Graphs in Statistical Analysis¶
Anscombe, F. J. (1973). Graphs in Statistical Analysis. The American Statistician, 27(1), 17-21.
Cited by¶
3 citations across 3 artifacts.
Each citation links to the sentence it supports in the citing article.
Mechanisms¶
- Correlation Heatmap
- Identical correlation values can hide wildly different shapes.
This sourceAnscombe's four datasets demonstrate that identical correlation values and regression summaries can coexist with radically different plotted shapes.
- Identical correlation values can hide wildly different shapes.
- Residual-versus-Fitted Plot
- Its strength is speed and directness: it is the fastest way to catch a misspecified mean function, and a single picture surfaces curvature
This sourceShows that a plot can surface curvature that a lone fit statistic hides.
- Its strength is speed and directness: it is the fastest way to catch a misspecified mean function, and a single picture surfaces curvature
- Support, Shape, and Tail Diagnostic Suite
- The cautionary classic is Anscombe's quartet — four datasets with identical means, variances, and correlations but wildly different shapes — which is exactly why a responsible profile always looks at multiple views rather than trusting the summary numbers.
This sourceConstructs four datasets with matching summaries but sharply different plots to show why graphical views must accompany statistics.
- The cautionary classic is Anscombe's quartet — four datasets with identical means, variances, and correlations but wildly different shapes — which is exactly why a responsible profile always looks at multiple views rather than trusting the summary numbers.
Verification¶
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