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 \( P(X \geq 2) \):
\[ P(X \geq 2) = 1 - P(X = 0) - P(X = 1). \]
\[ P(X = 0) = \frac{4}{9} \cdot (0 + 1) \cdot 3^0 = \frac{4}{9}. \]
\[ P(X = 1) = \frac{4}{9} \cdot (1 + 1) \cdot 3^{-1} = \frac{4}{9} \cdot 2 \cdot \frac{1}{3} = \frac{8}{27}. \]
\[ P(X \geq 2) = 1 - \frac{4}{9} - \frac{8}{27} = \frac{27}{27} - \frac{12}{27} - \frac{8}{27} = \frac{7}{27}. \]
Consider the following reaction occurring in the blast furnace. \[ {Fe}_3{O}_4(s) + 4{CO}(g) \rightarrow 3{Fe}(l) + 4{CO}_2(g) \] ‘x’ kg of iron is produced when \(2.32 \times 10^3\) kg \(Fe_3O_4\) and \(2.8 \times 10^2 \) kg CO are brought together in the furnace.
The value of ‘x’ is __________ (nearest integer).
Among the following cations, the number of cations which will give characteristic precipitate in their identification tests with
\(K_4\)[Fe(CN)\(_6\)] is : \[ {Cu}^{2+}, \, {Fe}^{3+}, \, {Ba}^{2+}, \, {Ca}^{2+}, \, {NH}_4^+, \, {Mg}^{2+}, \, {Zn}^{2+} \]
X g of benzoic acid on reaction with aqueous \(NaHCO_3\) release \(CO_2\) that occupied 11.2 L volume at STP. X is ________ g.
Standard entropies of \(X_2\), \(Y_2\) and \(XY_5\) are 70, 50, and 110 J \(K^{-1}\) mol\(^{-1}\) respectively. The temperature in Kelvin at which the reaction \[ \frac{1}{2} X_2 + \frac{5}{2} Y_2 \rightarrow XY_5 \quad \Delta H = -35 \, {kJ mol}^{-1} \] will be at equilibrium is (nearest integer):
37.8 g \( N_2O_5 \) was taken in a 1 L reaction vessel and allowed to undergo the following reaction at 500 K: \[ 2N_2O_5(g) \rightarrow 2N_2O_4(g) + O_2(g) \]
The total pressure at equilibrium was found to be 18.65 bar. Then, \( K_p \) is: Given: \[ R = 0.082 \, \text{bar L mol}^{-1} \, \text{K}^{-1} \]
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.