Using linear and non-linear regression to fit biochemical data.
For biochemists or chemists the most common form of data analysis is likely to be regression analysis. This is a technique to find the 'best' values for various experimental parameters; defined as those values which, when used in an appropriate equation, result in the minimum deviation of the calculated results from the experimental data. Despite the widespread application of regression analysis, the basis of the technique and the underlying assumptions are often poorly understood or appreciated. This article describes the basics of linear and non-linear regression, the role of 'weighting' and the potential pitfalls of such analyses.
Leatherbarrow
R J
RJ
Department of Chemistry, Imperial College of Science, Technology and Medicine, South Kensington, London, UK.
D001669:Biochemical Phenomena; D001671:Biochemistry; D001330:Electronic Data Processing; D004798:Enzymes / Q000378:metabolism; D007700:Kinetics; D012044:Regression Analysis
D004798:Enzymes