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The random variable X has a normal distribution with mean mu and standard deviation sigma. The mean and standard deviation are also related by the equation 2sigma = 5mu, where mu eq 0. Calculate P(X >

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Here are the solutions for questions 18, 19, and 20.

18. The random variable XX has a normal distribution with mean μ\mu and standard deviation σ\sigma. The mean and standard deviation are also related by the equation 2σ=5μ2\sigma = 5\mu, where μ≠0\mu \neq 0. Calculate P(X>5μ)P(X > 5\mu).

  1. Express the standard deviation σ\sigma in terms of the mean μ\mu.

    Given 2σ=5μ2\sigma = 5\mu, we can write:

    σ=52μ=2.5μ\sigma = \frac{5}{2}\mu = 2.5\mu
  2. Standardize the value X=5μX = 5\mu using the Z-score formula.

    The Z-score formula is Z=X−μσZ = \frac{X - \mu}{\sigma}.
    Substitute X=5μX = 5\mu and σ=2.5μ\sigma = 2.5\mu:

    Z=5μ−μ2.5μ=4μ2.5μ=42.5=1.6Z = \frac{5\mu - \mu}{2.5\mu} = \frac{4\mu}{2.5\mu} = \frac{4}{2.5} = 1.6
  3. Calculate the probability P(X>5μ)P(X > 5\mu), which is equivalent to P(Z>1.6)P(Z > 1.6).

    Using a standard normal distribution table or calculator, P(Z≤1.6)≈0.9452P(Z \le 1.6) \approx 0.9452.
    Therefore, P(Z>1.6)=1−P(Z≤1.6)P(Z > 1.6) = 1 - P(Z \le 1.6):

    P(Z>1.6)=1−0.9452=0.0548P(Z > 1.6) = 1 - 0.9452 = 0.0548

    The correct option is A.
    The probability P(X>5μ)P(X > 5\mu) is A. 0.0548\boxed{\text{A. } \textbf{0.0548}}.

    19. In a Durban suburb, 10% of houses have solar geyser. 16 houses are chosen at random. Find the probability that fewer than 4 houses have solar geyser.

  4. Identify the parameters for the binomial distribution.

    This is a binomial distribution problem, where nn is the number of trials and pp is the probability of success.
    Number of houses chosen, n=16n = 16.
    Probability of a house having a solar geyser, p=0.10p = 0.10.
    Probability of a house not having a solar geyser, 1−p=0.901-p = 0.90.
    We need to find the probability that fewer than 4 houses have a solar geyser, which means P(X<4)P(X < 4) or P(X≤3)P(X \le 3).
    This is P(X=0)+P(X=1)+P(X=2)+P(X=3)P(X=0) + P(X=1) + P(X=2) + P(X=3).

  5. Use the binomial probability formula P(X=k)=(nk)pk(1−p)n−kP(X=k) = \binom{n}{k} p^k (1-p)^{n-k} for each value of kk.

    For k=0k=0:

    P(X=0)=(160)(0.10)0(0.90)16=1×1×0.185302≈0.1853P(X=0) = \binom{16}{0} (0.10)^0 (0.90)^{16} = 1 \times 1 \times 0.185302 \approx 0.1853

    For k=1k=1:

    P(X=1)=(161)(0.10)1(0.90)15=16×0.10×0.205891≈0.3294P(X=1) = \binom{16}{1} (0.10)^1 (0.90)^{15} = 16 \times 0.10 \times 0.205891 \approx 0.3294

    For k=2k=2:

    P(X=2)=(162)(0.10)2(0.90)14=120×0.01×0.228768≈0.2745P(X=2) = \binom{16}{2} (0.10)^2 (0.90)^{14} = 120 \times 0.01 \times 0.228768 \approx 0.2745

    For k=3k=3:

    P(X=3)=(163)(0.10)3(0.90)13=560×0.001×0.254187≈0.1423P(X=3) = \binom{16}{3} (0.10)^3 (0.90)^{13} = 560 \times 0.001 \times 0.254187 \approx 0.1423
  6. Sum the probabilities.

    P(X<4)=P(X=0)+P(X=1)+P(X=2)+P(X=3)P(X < 4) = P(X=0) + P(X=1) + P(X=2) + P(X=3) P(X<4)=0.1853+0.3294+0.2745+0.1423=0.9315P(X < 4) = 0.1853 + 0.3294 + 0.2745 + 0.1423 = 0.9315

    Rounding to four decimal places, $P(X < 4) \approx 0.931

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