Regression Residual Plot Warm‑up
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This GeoGebra example demonstrates how to construct a residual plot for regression analysis, allowing users to visually assess model fit. It involves key concepts such as linear regression, polynomial regression, residual calculation, and hypergeometric distribution in statistics. Using the ResidualPlot command together with data points (e.g., A1, B1, …) and lines l1, l2, learners can adjust regression parameters and instantly view the residual distribution. It is suitable for high‑school or early‑university courses in statistics, mathematical modeling, and data analysis, serving as an essential tool for regression diagnostics.
GeoGebra Commands & Parameters
ResidualPlot({(0, 0), (1, 1), (3, 9), (4, 16), (5, 25), (6, 36), (7, 49), (8, 64), (9, 81), (10, 100)}, FitPoly[{(0, 0), (1, 1), (3, 9), (4, 16), (5, 25), (6, 36), (7, 49), (8, 64), (9, 81), (10, 100)}, 2]) →
AI Drawing Prompt Reference
In one coordinate plane, create a regression residual plot. Use the given data points whose pattern follows an approximate square relationship: (0,0), (1,1), (3,9), (4,16), (5,25), (6,36), (7,49), (8,64), (9,81), and (10,100). Fit a second-degree (quadratic) polynomial regression to these points, then compute the residual for each point as the observed value minus the fitted value. Visualize the resulting residuals in the residual plot, maintaining the mapping between each data point and its regression fit so the residual distribution can be inspected for how well the model matches the data.
The prompt above was compiled from the original drawing commands and is for reference only.