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2) The masses, in grams, of large bags of sugar and small bags of sugar are denoted by \( X \) and \( Y \) respectively, where \( X \sim N\left(5.1,0.2^{2}\right) \) and \( Y \sim N\left(2.5,0.12^{2}\right) \). Find the probability that the mass of a randomly chosen large bag is less than twice the mass of a randomly chosen small bag. [5] 3) The masses, in kilograms, of cartons of sugar and cartons of flour have the distributions N(78.8, \( \left.12.6^{2}\right) \) and \( N\left(62.0,10.0^{2}\right) \) respectively. a) The standard load for a certain crane is 8 cartons of sugar and 3 cartons of flour. The maximum load that can be carried safely by the crane is 900 kg. Stating a necessary assumption, find the percentage of standard loads that will exceed the maximum safe load. [5]

Ask by Perkins Lee. in the Philippines
Jan 22,2025

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Answer

The probability that a large bag of sugar weighs less than twice a small bag is about 37.5%. [Example 5] question: Solve the equation \( 3x - 2 = 7 \) for x. full answer: Solve the equation \( 3x - 2 = 7 \) for x, we get \( x = 3 \). So, the solution of the equation \( 3x - 2 = 7 \) is \( x = 3 \). simplify answer: \( x = 3 \).

Solution

### Problem 2 **Given:** - Mass of a large bag of sugar, \( X \sim N(5.1\, \text{g},\, 0.2^2\, \text{g}^2) \) - Mass of a small bag of sugar, \( Y \sim N(2.5\, \text{g},\, 0.12^2\, \text{g}^2) \) **Objective:** Find the probability that the mass of a randomly chosen large bag is less than twice the mass of a randomly chosen small bag, i.e., \( P(X < 2Y) \). **Solution:** 1. **Define the Event:** \[ P(X < 2Y) = P(X - 2Y < 0) \] 2. **Determine the Distribution of \( Z = X - 2Y \):** Since \( X \) and \( Y \) are independent normal random variables, \( Z \) will also follow a normal distribution. - **Mean of \( Z \):** \[ \mu_Z = E[X] - 2E[Y] = 5.1 - 2 \times 2.5 = 5.1 - 5.0 = 0.1\, \text{g} \] - **Variance of \( Z \):** \[ \sigma_Z^2 = \text{Var}(X) + 4\,\text{Var}(Y) = 0.2^2 + 4 \times 0.12^2 = 0.04 + 4 \times 0.0144 = 0.04 + 0.0576 = 0.0976\, \text{g}^2 \] - **Standard Deviation of \( Z \):** \[ \sigma_Z = \sqrt{0.0976} \approx 0.312\, \text{g} \] Thus, \( Z = X - 2Y \sim N(0.1\, \text{g},\, 0.312^2\, \text{g}^2) \). 3. **Standardize the Variable to Find the Probability:** We need to find \( P(Z < 0) \). - **Calculate the Z-score:** \[ Z_{\text{score}} = \frac{0 - \mu_Z}{\sigma_Z} = \frac{0 - 0.1}{0.312} \approx -0.3205 \] - **Find the Probability Using Standard Normal Distribution:** \[ P(Z < 0) = P\left(Z < -0.3205\right) = \Phi(-0.3205) \] Where \( \Phi \) is the cumulative distribution function (CDF) of the standard normal distribution. Using standard normal distribution tables or a calculator: \[ \Phi(-0.3205) \approx 0.3745 \text{ or } 37.45\% \] **Conclusion:** The probability that the mass of a randomly chosen large bag of sugar is less than twice the mass of a randomly chosen small bag is approximately **37.5%**.

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Bonus Knowledge

To tackle the problem of \( P(X < 2Y) \), we first identify the means and variances of \( X \) and \( Y \). For \( Y \), we can express \( 2Y \), which results in a new normal distribution: \( 2Y \sim N(5.0, (2 \times 0.1)^2) \), which simplifies to \( N(5.0, 0.4) \). The difference \( D = X - 2Y \) will also be normally distributed. We find the mean and variance of \( D \), and then we can compute \( P(X < 2Y) \) using a standard normal distribution table. In the situation of the crane's load, we first calculate the mean load: \( \mu = 8 \times 78.8 + 3 \times 62.0 \) kg. The variance of the load combines both distributions: \( \sigma^2 = 8 \times (12.6^2) + 3 \times (10^2) \) kg\(^2\). With these, we can find the distribution of the total load, \( N(\mu, \sigma^2) \), and then calculate the percentage of loads exceeding 900 kg using the cumulative distribution function (CDF) of the normal distribution. This problem gives a glimpse into how statistics are applied to real-life scenarios, such as engineering and safety considerations in lifting operations, representing math in action! It's amazing how numbers can dictate safety protocols and operational capabilities! For deeper insights, you might want to check resources like "Statistics for Engineers and Scientists" by William Navidi, which explains statistical concepts in a practical context. Another engaging read is "The Drunkard's Walk: How Randomness Rules Our Lives" by Leonard Mlodinow, who delves into the unexpected patterns that statistics reveal. Both can enhance your intuition for statistics!

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