Stem-and-Leaf Box Plots: Outliers and Standard Deviation View

This example demonstrates how to visualize a data set’s distribution by combining a stem-and-leaf plot with a box plot, with emphasis on identifying outliers and relating the spread to standard deviation–type dispersion. The key statistical ideas include the stem-and-leaf representation (organizing data into stems and leaves), the five-number summary via box plots (Q1, median, Q3), outlier detection using the interquartile range (IQR) thresholds, and an interpretation of overall variability (dispersive behavior that connects to standard deviation). Construction-wise, the data are ordered and quartiles Q1/Q2/Q3 are computed, then GeoGebra’s BoxPlot is used to draw the box and whiskers, while StemPlot provides the stem-and-leaf structure. The build also computes and labels lower/upper outlier bounds (LV/UV) and uses text/annotation objects (e.g., text1 and various labeled components) to clearly mark the results on the same workspace. The construction summary indicates the core commands and objects: BoxPlot, StemPlot, Q1, Q3, Median, along with supporting variables/objects such as Q_1, Q_2, Q_3, LV, UV, and data/label holders (A1–A21, l1, a, b, etc.). Suitable for high-school statistics lessons, classroom demonstrations, and exploratory learning when comparing how the same dataset looks under different distribution visualizations.