standard error of three dimensions

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This application enables regression and standard error calculations for datasets consisting of three dimensions.
These operations are enabled compression of two dimensions via pythagorean geometric distance



  1. Copy dataset into the input feild as comma seperated (csv) or tab delimited (txt) values.
  2. To avoid errors, ensure tabs are not present in csv and that commas are not present in csv.
  3. Headers will not impact results. They will be interpreted as invalid data and removed from the dataset,
  4. For analysis, your data will be labeled as x,y, and z.
  5. Data must be input according to the scheme below.
    • x,y,z
    • x,y,z
    • x,y,z
  6. Select the se3d button.
  7. The log feild will note any issues that may arise while processing.
  8. The results field provides a number of descriptive a parameters.
  9. Data fields present input and calculated values.
  10. The visualization feature pictures 3d data as input and as converted to distance.
  11. The graphing feature pictures 2d comparisons of z relative to the distance calculated for x (dx) and y (dy) as well as the calculated geometric distance (dg).
  12. The comparison feature enables the evaluation of a secondary 3d dataset in comparison to the primary 3d set and indicates the similarity of the two.
    • Using the methods defined above, copy the secondary dataset into the compare field.
    • Select the se3d2 button to derive a similarity between the two.
    • Any issues found in the secondary data are noted in the log field.
    • To construct models of the secondary set, copy this set to the input field and select the se3d button to start over.
  13. For an example, an input csv file can be downloaded here: se3d_example.csv
  14. To perform only 2d analysis, add a column of constant x or y values to the 2d set.




log results data visual graph compare
values ln(values)
absolute distance
color (c) = ( z(val) - z(min) ) / ( z(max) - z(min) )
c < 0.2 c < 0.4 c < 0.6 c < 0.8 c < 1.0
dg dx dy
color (c) = ( z(val) - z(min) ) / ( z(max) - z(min) )
c < 0.2 c < 0.4 c < 0.6 c < 0.8 c < 1.0