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Fitting curves to data using nonlinear regression: a practical and nonmathematical review
Author(s) -
Motulsky Harvey J.,
Ransnas Lennart A.
Publication year - 1987
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.1.5.3315805
Subject(s) - nonlinear regression , polynomial regression , regression , nonlinear system , regression analysis , local regression , regression diagnostic , multivariate adaptive regression splines , computer science , proper linear model , statistics , linear regression , mathematics , physics , quantum mechanics
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.