Stem-and-Leaf Plots, Box Plots, and Outliers from Random Data

This example demonstrates how to generate a dataset (via randomness) and then construct both a stem-and-leaf plot and a box plot, highlighting potential outliers to compare data spread and central tendency. Key concepts include the five-number summary (minimum, Q1, median, Q3, maximum), the interquartile range IQR = Q3 − Q1, and the outlier criterion based on 1.5·IQR (commonly used to flag unusually small/large observations). Construction-wise, the workflow is: generate data with Random, sort it with Sort, compute Min, Q1, Median, Q3, and Max, then build the stem-and-leaf representation with StemPlot and the box-and-whisker visualization with BoxPlot. The construction also references Mode and Sum for additional summary/feature display. It uses objects such as the data list (l1) and UI/annotation elements (e.g., button1, text1, and auxiliary labeled objects like a,b,c,… and A1…A29) to manage and render the plots. Suitable for middle/high school statistics, descriptive data analysis lessons, and classroom demonstrations of quartiles and outlier interpretation.