Heart Rates Practice: Correlation and Normal Distribution

This GeoGebra example uses heart-rate sample data to illustrate how statistical visualization on the coordinate plane helps interpret patterns in the data and compare them with a normal-distribution model. Key mathematical ideas include correlation (to measure linear association between variables or derived quantities), the normal distribution (to model and visualize the expected shape of variation), and the use of integer-valued measurements (integers) commonly found in health and sensor datasets. Construction focuses on building graphical representations from the data—typically scatter/point-style displays to reveal relationships—then overlaying or referencing a normal distribution curve for visual comparison. The goal is to connect what students see in the graph to statistical reasoning about dependence and distributional assumptions. Suitable for late secondary school through early undergraduate levels, especially for introductory probability & statistics and data analysis practice.