76  Trend Lines

There are two strategies for fitting a smoothed curve to a data set: parametric or non-parametric fitting. The choice of fitting algorithm not only affects how you analyse your result, but also the reader’s interpretation of them.

Parametric fitting relies on a predefined model, or equation. The fitting algorithm attempts to find the model’s best fit by adjusting the equation’s coefficients. Linear regression is a frequently used form of parametric fitting. 1 In the example below, showing Bradford assay calibration curves, where the OD of samples with known protein concentrations is measured, different equations change the impression of the data.

  • 1 Parametric fitting is popular because the model provides a equation which describes that data set. This can be used to predict values, but because the real function is normally not known, the risk of fitting a line that misrepresents the data exists.

    • 2

  • 2 For details on regression lines see the Statistical Literacy Workshop.