Compare and contrast:

- \(\frac{\mathit{exp}(\beta_0 + \beta_1 x_1 + \beta_2 x_2)}{1 + \mathit{exp}(\beta_0 + \beta_1 x_1 + \beta_2 x_2)} \)
- \(\mathit{logit}^{-1}(\beta_0 + \beta_1 x_1 + \beta_2 x_2)\), where \(\mathit{logit}^{-1}(x)\) is defined as \(\frac{\mathit{exp}(x)}{1 + \mathit{exp}(x)} \)

Variants of version 1 are often found in psychology journals. Rather than separating out reusable defintions, e.g., here of the *logit *function, many psychologists for some reason find it necessary to plug all bits of a formula in together at once to make a monster formula.

Here’s another example; two definitions of correlation:

- $latex \frac{n\sum x_iy_i-\sum x_i\sum y_i}

{\sqrt{n\sum x_i^2-(\sum x_i)^2}~\sqrt{n\sum y_i^2-(\sum y_i)^2}}&s=2$ - \(\mathit{cov}(X,Y) \over \sigma_X \sigma_Y\)

(Okay, I cheated a bit with the second and left out definitions of covariance and *SD*.)

So the question: which do you find easier to understand?