# 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 21 - 30 of **34 matches**

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 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 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.

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.

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.

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

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.).

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.