The central limit theorem is important in statistics because it allows us to use a Normal distribution to find probabilities involving the sample mean if the population size is reasonably large (whether the population distribution is known or not).a. population is Normally distributed (for any sample size). b. sample size is reasonably large (for any population)c. population is Normally distributed and the sample size is reasonably large. d. population is Normally distributed and the population standard deviation is known (for any sample size).

1 3 5

step-by-step explanation:

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the correct answer is 1: 2

step-by-step explanation:

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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