Hi everyone, I'm trying to understand some R output here for ordinal regression. I have some integer data called "A" split up into 3 ordinal categories, top, middle and bottom, T, M and B respectively.

I have to explain this output to people who have a very poor idea about statistics and just need to make sure I know what I'm talking about first.

Here's the output:

Call:
polr(formula = Factor ~ A, data = a, Hess = TRUE, method = "logistic")

Coefficients:
Value Std. Error t value
A -0.1259028 0.04758539 -2.645829

Intercepts:
Value Std. Error t value
B|M -2.5872 0.5596 -4.6232
M|T 0.3044 0.4864 0.6258

Residual Deviance: 204.8798
AIC: 210.8798

I really am not sure what hte intercepts mean at all. However, my understanding of the coefficient of A is that as the category increases, A decreases? If I have an A value of 10, how to I figure out the estimated probability that this score is in one of the three categories?

thanks!