Flip a coin 9 times. which of the following estimators is most likely considering your data? p is probability of heads. p = 25%, p = 50%, p = 75% that is, fine the probability of seeing your data under each of these 3 possible values of p. which of the 3 had the highest probability? now try p = # heads / 9 and see how that compares. now let's try a bayasian version. assume the priori distribution on p p( p = 25% ) = 20% p( p = 50% ) = 60% p( p = 75% ) = 20% now what is your answer? note this is basically taking the assumption that you believe the true value of p is three times as likely to be 50% than either 25% or 75% before you even started taking data.

85.560%

step-by-step explanation:

conditional probability is defined as:

p(a|b) = p(a∩b) / p(b)

in other words, the probability that a occurs, given that b has occurred = the probability that both a and b occur / the probability that b occurs.

in this example:

p(turns 64 | turns 44) = p(turns 64 & turns 44) / p(turns 44).

if someone turns 64, they must have already turned 44, so:

p(turns 64 | turns 44) = p(turns 64) / p(turns 44)

on page 42, we see that of the original 100,000, 94944 turn 44, and 81234 turn 64.   therefore:

p(turns 64 | turns 44) = 81234 / 94944

p(turns 64 | turns 44) = 0.85560

answer: you need to organize this better it does not make sense.

step-by-step explanation: ?