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## Subject: Statistics

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# Subject: Statistics Show all Subject: Statistics

- Multivariate Quantitative Relationships 2 matches

## Mathematics > Statistics > Data Presentation

11 matches General/OtherResults 1 - 10 of **11 matches**

Reese's Pieces Activity: Sampling from a Population part of Teaching with Data Simulations:Examples

This activity uses simulation to help students understand sampling variability and reason about whether a particular samples result is unusual, given a particular hypothesis. By using first candies, then a web applet, and varying sample size, students learn that larger samples give more stable and better estimates of a population parameter and develop an appreciation for factors affecting sampling variability.

Simulating a P-value for Testing a Correlation with Fathom part of Teaching with Data Simulations:Examples

This activity has students use Fathom to test the correlation between attendance and ballpark capacity of major league baseball teams by taking a sample of actual data and scrambling one of the variables to see how the correlation behaves when the variables are not related. After displaying the distribution of correlations for many simulated samples, students find an approximate p-value based on the number of simulations that exceed the actual correlation.

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.

Reasoning About Center and Spread: How do Students Spend Their Time? part of Testing Conjectures:Examples

This activity helps students develop better understanding and stronger reasoning skills about distributions in terms of center and spread. Key words: center, spread, distribution

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.

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.

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.

Interpreting Graphical Displays of Univariate Distributions part of Gallery Walks:Examples

Students give practical interpretation of graphs based on shape, center, and spread.

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.

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.).