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Question

A biased six-faced die is tossed once. Suppose that the probability of any prime number showing up is twice that of any non-prime number showing up. Then, the probability that an odd number will show up is

The correct answer is
$\frac{5}{9}$

Understanding the Biased Die Problem

This problem involves calculating probabilities for a special type of six-faced die. The die is 'biased', meaning the outcomes (numbers 1 through 6) do not have equal probabilities. We are given specific conditions about the probabilities of prime versus non-prime numbers.

Defining Prime and Non-Prime Numbers

First, let's identify the numbers on a standard six-faced die and classify them:

  • The numbers on the die are: {1, 2, 3, 4, 5, 6}.
  • Prime Numbers: A prime number is a natural number greater than 1 that has no positive divisors other than 1 and itself. In our set, the prime numbers are {2, 3, 5}.
  • Non-Prime Numbers: These are the numbers that are not prime. In our set, the non-prime numbers are {1, 4, 6}. (Note: 1 is neither prime nor composite).

Setting Up Probability Variables

Let's denote the probability of rolling a non-prime number as '$p$'. According to the question, the probability of rolling a prime number is twice that of a non-prime number. So:

  • Probability of rolling a non-prime number = $P(\text{non-prime}) = p$.
  • Probability of rolling a prime number = $P(\text{prime}) = 2p$.

This means:

  • The probability for each non-prime face (1, 4, 6) is $p$. So, $P(1) = p$, $P(4) = p$, $P(6) = p$.
  • The probability for each prime face (2, 3, 5) is $2p$. So, $P(2) = 2p$, $P(3) = 2p$, $P(5) = 2p$.

Calculating the Value of '$p$'

The sum of the probabilities of all possible outcomes must equal 1. Let's sum the probabilities:

$P(1) + P(2) + P(3) + P(4) + P(5) + P(6) = 1$

Substitute the probabilities in terms of '$p$':

$p + 2p + 2p + p + 2p + p = 1$

Combine the terms:

$9p = 1$

Solve for '$p$':

$p = \frac{1}{9}$

Determining Individual Probabilities

Now we can find the specific probability for each face:

  • $P(\text{non-prime}) = p = \frac{1}{9}$ (for faces 1, 4, 6)
  • $P(\text{prime}) = 2p = 2 \times \frac{1}{9} = \frac{2}{9}$ (for faces 2, 3, 5)

Let's summarize this in a table:

Face NumberTypeProbability
1Non-prime$\frac{1}{9}$
2Prime$\frac{2}{9}$
3Prime$\frac{2}{9}$
4Non-prime$\frac{1}{9}$
5Prime$\frac{2}{9}$
6Non-prime$\frac{1}{9}$

Calculating the Probability of an Odd Number

The question asks for the probability that an odd number will show up. The odd numbers on the die are {1, 3, 5}.

The probability of rolling an odd number is the sum of the probabilities of rolling a 1, a 3, or a 5:

$P(\text{odd}) = P(1) + P(3) + P(5)$

Substitute the calculated probabilities:

$P(\text{odd}) = \frac{1}{9} + \frac{2}{9} + \frac{2}{9}$

Add the fractions:

$P(\text{odd}) = \frac{1 + 2 + 2}{9} = \frac{5}{9}$

Conclusion

Therefore, the probability that an odd number will show up when this biased die is tossed is $\frac{5}{9}$.

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Important Questions from Discrete Probability

  1. Let $X$ and $Y$ be independent Poisson random variables with means $4$ and $2$, respectively. Then, which of the following statements are true?
  2. Consider the M/M/1 queue in which customers arrive according to a Poisson process with rate $3$ and successive service times are independent exponential random variables having mean $\frac{1}{9}$. Let $P_n$ be the long run probability that there are exactly $n$ customers in the system. Then, which of the following statements are true?
  3. Let $X$ be a Binomial$(n, p)$ random variable, where $n \in \{5,6\}$ and $p\in \{\frac{1}{4}, \frac{3}{4}\}$. If $X = 3$ is observed, then the maximum likelihood estimate of $(n, p)$ is
  4. Suppose two fair dice are thrown independently at random. Let $X$ and $Y$ be the numbers on the upper face of the first die and that of the second die, respectively. Then which of the following statements are true?
  5. Suppose customers arrive in a shop according to a Poisson process with rate 4 per hour. The shop opens at 10:00 am. If it is given that the second customer arrives at 10:40 am, what is the probability that no customer arrived before 10:30 am?
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