So as I mentioned last time, we might have something called a logistic regression model using logits as the response and how can I explain that … hmmm. Well, I can take the probability of being in a bad dimension, p, and then divided that by a number that’s one minus that probability (or 1 – p) and take the natural logarithm as explained here and voila, that’s our response. See, that wasn’t so bad!  Right?  And what’s the purpose of this? Well, it’s to map a binary variable, or a variable only having two levels, like whether you’re in a good or bad dimension, to a continuous variable.  And how do we know the probability, p, say, that we are in a good dimension?  Well, that depends on the factors we adjust for in the regression of course!  And now, I have a subject to cover next time!


Until then — again, if you need some more summer reading …