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Quantitative Frequency Distribution Construction

Quantitative Frequency Distribution Construction
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2013-09-04 06:15
Probability & StatisticsGeometric Construction
In collections Charts & Graphs
This example demonstrates how to construct quantitative frequency distributions from numerous data samples. It involves core mathematical concepts like data statistics, frequency distribution, histogram, maximum/minimum values, and mean. Key GeoGebra commands and objects used include Sequence, Element, Max, Min, If, KeepIf, Mean, Sort, CountIf, Append, Polygon, Segment, and Sum. Suitable for high school and above students for probability & statistics teaching and data analysis practice.

GeoGebra Commands & Parameters

  1. Sequence(TbTp - k, k, 0, 3) → TbLHSq
  2. Element(TbLHSq, 3) → TbLH2
  3. Element(RtSq, Mod[Seed, 8] + 1) → Rate
  4. Sequence(Mod[Rate * k + Strt, 20] + 1, k, 0, 19, 1) → OpSq
  5. Element(DtSq, Data) → Dt0
  6. Sequence(Element[Dt0, Element[OpSq, k]], k, 1, Size) → SmpSq
  7. Element(TbLHSq, 1) → TbLH0
  8. Element(AcSq, Data) → AcDt
  9. Max(SmpSq) → DtMx
  10. Min(SmpSq) → DtMn
  11. If(C0 ≤ UB, 0, If[C1 ≤ UB, 0, If[C2 ≤ UB, 1, If[C3 ≤ UB, 2, 3]]]) → AcCW
  12. Max(AcDt, AcCW) → AcCn
  13. If(C0 ≤ UB, C0, If[C1 ≤ UB, C1, If[C2 ≤ UB, C2, If[C3 ≤ UB, C3, C4]]]) → ClWd
  14. Sequence(DtMn + k * ClWd, k, 0, NmCl - 1) → LCL
  15. Element(LCL, 2) → LCL2
  16. Sequence(UCL1 + ClWd * k, k, 0, NmCl - 1) → UCL
  17. Sequence(CountIf[Element[LCL, k] ≤ x ≤ Element[UCL, k], SmpSq], k, 1, NmCl) → FrSq
  18. Max(FrSq) → FrMx
  19. If(FrMx > 12, 3, If[FrMx > 6, 2, 1]) → VScl
  20. Element(TbLHSq, 4) → TbLH3
  21. Element(TbLHSq, 2) → TbLH1
  22. Element(LCL, 1) → AxMn
  23. Element(PlcSq, Data) → DP
  24. Sequence(k, k, 1, 19) → StrSq
  25. KeepIf(floor(20 / x) ≠ 20 / x, StrSq) → RlPrSq
  26. Sequence(Element[SmpSq, k], k, 1, RwLn1) → DtRw1
  27. Sequence(Element[SmpSq, k], k, RwLn1 + 1, Size) → DtRw2
  28. Element(DDSq, Data) → DD0
  29. Mean(SmpSq) → Xbar
  30. Sort(SmpSq) → SrtSq
  31. CountIf(x > 0, RtSq) → RtNo
  32. Element(SclSq, Data) → Scale
  33. Sequence(If[k > 1, " + ", " "], k, 1, NmCl) → PlsSq
  34. Sequence(" = ", k, 1, NmCl) → EqSq
  35. Sequence(DtMn + k * ClWd, k, 0, NmCl - 2) → LCLCn1
  36. Append(DtMn, LCLCn1) → LCLCn2
  37. Sequence(ClWd, k, 1, NmCl - 1) → CWSq1
  38. Append(" ", CWSq1) → CWSq2
  39. Sequence(UCL1 + ClWd * k, k, 0, NmCl - 2) → UCLCn1
  40. Append(UCL1, UCLCn1) → UCLCn2
  41. Sequence("to", k, 1, NmCl) → ToSq
  42. Sequence(If[Element[FrSq, j] > 0, Sequence["|", k, 1, Element[FrSq, j]], {" "}], j, 1, NmCl) → TlSq
  43. Polygon(FDUL, FDLL, FDLR, FDUR) → FDBx, fdrg, fdbt, fdlf, fdtp
