Rafael Franco has prepared this Prism file so others can also fit data to a model of receptor dimers.  The model is equation 2 from Casado et. al. 

The file compares the fit of the dimer model to the fit of a model with two independent sites. WIth the sample data , the dimer model fits a bit worse (has a higher sum-of-squares) than a model with two independent sites. But the dimer model has one less parameter, so that fit has one more degree of freedom. The AIC method for comparing models balances goodness-of-fit with degrees of freedom, and concludes that the dimer model is more likely to be true. 

Note that you should not use the extra sum-of-squares F test to compare these two models. The results of that F test are only valid when one model is a simpler case of the other. The dimer model is not nested within (is not a simple case of) the two-site model, so the F test would be invalid. 

There is more to choosing a model than just letting Prism do calculations based on the sum-of-squares  and degrees of freedom. In this example, the affinity parameter is very close to the smallest concentration of radioligand used in that experiment, which makes the fit somewhat suspect.

If you want to use this model with your own data, it is easy to add the model to your list of user-defined equations. From the nonlinear regression results, click the button to open the parameters dialog. Go to the Fit tab, click the button to edit the equation, and click OK twice. Now the equation will be on your user-defined list. 

Maxwell and Delaney have written a great book on ANOVA.  The title makes it seem like the book is more general, but it really is an advanced text of ANOVA, written in clear accessible language with plenty of examples. They emphasize the perspective of using ANOVA to compare models (rather than divide variation into its components). That perspective makes lots of sense to me, and often matches the scientific question the experiment was designed to answer. Some books leave you thinking that ANOVA is no more than a mathematical stunt. This one really approaches ANOVA as a way of thinking, used to answer experimental questions.