If a and b are independent events, which equation must be true? a. p(b|a) = p(a) b. p(b|a) = p(b) c. p(b|a) = p(a) × p(b) d. p(b|a) = p(a) + p(b)

P(B|A) = P(B)

P(B|A) is the conditional probability of B knowing that A is happened. But since A and B are independent events, knowing that A already happened doesn't change anything.

Here's an example: suppose that you have to flip a coin, and then toss a die. A is the event "the coin lands on tails" and B is the event "the die lands with the 5 face up".

Now, we know that B has a probability of 1/6, because each of the six faces of the die will appears with the same probability.

P(B|A) means "what's the probability that the die will land with the 5 face up, knowing that the coin landed on tail?"

Well, it's always 1/6. Knowing that the coin landed on tail changes nothing about the die toss, because the two events are independent.

B. P(B|A) = P(B)

Step-by-step explanation:

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mark branliest

step-by-step explanation:

.6 is 1/2 of 12

the answer is actually d. i just took the test

answer: 819.89 miles across america

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