# Activities

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Investigating the Modernity of the University Library part of Campus-Based Learning:Examples

Students will investigate the modernity of the university library by designing and implementing a complex survey design.

Statistics and Error Rates in Death Penalty Cases part of Cooperative Learning:Examples

Nature of the chi-square distribution part of Cooperative Learning:Examples

Explaining the chi-square and F distributions in terms of the behavior of variables constructed by generating random samples of normal variates and summing the sqaures of the values.

How well can hand size predict height? part of Cooperative Learning:Examples

This activity is deigned to introduce the concepts of bivariate relationships. It is one of the hands-on activities of the ‘real-time online hands-on activities’. Students collect their own data, enter and retrieve the data in real time. Data are stored in the web database and are shared on the net.

Body Measures: Exploring Distributions and Graphs Using Cooperative Learning part of Cooperative Learning:Examples

This lesson is intended as an early lesson in an introductory statistics course. The lesson introduces distributions, and the idea that distributions help us understand central tendencies and variability. Cooperative learning methods, real data, and structured interaction emphasize an active approach to teaching statistical concepts and thinking.

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

Using cooperative learning methods, this activity helps students develop a better intuitive understanding of what is meant by variability in statistics.

Histogram Sorting Using Cooperative Learning part of Cooperative Learning:Examples

Intended as an early lesson in an introductory statistics course, this lesson uses cooperative learning methods to introduce distributions. 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 there is a difference between models (normal, uniform) and characteristics (skewness, symmetry, etc.).