Fitting curves to data using nonlinear regression: a practical and nonmathematical review
Abstract
Many types of data are best analyzed by fitting a curve using nonlinear regression, and computer programs that perform these calculations are readily available. Like every scientific technique, however, a nonlinear regression program can produce misleading results when used inappropriately. This article reviews the use of nonlinear regression in a practical and nonmathematical manner to answer the following questions: Why is nonlinear regression superior to linear regression of transformed data? How does nonlinear regression differ from polynomial regression and cubic spline? How do nonlinear regression programs work? What choices must an investigator make before performing nonlinear regression? What do the final results mean? How can two sets of data or two fits to one set of data be compared? What problems can cause the results to be wrong? This review is designed to demystify nonlinear regression so that both its power and its limitations will be appreciated.— M otulsky , H. J.; R ansnas , L. A. Fitting curves to data using nonlinear regression: a practical and nonmathematical review. FASEB J. 1: 365‐374; 1987.
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- Authors
- Motulsky, H. J., & Ransnas, L. A.
- Affiliations
- University of California San Diego (US)
- Department of Pharmacology University of California, San Diego La Jolla California 92093 USA
- ORCID
- Motulsky, Harvey J.
- Venue
- The FASEB Journal
- Publisher
- Wiley
- Published
- 1987-11
- Volume
- 1
- Issue
- 5
- Pages
- 365-374
- ISSN
- 0892-6638 (ISSN-L)
- 1530-6860
- OpenAlex
- W1497432777
- Cited by
- 1,327
- References
- 10
- Citations by year
- 2026: 19 · 2025: 28 · 2024: 31 · 2023: 43 · 2022: 57 · 2021: 47 · 2020: 41 · 2019: 46 · 2018: 28 · 2017: 50 · 2016: 33 · 2015: 56 · 2014: 27 · 2013: 43 · 2012: 30
- FWCI
- 44.11
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The FASEB Journal (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Applied Mathematics › Statistical and numerical algorithms
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Keywords
- nonlinear regression, curve fitting, polynomial regression, cubic splines, model selection, goodness-of-fit comparison, interpretation of regression results
- MeSH terms
- Data Interpretation, Statistical, Microcomputers, Regression Analysis, Software
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