  44. If(VScl ≟ 3, ceil(FrMx / 3) * 3, If[VScl ≟ 2, ceil(FrMx / 2) * 2, FrMx]) → VMax
  45. Element(LCL, 1) → LCL1
  46. Segment(TbML, TbMR) → tbmd
  47. Segment(TbLL, TbLR) → tbbt
  48. Segment(TbUL, TbUR) → tbtp
  49. Sum(FrSq) → ChSz
  50. CountIf(x ≠ (0, 0), Dt0) → DtCn
  51. Sequence(Text[Element[LCL, k], (17.1, CLTp - 0.9 * k)], k, 1, NmCl) → LCLTx1
  52. Sequence(Text[Element[EqSq, k], (16.1, CLTp - 0.9 * k)], k, 1, NmCl) → EqTx1
  53. Sequence(Text[Element[CWSq1, k], (14.3, CLTp - 0.9 - 0.9 * k)], k, 1, NmCl - 1) → CWTx1
  54. Sequence(Text[Element[PlsSq, k], (13.3, CLTp - 0.9 * k)], k, 1, NmCl) → PlsTx1
  55. Sequence(Text[Element[LCLCn2, k], (11.6, CLTp - 0.9 * k)], k, 1, NmCl) → LCLCnTx
  56. Sequence(Text[Element[UCL, k], (26, CLTp - 0.9 * k)], k, 1, NmCl) → UCLTx1
  57. Sequence(Text[Element[EqSq, k], (25, CLTp - 0.9 * k)], k, 1, NmCl) → EqTx2
  58. Sequence(Text[Element[CWSq1, k], (23.3, CLTp - 0.9 - 0.9 * k)], k, 1, NmCl - 1) → CWTx2
  59. Sequence(Text[Element[PlsSq, k], (22.2, CLTp - 0.9 * k)], k, 1, NmCl) → PlsTx2
  60. Sequence(Text[Element[UCLCn2, k], (20.5, CLTp - 0.9 * k)], k, 1, NmCl) → UCLCnTx
  61. Sequence(Segment[(TbLf + CW * k, TbLH0), (TbLf + CW * k, TbLH2)], k, 0, RwLn2) → TbVLn
  62. If(Mod[Size, 2] ≟ 1, Segment[TbMR, TbUR], Segment[TbLR, TbUR]) → tbrg
  63. Sequence(Text[Element[UCL, k], (15.4, DLHt - 0.9 * k)], k, 1, NmCl) → UCLTx2
  64. Sequence(Text[Element[ToSq, k], (14.3, DLHt - 0.9 * k)], k, 1, NmCl) → ToTx
  65. Sequence(Text[Element[LCL, k], (12.2, DLHt - 0.9 * k)], k, 1, NmCl) → LCLTx2
  66. Sequence(Text[Element[FrSq, k], (24.3, DLHt - 0.9 * k)], k, 1, NmCl) → FrTx
  67. Sequence(If[Element[FrSq, j] > 0, Sequence[Text["|", (17.8 + k * 0.3, DLHt - 0.9 * j)], k, 1, Element[FrSq, j]], Text[" ", (17.8, DLHt - 0.9 * j)]], j, 1, NmCl) → TlTxSq
  68. Sequence(Text[Element[DtRw1, k], (TbLf - 1.8 + k * CW, TbLH1 + 0.15)], k, 1, RwLn1) → TxRw1
  69. Sequence(Text[Element[DtRw2, k], (TbLf - 1.8 + k * CW, TbLH2 + 0.15)], k, 1, RwLn2) → TxRw2
  70. If(AcCn ≠ 1, "s", "") → PlCn
  71. If(AcDt ≠ 1, "s", "") → PlDt
  72. If(AcCW ≠ 1, "s", "") → PlCW

AI Drawing Prompt Reference

Create a statistical chart demonstrating the construction of quantitative frequency distributions from multiple data samples. Start by generating a histogram based on the data, marking the maximum and minimum values, and calculating the mean. Then, divide the data into intervals, count the frequency in each, and represent it with bars. Finally, combine all elements into a complete statistical chart, including data points, interval divisions, frequency counts, and the histogram.
The prompt above was compiled from the original drawing commands and is for reference only.