I'm working with a dataset of combined continuous and categorical data, and I'm constructing a regression model based on both types of data simultaneously, using [dummy variables](http://www.mathworks.com/help/toolbox/stats/dummyvar.html). The model will look something like this:

$y = \alpha + \beta\_1 x\_1 + \beta\_2 x\_2$

where, for example, $x\_1 \in [0,1]$ is continuous, and $x\_2 \in \textrm{{a, b, c}}$ is nominal. Because of the dummy variables, I will obtain different significance values for each of the three nominal categories. My question is, **is there a simple, commonly used method for collapsing the three significances into a single significance value, taking into account both the p-value and coefficient value?** I say both p-value and coefficient value because if one term has a very high significance but a very $\beta$, I imagine that should be factored in.

From some discussion with others in my group, we determined that this method (if extant) would have to be some non-linear transformation of the $\beta$ and p-value. The simplest method we came up with was simply select the minimum p-values with an optional weighting term to account for $\beta$. Are there any accepted ways of accomplishing this?