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5 courses
94 practices
6 hours
Learn to interpret data distributions using shape, center, and spread. You will calculate mean, median, IQR, and standard deviation, identify outliers, and choose the best summaries for skewed data. Finish by writing clear, professional comparisons for any audience.
You will learn to look at a dot plot or histogram and describe what you see in clear, organized words. By the end, you will name a distribution's shape, eyeball its center and spread, flag unusual features, and recognize when "just looking" is no longer enough.
You will learn to compute and interpret the three core measures of center—mode, median, and mean—on small, hand-workable datasets. By the end, you will see firsthand how outliers pull the mean but barely move the median, and you will pick the most informative center for a given dataset.
You will measure how spread out a dataset is using two practical tools: the range and the interquartile range (IQR). Along the way, you will assemble five-number summaries, read box plots, flag outliers with the 1.5×IQR rule, and decide which spread measure tells the more honest story.
You will build standard deviation from the ground up: first as the idea of a "typical distance from the mean," then step by step as a formula you can compute by hand on small datasets. By the end, you will interpret what larger or smaller standard deviations say about real distributions like prices, heights, or commute times.
You will pull together everything from this path to describe and compare distributions clearly. By the end, you will pair center and spread measures appropriately, recognize when single summaries mislead, summarize distributions completely, and write side-by-side comparisons that a non-expert reader can follow and act on.
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