Step 1: Understanding the problem.
We are given that there are 19 unbiased coins and 1 biased coin in the bag. The problem asks for the probability that the drawn coin was unbiased given that heads turned up. This is a conditional probability problem, and we can apply Bayes' Theorem to solve it.
Let:
- \( A \) be the event that the coin drawn is unbiased.
- \( B \) be the event that heads turns up. We are required to find \( P(A | B) \), the probability that the coin is unbiased given that heads turned up.
According to Bayes' Theorem: \[ P(A | B) = \frac{P(B | A) P(A)}{P(B)} \]
Step 2: Finding individual probabilities.
\( P(A) = \frac{19}{20} \), the probability of drawing an unbiased coin. \( P(B | A) = \frac{1}{2} \), the probability of getting heads when the coin is unbiased. \( P(B | A^c) = 1 \), the probability of getting heads when the coin is biased (since both sides are heads). \( P(A^c) = \frac{1}{20} \), the probability of drawing the biased coin. Now, calculate \( P(B) \), the total probability of getting heads: \[ P(B) = P(B | A) P(A) + P(B | A^c) P(A^c) \] \[ P(B) = \left(\frac{1}{2}\right) \left(\frac{19}{20}\right) + 1 \left(\frac{1}{20}\right) \] \[ P(B) = \frac{19}{40} + \frac{1}{20} = \frac{19}{40} + \frac{2}{40} = \frac{21}{40} \]
Step 3: Using Bayes' Theorem.
Now, applying Bayes' Theorem: \[ P(A | B) = \frac{P(B | A) P(A)}{P(B)} = \frac{\left(\frac{1}{2}\right) \left(\frac{19}{20}\right)}{\frac{21}{40}} \] \[ P(A | B) = \frac{\frac{19}{40}}{\frac{21}{40}} = \frac{19}{21} \] This gives the probability that the coin was unbiased as \( \frac{19}{21} \). So, \( m = 19 \) and \( n = 21 \).
Step 4: Finding \( n^2 - m^2 \).
Now we calculate \( n^2 - m^2 \): \[ n^2 - m^2 = 21^2 - 19^2 = (21 + 19)(21 - 19) = 40 \times 2 = 80 \]
Match List-I with List-II.
Choose the correct answer from the options given below :