Experimenting in GeoGebra: Linear/Polynomial Functions and Boxplots

This GeoGebra example demonstrates an experimental approach: generate sample data from linear or polynomial function outputs and use a boxplot to compare distribution features. It highlights how changes in the underlying function (e.g., linear vs. polynomial behavior) affect statistics such as the median, quartiles, interquartile range, and potential outliers. The key math concepts are linear functions, polynomial functions, and descriptive statistics visualization via boxplots. Construction-wise, the typical workflow is: define a function in GeoGebra (and plot its graph), compute function values for selected inputs to form a dataset, then apply the Boxplot tool to visualize that dataset’s five-number summary and distribution spread. The provided source shows no GeoGebra construction commands (“construction summary” is empty), so the example primarily relies on GeoGebra’s built-in Function/Graph creation and statistical Boxplot object generation. Suitable for late middle school through high school, and for lessons or practice focused on function-to-data connections and introductory data analysis.