Next Article in Journal
Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning
Previous Article in Journal
Estimating Cost Savings from Early Cancer Diagnosis
Article Menu

Export Article

Open AccessArticle

An Improved Power Law for Nonlinear Least-Squares Fitting?

BTG Research, 9574 Simon Lebleu Road, Lake Charles, LA 70607, USA
*
Author to whom correspondence should be addressed.
Received: 30 August 2017 / Revised: 13 September 2017 / Accepted: 15 September 2017 / Published: 19 September 2017
View Full-Text   |   Download PDF [700 KB, uploaded 19 September 2017]   |  

Abstract

Models based on a power law are prevalent in many areas of study. When regression analysis is performed on data sets modeled by a power law, the traditional model uses a lead coefficient. However, the proposed model replaces the lead coefficient with a scaling parameter and reduces uncertainties in best-fit parameters for data sets with exponents close to 3. This study extends previous work by testing each model for a range of parameters. Data sets with known values of scaling parameter and exponent were generated by adding normally distributed random errors with controlled mean and standard deviations to underlying power laws. These data sets were then analyzed for both forms of the power law. For the scaling parameter, the proposed model provided smaller errors in 96/180 cases and smaller uncertainties in 88/180 cases. In most remaining cases, the traditional model provided smaller errors or uncertainties. Examination of conditions indicates that the proposed law has potential in select cases, but due to ambiguity in the conditions which favor one model over the other, an approach similar to the one in this study is encouraged for determining which model will offer reduced errors and uncertainties in data sets where additional accuracy is desired. View Full-Text
Keywords: power law; least-squares fitting; nonlinear regression; parameter uncertainty power law; least-squares fitting; nonlinear regression; parameter uncertainty
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Helyer, B.; Courtney, M. An Improved Power Law for Nonlinear Least-Squares Fitting? Data 2017, 2, 31.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Data EISSN 2306-5729 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top