If the probability that the random variable \( X \) takes values \( x \) is given by \( P(X = x) = k(x + 1) 3^{-x}, x = 0, 1, 2, \dots \), where \( k \) is a constant, then \( P(X \geq 2) \) is equal to:
For probability distributions, ensure the sum of all probabilities equals 1. Use geometric series formulas to simplify summations efficiently.
\(\frac{20}{27}\)
\(\frac{7}{27}\)
The total probability is:
\[ \sum_{x=0}^\infty P(X = x) = 1. \]
Substitute \( P(X = x) = k(x + 1) 3^{-x} \):
\[ k \sum_{x=0}^\infty (x + 1) 3^{-x} = 1. \]
Let:
\[ S = \sum_{x=0}^\infty (x + 1) 3^{-x}. \]
Split \( S \) into two components:
\[ S = \sum_{x=0}^\infty 3^{-x} + \sum_{x=1}^\infty x \cdot 3^{-x}. \]
1. For the first term:
The sum of a geometric series is:
\[ \sum_{x=0}^\infty 3^{-x} = \frac{1}{1 - \frac{1}{3}} = \frac{3}{2}. \]
2. For the second term:
Using the formula for a weighted geometric series:
\[ \sum_{x=1}^\infty x \cdot 3^{-x} = \frac{\frac{1}{3}}{\left(1 - \frac{1}{3}\right)^2} = \frac{\frac{1}{3}}{\left(\frac{2}{3}\right)^2} = \frac{3}{4}. \]
Thus:
\[ S = \frac{3}{2} + \frac{3}{4} = \frac{9}{4}. \]
Equating to 1:
\[ k \cdot \frac{9}{4} = 1 \implies k = \frac{4}{9}. \]
Finding \( P(X \geq 2) \):
\[ P(X \geq 2) = 1 - P(X = 0) - P(X = 1). \]
\[ P(X = 0) = \frac{4}{9} \cdot (0 + 1) \cdot 3^0 = \frac{4}{9}. \]
\[ P(X = 1) = \frac{4}{9} \cdot (1 + 1) \cdot 3^{-1} = \frac{4}{9} \cdot 2 \cdot \frac{1}{3} = \frac{8}{27}. \]
\[ P(X \geq 2) = 1 - \frac{4}{9} - \frac{8}{27} = \frac{27}{27} - \frac{12}{27} - \frac{8}{27} = \frac{7}{27}. \]
What will be the equilibrium constant of the given reaction carried out in a \(5 \,L\) vessel and having equilibrium amounts of \(A_2\) and \(A\) as \(0.5\) mole and \(2 \times 10^{-6}\) mole respectively?
The reaction : \(A_2 \rightleftharpoons 2A\)
A black body is at a temperature of 2880 K. The energy of radiation emitted by this body with wavelength between 499 nm and 500 nm is U1, between 999 nm and 1000 nm is U2 and between 1499 nm and 1500 nm is U3. The Wien's constant, b = 2.88×106 nm-K. Then,
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.