Statistical analysis of linear and nonlinear correlation of the Arrhenius equation constants

Neima Brauner, Mordechai Shacham

Research output: Contribution to journalArticlepeer-review

47 Scopus citations


Engineers must often use correlations that were developed before statistical analysis and verification of the correlation became a routine procedure. In this paper, we use modern statistical techniques to compare the traditional linear regression technique with the modern nonlinear regression as applied to the Arrhenius equation. The objective of the comparison is to determine whether there are basic flaws with the technique used in the past and whether these flaws may render the constants published in the literature untrustworthy. It is concluded that linear regression, when applied to the Arrhenius expression, is in principle not inferior to nonlinear regression and if the relative error in the data is distributed normally, it can even be superior. Nevertheless, if insufficient data were used for calculation of the constants and/or the experimental data were interpolated or smoothed, the accuracy of the published correlation is unpredictable.

Original languageEnglish
Pages (from-to)243-249
Number of pages7
JournalChemical Engineering and Processing: Process Intensification
Issue number3
StatePublished - 1 Jan 1997


  • Arrhenius
  • Linear
  • Nonlinear
  • Regression
  • Statistical analysis

ASJC Scopus subject areas

  • Chemistry (all)
  • Chemical Engineering (all)
  • Energy Engineering and Power Technology
  • Industrial and Manufacturing Engineering


Dive into the research topics of 'Statistical analysis of linear and nonlinear correlation of the Arrhenius equation constants'. Together they form a unique fingerprint.

Cite this