smaller? part of CAUSE Teaching Methods:Cooperative Learning:Examples
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.

Histogram Sorting Using Cooperative Learning part of CAUSE Teaching Methods:Cooperative Learning:Examples
Using cooperative learning methods, this activity provides students with 24 histograms representing distributions with differing shapes and characteristics. By sorting the histograms into piles that seem to go together, and by describing those piles, students develop awareness of the different versions of particular shapes (e.g., different types of skewed distributions, or different types of normal distributions), and that not all histograms are easy to classify. Students also learn that there is a difference between models (normal, uniform) and characteristics (skewness, symmetry, etc.).