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Teacher guide — Grade 9, Unit 6: Charts that work

Time: 3 lessons + project (about 5 periods). Track: B (code: matplotlib).

# Lesson Time
1 Histograms and bar charts 40–45 min
2 Box plots and outliers 40–45 min
3 Scatter plots 40–45 min
P Unit project: One chart, one claim 2 periods

Before you start

  • Materials: Notebook notebooks/charts.ipynb.
  • Read docs/pedagogy/lesson-anatomy.md and docs/pedagogy/ai-use-policy.md.

If you have no computers / no internet

Draw the same charts on graph paper from a 15-row extract (Grade 8 Unit 4 skills).

Common misconceptions

  • Bar chart ≠ histogram: bars for categories, bins for numerical ranges.
  • Correlation (scatter pattern) is not proof of cause.

Differentiation

  • Support: give a partially filled table / sentence starters; pair with a data-steward role.
  • Stretch: ask "what would change your conclusion?" and "who is missing?"

Answer key (check questions)

Lesson 1: Histograms and bar charts 1. Bars or histogram for 'belt'?
Answer: Bar chart (categorical). 2. What is a bin?
Answer: A range of values counted together. 3. Why try different bin counts?
Answer: Shapes can look different; avoid being fooled by one choice.

Lesson 2: Box plots and outliers 1. What does the line in the box show?
Answer: The median. 2. What is an outlier?
Answer: A value far from the rest. 3. Should you delete outliers automatically?
Answer: No: investigate first; they may be errors or important facts.

Lesson 3: Scatter plots 1. What does each dot represent?
Answer: One district. 2. If points slope upward, what do we call it?
Answer: A positive relationship. 3. Does it prove one causes the other?
Answer: No: other factors may drive both.

Assessment

Starter quiz each lesson (3 questions, one recalling the previous lesson); unit quiz in quiz.yml; project marked with the rubric in docs/pedagogy/assessment.md; 2-minute oral check per group.

Alignment

Optional Computer Science (Grades 9–10): databases/SQL and introductory programming; compulsory ICT/Maths data handling.

Answers: practice exercises

  1. (a) bar chart (b) histogram (or box plot) (c) scatter plot (d) side-by-side box plots (or two histograms on one scale).
  2. Halves: 2, 4, 4, 5, 7 and 8, 9, 10, 12, 30 → Q1 = 4, median = (7 + 8) ÷ 2 = 7.5, Q3 = 10, IQR = 6; upper fence 10 + 9 = 19 → 30 is an outlier. (Software uses slightly different quartile rules; small differences are fine.)
  3. Both set the x-axis label. plt.xlabel works on the current single chart; ax.set_xlabel on one chosen panel when using plt.subplots.
  4. Right-skewed: most districts 1,000–2,500 km², a long tail up to about 5,500 km² (Mountain districts are large).
  5. top = df.sort_values("population").tail(10); plt.barh(top["district"], top["population"]) (sorting ascending then barh puts the largest at the top).
  6. Medians: Hill 4.62, Mountain 4.64, Terai 4.59 — very similar; no outliers by the 1.5 × IQR rule.
  7. r ≈ −0.34: a moderate negative relationship — bigger (mountain) districts tend to have fewer people. Area does not mean more people.
  8. r ≈ 0.03: no relationship. Plausible: travel time hardly changes sleep in this (synthetic) class; real data might differ if long journeys mean early waking.
  9. Richer districts have more phones and more schools (income as third variable); urban districts have both; literate people may buy more phones (reverse direction).
  10. The cut axis makes small differences in counts look large (Grade 8 Unit 4).

Lesson 3 activity: abroad_per_1000 vs literacy: r ≈ 0.37 overall (moderate positive). But within belts it is weak or negative (Hill 0.12, Mountain −0.39, Terai −0.04): the overall pattern comes mostly from differences between belts (Mountain districts have both low literacy and low emigration). This is a lovely, simple example of a confounder (belt) — make sure students see it in step 3.

Real-data exercises: notes

  • R1 Strong positive but curved relationship; on a log x-axis it is roughly a straight line, because each doubling of income goes with a similar gain in life expectancy. Nepal sits in the lower-middle income group with a life expectancy higher than many countries of similar income.
  • R2 Histogram right-skewed; 253 of 304 events at exactly 10 km — USGS uses a fixed default depth when depth is poorly constrained.
  • R3 Check legend, source note, units ("% of children dying before age 5").