When dice is thrown, number of observations in the sample space = 6 × 6 = 36
S = (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1)
(1, 2), (2, 2), (3, 2), (4, 2), (5, 2), (6, 2)
(1, 3), (2, 3), (3, 3), (4, 3), (5, 3), (6, 3)
(1, 4), (2, 4), (3, 4), (4, 4), (5, 4), (6, 4)
(1, 5), (2, 5), (3, 5), (4, 5), (5, 5), (6, 5)
(1, 6), (2, 6), (3, 6), (4, 6), (5, 6), (6, 6)
∴n(S)= 36
Let,
A represents the event “the sum of numbers on the dice is 4” and,
B represents the event “the two numbers appearing on throwing two dice are different”.
Therefore,
A = {(1, 3), (2, 2), (3, 1)}
⇒n(A)=3
\(P(A)=\frac {n(A)}{n(S)}\)
\(P(A)=\frac {3}{36}\)
Also,
B={(2,1),(3,1),(4,1),(5,1),(6,1),(1,2),(3,2),(4,2),(5,2),(6,2),(1,3),(2,3),(4,3),(5,3),(6,3),(1,4),(2,4),(3,4),(5,4),(6,4),(1,5),(2,5),(3,5),(4,5),(6,5),(1,6),(2,6),(3,6),(4,6),(5,6)}
\(⇒ n(B)=30\)
\(P(B)=\frac {n(B)}{n(S)}\)
\(P(B) =\frac {30}{36}\)
Now,
A∩B = {(1,3),(3,1)}
⇒n(A∩C) = 2
\(P(A∩B) =\frac {n(A∩B)}{n(S)}\)
\(P(A∩B)=\frac {2}{36}\)
Hence, \(P(A|B)=\frac {P(A∩B)}{P(B)}\)
\(P(A|B)=\frac {2/36}{30/36}\)
\(P(A|B)=\frac {2}{30}\)
\(P(A|B)=\frac {1}{15}\)
"
If a random variable X has the following probability distribution values:
X | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
---|---|---|---|---|---|---|---|---|
P(X) | 1/12 | 1/12 | 1/12 | 1/12 | 1/12 | 1/12 | 1/12 | 1/12 |
Then P(X ≥ 6) has the value:
The correct IUPAC name of \([ \text{Pt}(\text{NH}_3)_2\text{Cl}_2 ]^{2+} \) is:
Conditional Probability is defined as the occurrence of any event which determines the probability of happening of the other events. Let us imagine a situation, a company allows two days’ holidays in a week apart from Sunday. If Saturday is considered as a holiday, then what would be the probability of Tuesday being considered a holiday as well? To find this out, we use the term Conditional Probability.
P(S | B) = P(B | B) = 1.
Proof of the same: P(S | B) = P(S ∩ B) ⁄ P(B) = P(B) ⁄ P(B) = 1.
[S ∩ B indicates the outcomes common in S and B equals the outcomes in B].
P(B | A), P(A) >0 or, P(A ∩ B) = P(B).P(A | B), P(B) > 0.
This theorem is named as the Multiplication Theorem of Probability.
Proof of the same: As we all know that P(B | A) = P(B ∩ A) / P(A), P(A) ≠ 0.
We can also say that P(B|A) = P(A ∩ B) ⁄ P(A) (as A ∩ B = B ∩ A).
So, P(A ∩ B) = P(A). P(B | A).
Similarly, P(A ∩ B) = P(B). P(A | B).
The interesting information regarding the Multiplication Theorem is that it can further be extended to more than two events and not just limited to the two events. So, one can also use this theorem to find out the conditional probability in terms of A, B, or C.
Read More: Types of Sets
Sometimes students get confused between Conditional Probability and Joint Probability. It is essential to know the differences between the two.