Let's say that I have a dependent variable, the probability of winning on ebay, and I want to model that on various variables. Let's say I have data on each individual ebay item I am bidding on and whether I won or not. Is there a way to model the probability of winning. Or would it better to think of the dep var as a dichotomous variable which is either won or did not win, and then construct a logit. Basically, I'm wondering if and what strategies to pursue in order to find the probability of winning, while accounting for various independent variables.
The logit plan sounds more logical, but I'm wondering if there's something I could use as an alternative for modeling the probability of an outcome.
Basically, I'm wondering about finding: When I bid $5, what's the probability of winning When I bid $6, what's the probability of winning and so forth
Let's say I have data on various products that I won and those that I lose, along with various other variables. Why wouldn't it make sense to predict on whether it was win/loss. I guess 0/1 is still not probability, but hmm... I have the data, I'm just not sure if or how to model the probability of winning.
I'm really interested in a single bid price and whether I won/lost.
So predict win/loss on bid price, use a logit model, then estimate the probability of winning (Which I'm not sure about)