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Session 14: Charting & Trendlines

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Session 14

Charting & Trendlines

(Last session of the Foundations unit)

Why Plot?

A table of numbers can hide a lot — an outlier, a pattern, data that doesn't behave quite the way you expected.

A plot makes it visible at a glance.

Building the Plot

  1. Scatter plot: weight vs. displacement
  2. Add a linear trendline
  3. Display the equation
  4. Display R²

The Slope Tells You the Relationship

The slope of this line tells you the rate one thing changes relative to the other.

For a launcher, that's the whole idea behind calibration.

R² — What It Tells You

Closer to 1 = tighter fit.

R² — What It Tells You

It's diagnostic information, not a pass/fail grade — same idea as last session's conditional-formatting flags.

If R² Is Low — Don't Just Move On

Ask:

  • Were a couple trials noisy? (measurement problem)
  • Does the data genuinely behave non-linearly here? (real effect)

Different problems, different fixes.

You Could Also Compute This Directly

=SLOPE(y-range, x-range)

You Could Also Compute This Directly

=RSQ(y-range, x-range)

The chart isn't magic — it's the same functions skill from Session 12, just drawn as a picture.

Submit Today

Data table + plot + trendline + R² + a one-sentence diagnostic note on your fit quality.

Watch Out For

  • Treating R² as a grade instead of information
  • Assuming low R² always means "redo the experiment"
  • Confusing the trendline's slope with individual data points

That's the Last New Skill in This Unit

Define, Design, Notebook, Safety, Measurement, Functions, Data Quality, Conversion, Tolerance, and now Charting — all of it was building toward one thing.

Next Up

The Launcher Project begins.

Same measure → log → chart workflow. Real stakes. Your own launcher.

'F' → Fullscreen

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Build

Step 1: Build the Plot

Using your Session 11–13 data, create a scatter plot of weight vs. displacement in Sheets.

Step 2: Add the Trendline

Fit a linear trendline. Display its equation and its R² value.

Step 3: Diagnose Your Fit

If your R² is low, don't just move on — decide whether it looks like a measurement problem (a couple of noisy trials) or a real effect (the data genuinely isn't linear across this range). If Session 13 flagged a row, check whether it's the source of any weakness in your fit.

Step 4: Write Your Diagnostic Note

One sentence: is your fit strong or weak, and if weak, do you think that's from measurement noise or a real effect?

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Checkpoint

Your submitted Sheets file must include all of the following:

  • Raw data table (from Sessions 11–13) — trials, weights, displacements, units
  • Scatter plot of weight vs. displacement
  • Trendline with displayed equation and R² value
  • One-sentence diagnostic note: is your fit strong or weak, and if weak, do you think that's from measurement noise or a real effect?
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Reflection

Answer the following questions before submitting your work.

  1. Is your fit strong or weak? Do you think that's from measurement noise or a real effect, and why?
  2. What would it take to convince you your fit was actually wrong, even if R² looked good?
  3. This is the last new skill in Foundations. Looking back at Define, Design, Notebook, Safety, Measurement, Functions, Data Quality, Conversion, Tolerance, and now Charting — which one do you think you'll rely on most once the Launcher Project starts?
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Submit

Submit your data table, plot, trendline, R², and diagnostic note.

Activity Complete