# Activities

# Subject: Statistics

Results 11 - 20 of **34 matches**

A ducks story- introducing the idea of testing (statistical) hypotheses part of Testing Conjectures:Examples

The ideas and vocabulary of testing statistical hypotheses, from research question to conclusion, are introduced using a simple story regarding a population proportion and a small sample using the binomial table to find the p-value.

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)

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.

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.

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.

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.