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9.11: Summary

  • Page ID
    142431

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    Summary

    • Graphs reveal shape, sensitivity, constraint crossings, and comparative behavior — things tables cannot show efficiently.
    • Use XY scatter plots for continuous parameter sweeps. Avoid bar charts, pie charts, and 3D effects.
    • Five requirements: title, x-axis label with units, y-axis label with units, appropriate scale, clean formatting.
    • Graph shape communicates relationship type: straight line (linear), parabola (quadratic), hyperbola (inverse). Each has specific engineering implications about where margins erode fastest.
    • Misleading graphs (truncated axes, cherry-picked data, wrong chart type, missing units) can lead to wrong engineering decisions — the Challenger case demonstrates the most severe consequence.
    • Logarithmic scales are appropriate when data spans multiple orders of magnitude.
    • Multi-curve graphs support design comparison by overlaying alternatives against the same axes and constraint lines.
    • Every graph requires an interpretation paragraph: shape, sensitivity region, constraint status, design implication.
    • Trendlines fit a model to data. Forcing the intercept through zero is only correct when theory requires it. Displayed equations must be reformatted out of scientific notation. Extended trendlines are forecasts — useful but not guarantees.
    • Error bars make measurement uncertainty visible. A trendline that passes through all error bars supports the theoretical model; a point whose bar doesn't overlap the trendline warrants investigation.
    • Pie and column charts are appropriate only for categorical data (proportional shares, ranked counts). They are never correct for continuous parameter sweeps. The chart type must match the story the data is telling.

    This page titled 9.11: Summary was last modified on Thu, 24 Sep 2026 17:36:44 GMT and is shared under a CC BY-NC license and was authored, remixed, and/or curated by .

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