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A Poisson distribution models voter arrivals at a polling station with an average rate of 5 voters per 20 minutes. Calculate: a) The probability of no voters arriving between 3:10 pm and 3:30 pm. b) T

Mathematics

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Answers

5 parts
a

0.006738\text{0.006738}

b

0.001936\text{0.001936}

c

5\text{5}

20\text{20}

d

0.08192\text{0.08192}

e

21\text{21}

Step-by-step solution

10 steps

Part i

Poisson Distribution

The number of voters arriving at a polling station follows a Poisson distribution.
The average rate is given as λ0=5\lambda_0 = 5 voters in 20 minutes.
The probability mass function for a Poisson distribution is P(X=k)=e−λλkk!P(X=k) = \frac{e^{-\lambda} \lambda^k}{k!}.

Part a

0.006738\text{0.006738}

Calculate the probability that no voter will arrive between 3:10 and 3:30 pm.

  1. Determine the time interval and corresponding λ\lambda.

    The time interval is from 3:10 pm to 3:30 pm, which is 3:30−3:10=203:30 - 3:10 = 20 minutes.
    For a 20-minute interval, the average rate λ=5\lambda = 5.

  2. Calculate the probability for X=0X=0.

    We want to find P(X=0)P(X=0).

    P(X=0)=e−5500!=e−5⋅11=e−5P(X=0) = \frac{e^{-5} 5^0}{0!} = \frac{e^{-5} \cdot 1}{1} = e^{-5} P(X=0)≈0.006738P(X=0) \approx 0.006738

    The probability that no voter will arrive is 0.006738\boxed{\text{0.006738}}.

Part b

0.001936\text{0.001936}

Calculate the probability that there will be exactly 5 voters between 9:20-10:20.

  1. Determine the time interval and corresponding λ\lambda.

    The time interval is from 9:20 to 10:20, which is 10:20−9:20=6010:20 - 9:20 = 60 minutes.
    Since the rate is 5 voters per 20 minutes, for a 60-minute interval, the new average rate λ′\lambda' is:

    λ′=5 voters20 minutes×60 minutes=5×3=15\lambda' = \frac{5 \text{ voters}}{20 \text{ minutes}} \times 60 \text{ minutes} = 5 \times 3 = 15
  2. Calculate the probability for X=5X=5 with λ′=15\lambda' = 15.

    We want to find P(X=5)P(X=5).

    P(X=5)=e−151555!P(X=5) = \frac{e^{-15} 15^5}{5!} P(X=5)=e−15×759375120P(X=5) = \frac{e^{-15} \times 759375}{120} P(X=5)≈3.05902×10−7×759375120P(X=5) \approx \frac{3.05902 \times 10^{-7} \times 759375}{120} P(X=5)≈0.23226120≈0.0019355P(X=5) \approx \frac{0.23226}{120} \approx 0.0019355

    The probability that there will be exactly 5 voters is 0.001936\boxed{\text{0.001936}}.

Part b(ii)

Geometric Distribution

Let XX be the number of church services attended until the first success (finding a wife).
The probability of getting a wife (success) on any given service is p=15p = \frac{1}{5}.
The probability mass function for a Geometric distribution (number of trials until first success) is P(X=k)=(1−p)k−1pP(X=k) = (1-p)^{k-1}p.

Part c

5\text{5}

Calculate the mean and variance of the number of times he attends church service.

  1. Calculate the mean.

    For a Geometric distribution, the mean E(X)E(X) is given by:

    E(X)=1pE(X) = \frac{1}{p} E(X)=11/5=5E(X) = \frac{1}{1/5} = 5
  2. Calculate the variance.

    For a Geometric distribution, the variance Var(X)Var(X) is given by:

    Var(X)=1−pp2Var(X) = \frac{1-p}{p^2} Var(X)=1−1/5(1/5)2=4/51/25=45×25=20Var(X) = \frac{1 - 1/5}{(1/5)^2} = \frac{4/5}{1/25} = \frac{4}{5} \times 25 = 20

    The mean is 5\boxed{\text{5}} and the variance is 20\boxed{\text{20}}.

Part d

0.08192\text{0.08192}

Calculate the probability that he finds a wife on the 5th church service.

  1. Use the Geometric probability formula for X=5X=5.

    We want to find P(X=5)P(X=5).

    P(X=5)=(1−p)5−1pP(X=5) = (1-p)^{5-1}p P(X=5)=(1−15)4×15P(X=5) = \left(1 - \frac{1}{5}\right)^{4} \times \frac{1}{5} P(X=5)=(45)4×15P(X=5) = \left(\frac{4}{5}\right)^{4} \times \frac{1}{5} P(X=5)=256625×15=2563125P(X=5) = \frac{256}{625} \times \frac{1}{5} = \frac{256}{3125} P(X=5)=0.08192P(X=5) = 0.08192

    The probability that he finds a wife on the 5th church service is 0.08192\boxed{\text{0.08192}}.

Part e

21\text{21}

Find the least integer n, the number of times he attends church service such that P(X>n)<1%P(X > n) < 1\%.

  1. Set up the inequality for P(X>n)P(X > n).

    For a Geometric distribution, P(X>n)=(1−p)nP(X > n) = (1-p)^n.
    We are given P(X>n)<1%P(X > n) < 1\%, which is P(X>n)<0.01P(X > n) < 0.01.
    So, we need to solve:

    (1−15)n<0.01\left(1 - \frac{1}{5}\right)^n < 0.01 (45)n<0.01\left(\frac{4}{5}\right)^n < 0.01 0.8n<0.010.8^n < 0.01
  2. Solve for nn using logarithms.

    Take the logarithm (base 10 or natural log) of both sides. Using log⁡10\log_{10}:

    nlog⁡10(0.8)<log⁡10(0.01)n \log_{10}(0.8) < \log_{10}(0.01)

    Calculate the logarithm values:
    log⁡10(0.8)≈−0.09691\log_{10}(0.8) \approx -0.09691
    log⁡10(0.01)=−2\log_{10}(0.01) = -2
    Substitute these values into the inequality:

    n(−0.09691)<−2n (-0.09691) < -2

    Divide by −0.09691-0.09691. Remember to reverse the inequality sign when dividing by a negative number:

    n>−2−0.09691n > \frac{-2}{-0.09691} n>20.637n > 20.637
  3. Determine the least integer nn.

    Since nn must be an integer and n>20.637n > 20.637, the least integer value for nn is 21.
    The least integer nn is 21\boxed{\text{21}}.

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