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Boxplot of Time Series

Boxplot of Time Series
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2020-05-07 20:53
SequenceProbability & StatisticsGraphing
This example demonstrates the boxplot analysis of time series data. A boxplot is a statistical chart that shows the distribution of data, including the median, quartiles, and outliers. The core mathematical concepts involved include statistics such as the median, quartiles, and construction of boxplots. Using GeoGebra commands like Flatten, Sequence, PolyLine, and BoxPlot, the distribution characteristics of the data can be visually presented. Suitable for high school and above students to learn data statistics and analysis.

GeoGebra Commands & Parameters

  1. Flatten({l1, l2, l3, l4, l5, l6, l7, l8, l9, l10, l11, l12}) → L
  2. Sequence(Segment[((1.2 * i) + 0.6, 0), ((1.2 * i) + 0.6, -20)], i, 0, 11) → l14
  3. PolyLine(Sequence[((sclx * (i - 1)), (scly * Element[L, i])), i, 1, 12]) → lp1
  4. PolyLine(Sequence[((sclx * (i - 1)), (scly * Element[L, i])), i, 24, 36]) → lp3
  5. PolyLine(Sequence[((sclx * (i - 1)), (scly * Element[L, i])), i, 12, 24]) → lp2
  6. PolyLine(Sequence[((sclx * (i - 1)), (scly * Element[L, i])), i, 36, 48]) → lp4
  7. PolyLine(Sequence[((sclx * (i - 1)), (scly * Element[L, i])), i, 48, 60]) → lp5
  8. PolyLine(Sequence[((sclx * (i - 1)), (scly * Element[L, i])), i, 60, 72]) → lp6
  9. PolyLine(Sequence[((sclx * (i - 1)), (scly * Element[L, i])), i, 72, 84]) → lp7
  10. PolyLine(Sequence[((sclx * (i - 1)), (scly * Element[L, i])), i, 96, 108]) → lp9
  11. PolyLine(Sequence[((sclx * (i - 1)), (scly * Element[L, i])), i, 108, 120]) → lp10
  12. PolyLine(Sequence[((sclx * (i - 1)), (scly * Element[L, i])), i, 120, 132]) → lp11
  13. PolyLine(Sequence[((sclx * (i - 1)), (scly * Element[L, i])), i, 132, 144]) → lp12
  14. Sequence(50 + (50 * i), i, 0, 11) → Lbpy
  15. BoxPlot(Element[Lbpy, 1], 15, (sclbx * l1) - bxoff) → a1
  16. BoxPlot(Element[Lbpy, 2], 15, (sclbx * l2) - bxoff) → a2
  17. BoxPlot(Element[Lbpy, 3], 15, (sclbx * l3) - bxoff) → a3
  18. BoxPlot(Element[Lbpy, 4], 15, (sclbx * l4) - bxoff) → a4
  19. BoxPlot(Element[Lbpy, 5], 15, (sclbx * l5) - bxoff) → a5
  20. BoxPlot(Element[Lbpy, 6], 15, (sclbx * l6) - bxoff) → a6
  21. BoxPlot(Element[Lbpy, 7], 15, (sclbx * l7) - bxoff) → a7
  22. BoxPlot(Element[Lbpy, 8], 15, (sclbx * l8) - bxoff) → a8
  23. BoxPlot(Element[Lbpy, 9], 15, (sclbx * l9) - bxoff) → a9
  24. BoxPlot(Element[Lbpy, 10], 15, (sclbx * l10) - bxoff) → a10
  25. BoxPlot(Element[Lbpy, 11], 15, (sclbx * l11) - bxoff) → a11
  26. BoxPlot(Element[Lbpy, 12], 15, (sclbx * l12) - bxoff) → a12
  27. Vector((0, 0), (x(Corner[1, 2]), 0)) → vx2
  28. Vector((0, 0), (0, y(Corner[1, 4]))) → vy2
  29. Point(vy2) → A
  30. Segment(Qu, Qu + (0, Element[Lbpy, m])) → squ
  31. Segment(Qo, Qo + (0, Element[Lbpy, m])) → sqo
  32. Q1(Element[LNR, m]) → qu
  33. Q3(Element[LNR, m]) → qo
  34. Segment(Md1, Md1 + (0, Element[Lbpy, 1] - 15)) → sm1
  35. Segment(Md2, Md2 + (0, Element[Lbpy, 2] - 15)) → sm2
  36. Segment(Md3, Md3 + (0, Element[Lbpy, 3] - 15)) → sm3
  37. Segment(Md4, Md4 + (0, Element[Lbpy, 4] - 15)) → sm4
  38. Segment(Md5, Md5 + (0, Element[Lbpy, 5] - 15)) → sm5
  39. Segment(Md6, Md6 + (0, Element[Lbpy, 6] - 15)) → sm6
  40. Segment(Md7, Md7 + (0, Element[Lbpy, 7] - 15)) → sm7
  41. Segment(Md8, Md8 + (0, Element[Lbpy, 8] - 15)) → sm8
  42. Segment(Md9, Md9 + (0, Element[Lbpy, 9] - 15)) → sm9
  43. Segment(Md10, Md10 + (0, Element[Lbpy, 10] - 15)) → sm10
  44. Segment(Md11, Md11 + (0, Element[Lbpy, 11] - 15)) → sm11
  45. Segment(Md12, Md12 + (0, Element[Lbpy, 12] - 15)) → sm12
  46. Point(vx2) → C
  47. PolyLine(Sequence[((sclx * (i - 1)), (scly * Element[L, i])), i, 84, 96]) → lp8

AI Drawing Prompt Reference

Draw a boxplot of time series data, consisting of 12 boxplots, each representing the data distribution of a time period. First, draw a line graph of 12 data points, then draw 12 boxplots, each containing the median, quartiles, and outliers. Finally, draw the x-axis and y-axis.
The prompt above was compiled from the original drawing commands and is for reference only.