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Ultra-fine transformation of data for normality.
Mohammad M Hamasha1, Haneen Ali2, Sa'd Hamasha3
1Department of Industrial Engineering, Faculty of Engineering, The Hashemite University, B.O.Box 330127, Zarqa 13313, Jordan.
Heliyon
|May 23, 2022
Summary
A novel Ultra-fine transformation method accurately converts any dataset, regardless of its distribution, into a normal distribution. This simple yet powerful technique enhances statistical analysis by ensuring data normality, even for already normal data.
Area of Science:
- Statistics
- Data Science
Background:
- Normal distribution is essential for many statistical analyses.
- Existing data transformation methods are limited in scope and effectiveness for complex distributions.
- Handling non-normal data often requires specialized or less sensitive statistical methods.
Purpose of the Study:
- To introduce a novel method for transforming any data distribution to a standard normal distribution.
- To provide a universally applicable and user-friendly data transformation technique.
- To improve the reliability of statistical analyses by ensuring data normality.
Main Methods:
- Development of the Ultra-fine transformation method.
- Application of the method to diverse and complex data distributions.
- Validation of the method's accuracy and simplicity.
Main Results:
- The Ultra-fine transformation method successfully transforms any data distribution to a standard normal distribution.
- The method is shown to be accurate, simple to apply, and theoretically sound.
- Original data can be easily recovered from the transformed state.
Conclusions:
- The Ultra-fine transformation method offers a powerful solution for data normality.
- This method is recommended for all statistical analyses, including those with initially normal data, to ensure robustness.
- The technique simplifies data preprocessing for a wide range of statistical applications.
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