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The sum of variance and mean of a binomi...

The sum of variance and mean of a binomial distribution for 10 trials is 7.5. Find the distribution.

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To solve the problem step by step, we need to find the parameters of a binomial distribution given that the sum of its variance and mean is 7.5 and the number of trials \( n = 10 \). ### Step 1: Understand the properties of a binomial distribution For a binomial distribution with \( n \) trials and probability of success \( p \): - The mean \( \mu \) is given by: \[ \mu = np \] - The variance \( \sigma^2 \) is given by: \[ \sigma^2 = npq \] where \( q = 1 - p \). ### Step 2: Set up the equation based on the given information We know from the problem statement that: \[ \text{Variance} + \text{Mean} = 7.5 \] Substituting the expressions for mean and variance: \[ npq + np = 7.5 \] Given \( n = 10 \): \[ 10pq + 10p = 7.5 \] ### Step 3: Simplify the equation Dividing the entire equation by 10 to simplify: \[ pq + p = 0.75 \] Factoring out \( p \): \[ p(q + 1) = 0.75 \] Since \( q = 1 - p \), we can substitute \( q + 1 \): \[ p(1 - p + 1) = 0.75 \] This simplifies to: \[ p(2 - p) = 0.75 \] ### Step 4: Rearranging the equation Expanding the equation gives: \[ 2p - p^2 = 0.75 \] Rearranging this into standard quadratic form: \[ p^2 - 2p + 0.75 = 0 \] ### Step 5: Solve the quadratic equation Using the quadratic formula \( p = \frac{-b \pm \sqrt{b^2 - 4ac}}{2a} \): Here, \( a = 1, b = -2, c = 0.75 \): \[ p = \frac{-(-2) \pm \sqrt{(-2)^2 - 4 \cdot 1 \cdot 0.75}}{2 \cdot 1} \] Calculating the discriminant: \[ p = \frac{2 \pm \sqrt{4 - 3}}{2} \] \[ p = \frac{2 \pm 1}{2} \] This gives us two possible values for \( p \): 1. \( p = \frac{3}{2} \) (not valid since \( p \) cannot be greater than 1) 2. \( p = \frac{1}{2} \) ### Step 6: Find \( q \) Since \( q = 1 - p \): \[ q = 1 - \frac{1}{2} = \frac{1}{2} \] ### Step 7: Conclusion The parameters of the binomial distribution are: - \( n = 10 \) - \( p = \frac{1}{2} \) - \( q = \frac{1}{2} \) Thus, the binomial distribution is: \[ B(10, 0.5) \]
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