J L Consider the following "training" data describing successes of past startup IPDs. [An IPO is an initial public offering of a firm, but to do this problem you can just think of the 3'00 trials as having successes and failures} There are 100 successful out of the 3"00 IPOs observed {those categories, _s_u_cce_s_s_ and failure, are mutually exclusive}, and for each category we have frequencies of different characteristics (the characteristics are definitely NOT mutually exclusive]. If, for instance, 75f100 successful IPOs hbld a patent, that means that 25,! 100 did NOT hold a patent, ; 50,'100 successful IP05 were NOT Tech firms. :Firm holds patent :founder managed :Tech rm :In California -------------------------------------------- ---------------------------- :successful (100 total) :75 :30 :50 :15 I :unsuccessful {600 total} :300 :300 :40 :40 I Using the Naive Bayes approach (L; assuming that, for instance, P{Firm holds patentlsuccessful, Founder Managed]=P{Firm holds Eatent|successfu and likewise for the other characteristics} estimate the ratio of the probability of an IPO being successful to it not being successful if it i Does not hold a patent, is FounderManaged, is a Tech firm, Not |=n California