Why the odd way of doing Scatchard (or Lineweaver-Burk) plots shown in the Prism manual? Why not use linear regression?

Scatchard, Lineweaver-Burk, and similar transformations are fine for visualization, but we discourage linearization of data followed by linear regression for the following reasons:

• Linear transformation distorts the experimental error. Linear regression assumes that the scatter of points around the line follows a Gaussian distribution, and that the standard deviation is the same at every value of X. This is usually not true with the transformed data.
• Some transformations alter the relationship between X and Y. For example, in a Scatchard plot the value of X (bound) is used to calculate Y (bound/free). This violates the assumptions of linear regression.

We show how to produce Scatchard and Lineweaver-Burk plots based upon the more accurate values obtained using nonlinear regression in two new step-be-step articles else where on this site. These methods preserve the visual benefit and avoid the mathematical difficulties mentioned above.