Optimality of Greedy algorithm for minimization Knapsack Problem
I didn't understand the question very well, but I think you're asking about the ratio of the greedy solution to the optimal solution when the problem instances are taken out of a certain probability distribution. I don't have a theoretic solution to this problem, but if you need the answer for practical reasons, then I think a Monte-Carlo simulation would give reliable results.  You can approximate knapsack to the desired precision using the well-known dynamic programming algorithm to obtain an approximate value of the optimal solution and then compare it to the greedy algorithm. You generate a number of random instances, and see where the ratio usually falls.
In order to have more reliable estimates, you can establish confidence intervals for the fraction of instances whose greedy/optimal ratio falls within a given interval, etc.