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Computing and graphing probability values of pearson distributions: a SAS/IML macro.

Qing Yang1, Xinming An2, Wei Pan1

  • 11Duke University, Durham, 27710 USA.

Source Code for Biology and Medicine
|January 1, 2020
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Summary

This study introduces a SAS macro program for analyzing Pearson distributions. The tool accurately identifies distribution types and calculates probabilities, simplifying statistical analysis for researchers.

Keywords:
Curve fittingDistribution-free statisticsHypothesis testingPearson distributions

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Area of Science:

  • Statistics
  • Computational Statistics

Background:

  • Empirical data can be approximated using Pearson distributions based on the first four moments.
  • Existing methods for calculating Pearson distribution percentage points are cumbersome for statistical analysis.

Purpose of the Study:

  • To develop a SAS/IML macro program for identifying Pearson distribution types and computing probability values.
  • To provide a tool that simplifies statistical analysis for data with unknown distributions.

Main Methods:

  • Developed a SAS/IML macro program.
  • The program identifies Pearson distribution types from datasets or moment values.
  • Computes and graphs probability values for any given percentage points.

Main Results:

  • The SAS macro program accurately approximates Pearson distributions.
  • The tool efficiently identifies the correct Pearson distribution type.
  • It computes probability values for any specified percentage points.

Conclusions:

  • The SAS macro program facilitates accurate statistical analysis of data with unknown distributions.
  • Researchers can efficiently conduct analyses using this new computational tool.