Non-linear (Univariate) Regression

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Non-linear univariate regression fits a specified function in X to X-Y data by the method of least squares.


To properly analyze and interpret results of non-linear (univariate) regression, you should be familiar with the following terms and concepts:

If you are not familiar with these terms and concepts, you are advised to consult with a statistician. Failure to understand and properly apply non-linear univariate regression may result in drawing erroneous conclusions from your data. Additionally, you may want to consult the following references:

  • Draper, N. R. and Smith, H. 1981. Applied Regression Analysis. 2nd ed. New York: John Wiley & Sons.
  • Neter, J., Kutner, M.H., Nachtsheim, C.J., and Wasserman, W. 1996. Applied Linear Regression Models. 3rd ed. Chicago: Irwin.
  • Neter, J., Wasserman, W., and Kutner, M.H. 1990. Applied Linear Statistical Models. 3rd ed. Homewood, IL: Irwin.
  • Scales, L. E. 1985. Introduction to Non-linear Optimization. New York: Springer-Verlag.
  • Sokal, Robert R. and Rohlf, F. James. 1995. Biometry. 3rd. ed. New York: W. H. Freeman and Co.

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