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Suppose that the number of sales per hour at Fry's Electronics can be modeled as a Poisson process (each hour-long time interval is independent of each other non-overlapping time interval). Here is some sample data of the sales. hour 12 noon 1 PM 2 PM | 3 PM 4 PM sales 2 3 6 (a) Compute the likelihood of observing the sample data if the sales rate parameter was A = 4 (b) Compute the maximum likelihood estimate (MLE) based on the sample data. This is a simple calculation (no proof needed). (c) Use the MLE and Chebyshev's Inequality to get a lower bound for the number of hours (amount of data) needed so that the sample average X is within 7 percent of the true population rate parameter ) with at least 95 percent probability