NV Chart (TW/CN/HK patent obtained)


In the field of statistics, whether certain method or model can be adopted depends on the distribution of the sample subject. And many commonly used methods or models today are only applicable when the sample is normal (or Gaussian) distribution, resulting that it is necessary and crucial to understand the distribution of the sample before adopting any model.

NV chart was therefore invented to provide a solution that can not only show whether a sample is normal (or Gaussian) distribution but also can present several samples in one chart for comparison.

Real life application example of Graphician NV Chart:

1stdataset
No. 1 2 3 4 5 6 7 8 9 10
Descending 68 55 48 34 29 29 27 26 26 23
No. 11 12 13 14 15 16 17 18 19 20
Descending 21 22 20 20 19 19 18 17 16 16
No. 21 22 23 24 25 26 27 28 29 30
Descending 15 15 14 14 13 13 10 10 10 8
2nddataset
No. 1 2 3 4 5 6 7 8 9 10
Descending 91 68 55 48 45 41 40 37 36 34
No. 11 12 13 14 15 16 17 18 19 20
Descending 31 30 29 29 27 26 26 26 26 25
No. 21 22 23 24 25 26 27 28 29 30
Descending 24 23 23 23 22 22 21 21 21 20
No. 31 32 33 34 35 36 37 38 39 40
Descending 20 20 19 19 19 19 19 18 18 18
No. 41 42 43 44 45 46 47 48 49 50
Descending 18 17 16 16 16 15 15 15 15 15
No. 51 52 53 54 55 56 57 58 59 60
Descending 14 14 14 13 13 13 13 13 13 13
No. 61 62 63 64 65 66 67 68
Descending 12 10 10 10 10 8 8 7

1stdataset
n16(M,3σ) 68.00 v16(μ,3σ) 63.20
n14(M,2σ) 68.00 v14(μ,2σ) 49.60
n12(M,1σ) 29.00 v12(μ,1σ) 36.10
n10(M,0σ) 19.00 v10(μ,0σ) 22.50
n11(M,3σ) 13.00 v11(μ,3σ) 8.90
n13(M,3σ) 8.00 v13(μ,3σ) -4.60
n15(M,3σ) 8.00 v15(μ,3σ) -18.20
2nddataset
n26(M,3σ) 91.00 v26(μ,3σ) 64.96
n24(M,2σ) 68.00 v24(μ,2σ) 50.88
n22(M,1σ) 31.00 v22(μ,1σ) 36.80
n20(M,0σ) 19.00 v20(μ,0σ) 22.72
n21(M,3σ) 13.00 v21(μ,3σ) 8.64
n23(M,3σ) 8.00 v23(μ,3σ) -5.44
n25(M,3σ) 7.00 v25(μ,3σ) -19.52

Graphician NV chart
  • With the respective normal distribution “ruler” listed aside, Graphician NV chart can clearly show that the distributions of the 1st dataset and the 2nd dataset are similar: both are not normal distribution but with longer right tail, a.k.a positive skew or skewed to the right, while there is an outlier (91) in the 2nd dataset.
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