Determine P: A coin is tossed three times, where

A coin tossed three times, i.e., S = (TTT, HTT, THT, TTH, HHT, HTH, THH, HHH)
⇒ n(S)=8
(i) E: heads on third toss E=(TTH, HTH, THH, HHH)
⇒N(E)=4
\(P(E)=\frac {n(E)}{n(S)}\) = \(\frac 48\) =\(\frac 12\)
F: heads on first two toss F=(HHT, HHH)
⇒ n(F)=2
\(P(F)=\frac {n(F)}{n(S)}\) =\(\frac 28\) =\(\frac 14\)
∴E∩F = (HHH)
⇒ n(E∩F) = 1
∴\(P(E∩F)=\frac {n(E∩F)}{n(S)} =\frac 18\)
And, \(P(E|F)=\frac {P(E∩F)}{P(F)} =\frac {1/8}{2/8} =\frac 12\)
(ii )E: at least two heads E=(HHT, HTH, THH, HHH)
⇒n(E)=4
\(P(E)=\frac {n(E)}{n(S)}\) =\(\frac 48\) =\(\frac 12\)
F: at most two heads F=(TTT, HTT, THT, TTH, HHT, HTH, THH)
⇒n(F)=7
\(P(F)=\frac {n(F)}{n(S)}\) =\(\frac 78\)
∴E∩F = (HHT, HTH, THH)
⇒n(E∩F) = 3
\(∴P(E∩F) = \frac {n(E∩F)}{n(S)}\) =\(\frac 38\)
And, \(P(E|F)=\frac {P(E∩F)}{P(F)} =\frac {3/8}{7/8} =\frac 37\)
(iii)E: at most two tails E=(HTT,THT,TTH,HHT,HTH,THH,HHH)
⇒n(E)=7
\(P(E)=\frac {n(E)}{n(S)}\) =\(\frac 78\)
F:at least one tail F = (TTT, HTT, THT, TTH, HHT, HTH, THH)
⇒n(F)=7
\(P(F)=\frac {n(F)}{n(S)}\) =\(\frac 78\)
∴E∩F=(HTT, THT, TTH, HHT, HTH, THH)
⇒n(E∩F)=6
∴\(P(E∩F)=\frac {n(E∩F)}{n(S)}\) =\(\frac 68\)
And, \(P(E∩F)=\frac {n(E∩F)}{n(S)}\)=\(\frac {6/8}{7/8}\) =\(\frac 67\)
What did the hunters decide to do when they realized that the tiger was not dead? (The Tiger King)
Alexia Limited invited applications for issuing 1,00,000 equity shares of ₹ 10 each at premium of ₹ 10 per share.
The amount was payable as follows:
Applications were received for 1,50,000 equity shares and allotment was made to the applicants as follows:
Category A: Applicants for 90,000 shares were allotted 70,000 shares.
Category B: Applicants for 60,000 shares were allotted 30,000 shares.
Excess money received on application was adjusted towards allotment and first and final call.
Shekhar, who had applied for 1200 shares failed to pay the first and final call. Shekhar belonged to category B.
Pass necessary journal entries for the above transactions in the books of Alexia Limited. Open calls in arrears and calls in advance account, wherever necessary.
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.