# Activities

# Subject: Statistics

# Pedagogy

- Campus-Based Learning 1 match
- Cooperative Learning 7 matches
- Gallery Walk 1 match
- Games 1 match
- Interactive Lectures 6 matches
- Lecture 6 matches
- Making and Testing Conjectures 6 matches
- Quantitative Reasoning 6 matches
- Quantitative Skills 6 matches
- Simulation of Data 7 matches
- Teaching with Data 7 matches
- Teaching with Models 5 matches

Results 11 - 20 of **34 matches**

Using Your Hair to Understand Descriptive Statistics part of Testing Conjectures:Examples

The purpose of this activity is to enhance students’ understanding of various descriptive measures in statistics. In particular, students will gain a visual understanding of means, medians, quartiles, and boxplots without doing any computations by completing this activity.

An In-Class Experiment to Estimate Binomial Probabilities part of Testing Conjectures:Examples

This hands-on activity asks students to conduct a binomial experiment and calculate a confidence interval for the true probabiity. It is useful for involving students, and for having a discussion about the interpretation of confidence intervals and the role of sample size in estimation.

Seeing and Describing the Predictable Pattern: The Central Limit Theorem part of Testing Conjectures:Examples

This activity helps students develop a better understanding and stronger reasoning skills about the Central Limit Theorem and normal distributions. Key words: Sample, Normal Distribution, Model, Distribution, Variability, Central Limit Theorem (CLT)

The Evolution of Pearsonâ€™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 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.

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.

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.).