In an experiment, the probabilities of the events E, F and G are known to be 0.500,0.150, and 0.220 respectively.
Suppose E and G are independent events. Then P(E∪G)=_____
If E and G are independent events, P(E ∩ G) = P(E) P(G)
P(E U G) = P(E)+P(G) - P(E ∩ G)
P(E U G) = P(E)+P(G) - P(E) P(G) = 0.500 +0.220 - (0.500)(0.220)
= .5+.22-.11 = 0.61
If E and G are independent, then the occurrence of E doesn't affect the probability of G and vice versa.
That is, P(E|G) = P(E) and P(G|E) = P(G).
P(E∩G) = P(E)P(G|E) (the multiplication rule), so this becomes
P(E∩G) = P(E)P(G)
P(E∩G) = P(E) + P(G) - P(E∪G) (the addition rule), which becomes
P(E)P(G) = P(E) + P(G) - P(E∪G)
This can be arranged for P(E∪G) = P(E) + P(G) - P(E)P(G).
So P(E∪G) = 0.500 + 0.220 - 0.500*0.220 = 0.61
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Verified answer
If E and G are independent events, P(E ∩ G) = P(E) P(G)
P(E U G) = P(E)+P(G) - P(E ∩ G)
P(E U G) = P(E)+P(G) - P(E) P(G) = 0.500 +0.220 - (0.500)(0.220)
= .5+.22-.11 = 0.61
If E and G are independent, then the occurrence of E doesn't affect the probability of G and vice versa.
That is, P(E|G) = P(E) and P(G|E) = P(G).
P(E∩G) = P(E)P(G|E) (the multiplication rule), so this becomes
P(E∩G) = P(E)P(G)
P(E∩G) = P(E) + P(G) - P(E∪G) (the addition rule), which becomes
P(E)P(G) = P(E) + P(G) - P(E∪G)
This can be arranged for P(E∪G) = P(E) + P(G) - P(E)P(G).
So P(E∪G) = 0.500 + 0.220 - 0.500*0.220 = 0.61