# Activities

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The Evolution of PearsonĂ¢s Correlation Coefficient/Exploring Relationships between Two Quantitative Variables part of Interactive Lectures:Examples

The evolution of ideas is often ignored in the teaching of statistics. It is important to show students how definitions and formulas evolve. This activity describes a fairly straightforward activity of how measures of association can evolve.

Using an Applet to Demonstrate Sampling Distributions of Regression Coefficients part of Interactive Lectures:Examples

This applet simulates a linear regression plot and the corresponding intercept and slope histograms. The program allows the user to change settings such as slope, standard deviation, sample size, and more.

Using an Applet to Demonstrate a Sampling Distribution part of Interactive Lectures:Examples

Introducing sampling distribution through cooperative learning among students using a group activity. Afterwards, use the sampling distribution applet to illustrate.

Psychic test part of Interactive Lectures:Examples

Show relative frequency converging to true probability by testing the psychic ability of your students.

Count the Fs: Why a Sample instead of a Census? part of Interactive Lectures:Examples

This interactive lecture activity motivates the need for sampling. "Why sample, why not just take a census?" Under time pressure, students count the number of times the letter F appears in a paragraph. The activity demonstrates that a census, even when it is easy to take, may not give accurate information. Under the time pressure measurement errors are more frequently made in the census rather than in a small sample.

Using an Applet to Demonstrate the Sampling Distribution of an F-statistic part of Interactive Lectures:Examples

This visualization activity combines student data collection with the use of an applet to enhance the understanding of the distributions of mean square treatment (MST), mean square error (MSE) as well as their ratio, an F-distribution. Students will see theoretical distributions of the mean square treatment, mean square error and their ratio and how they compare to the histograms generated by the simulated data.