Typically, the MNL choice model is estimated using a Maximum Likelihood Estimation (MLE) method. In a nutshell, given a transaction data, MLE method tries to find the optimal MNL choice model parameters to maximize the likelihood (or the log-likelihood) that such a transaction data is observed. Assume that you are selling a set of N substitutable products and you have transaction data for I customers. Each customer i=1, ..., I observe an assortment of available products Si and chooses one item from the available assortment or decides to not purchase which we denote as product j=0. Denote customer i's choice as . The choice can be any product in the available assortment or the no-purchase option. Given a transaction data, the likelihood function is given by Create a spreadsheet to find the estimates of 1,...,20 by maximizing the loglikelihood function (1,...,20). Use GRG nonlinear and force the decision variables to be Prof. Anton Braverman between two large numbers say -20 and 20. You do not need to submit a mathematical formulation for this problem. b) Verify that products that have been chosen more frequently in the data tend to have a higher vj value. Usually, vj is referred as