Overall interpretation of multinomial ordinal logistic regression model, and how to improve with more variables bit by bit

I have 3 categories to predict which are ordinal - Dead, Inactive, and Highly Active.

I tried MATLAB's logistic regression with 2 explanatory variables (my aim is to keep building up variables bit by bit to get the strongest model, with the correct variables).

I assumed my response variable was ordinal (3 categories to predict - totally dead, inactive, and highly active) - http://uk.mathworks.com/help/stats/mnrfit.html#btpyj65

y = ordinal(convertednbs.activity,{'Dead','Inactive','Highly Active'});
 [B,dev,stats] = mnrfit([convertednbs.artists_paying_for convertednbs.bookmarks]...
    ,y,'model','ordinal');
pvalues = stats.p;

interpretation = [B(1:2)'; repmat(B(3:end),1,2)];

I get the following for my stats.p (p values for the 2 predictors bookmarks and artists paying for to predict the ordinal "activity" variable):

0
0
1.06355069175051e-13
3.02082432919515e-173

Does this mean that artists paying for and bookmarks is significant, given that the p values (bottom 2) are tiny? The coefficients that come out are (only bottom two rows, the others are coefficients, but don't understand the matlab explanation to be honest):

-3.84122521578429    2.44911025915193
-0.178107681649582    -0.178107681649582
-0.321731337655824    -0.321731337655824

Am I using the MATLAB logistic function the right way? How can I continue my method to one by one build up a model? There doesn't seem to be a way that shows "I am doing better than a 50/50 coin flip for each variable I include in the model, for predicting inactive, dead and active".