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Sample Visualization and Impact of Mean/Std Deviation

Sample Visualization and Impact of Mean/Std Deviation
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2019-05-11 03:00
Probability & StatisticsGraphing
This example demonstrates how to visualize a sample from a given dataset and observe the impact of changing the overall mean and standard deviation on the sample distribution. Key math concepts include sample, mean, standard deviation, frequency distribution, and absolute deviation. It utilizes GeoGebra commands like Sequence, mean, Sort, Frequency, Unique as per the construction summary. Suitable for high school statistics education to help students intuitively understand the effect of parameter changes on data distribution.

GeoGebra Commands & Parameters

  1. Sequence(round(InverseNormal[mean1, standarddeviation1, random()]), n, 0, 99) → Numbers1
  2. Sequence(mean1 + (n * standarddeviation1 / 2), n, -10, 10, 1) → Boundaries1
  3. Sequence(abs(Numbers1(n) - mean1), n, 1, Length[Numbers1]) → AbsoluteDeviations1
  4. mean(AbsoluteDeviations1) → mad1
  5. Sequence(Numbers1(SampleIndices(n)), n, 1, samplesize) → Sample
  6. Sort(Numbers1) → Sorted
  7. Frequency(Sorted) → FrequencyNumbers
  8. Unique(Sorted) → List
  9. Sequence(Sequence[(List(n), m), m, 1, FrequencyNumbers(n)], n, 1, Length[List]) → Points
  10. Sort(Sample) → SortedSample
  11. Unique(Sample) → UniqueSample
  12. Frequency(Sample) → FrequencySample
  13. Sequence(Sequence[(UniqueSample(n), m), m, 1, FrequencySample(n)], n, 1, Length[UniqueSample]) → SamplePoints