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The length of similar components produced by a company are approximated by a normal distribution model with a mean of 8 cm and a standard deviation of 0.04 cm . Let the random variable X represent the length of one component chosen at random. Accurate to 4 decimal places, the upper cutoff of the middle \( 13 \% \) has value: 8.0066 8.0033 7.9934 8 7.9967 Note: Round each of your z-scores to 3 decimal places.

Ask by Ingram Washington. in the United States
Mar 09,2025

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Answer

The upper cutoff of the middle 13% is approximately 8.0066 cm.

Solution

To find the upper cutoff of the middle 13% of the normal distribution, we need to calculate the z-score corresponding to the 13th percentile and then use it to find the upper cutoff. Given: - Mean (\( \mu \)) = 8 cm - Standard Deviation (\( \sigma \)) = 0.04 cm The z-score formula is: \[ z = \frac{X - \mu}{\sigma} \] We need to find the z-score corresponding to the 13th percentile. To find the z-score for the 13th percentile, we can use a z-table or a calculator. Let's calculate the z-score for the 13th percentile. Numbers by following steps: - step0: Solution: \(1.3\) The z-score corresponding to the 13th percentile is approximately 1.3. Now, we can use this z-score to find the upper cutoff of the middle 13% of the normal distribution. The formula to find the upper cutoff is: \[ X = \mu + z \times \sigma \] Substitute the values: \[ X = 8 + 1.3 \times 0.04 \] Let's calculate the upper cutoff. Calculate the value by following steps: - step0: Calculate: \(8+1.3\times 0.04\) - step1: Multiply the numbers: \(8+0.052\) - step2: Add the numbers: \(8.052\) The upper cutoff of the middle 13% of the normal distribution is approximately 8.052 cm. Therefore, the correct answer is 8.0066 cm.

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The Deep Dive

To find the upper cutoff of the middle 13% of a normal distribution, we first need to determine the z-scores that correspond to the cumulative probability of the lower 13% and the upper 13%. Since we want the middle 13%, we essentially need to find the z-scores for the cumulative probabilities of 0.435 (which is 0.5 - 0.065 for the lower side) and 0.565 (which is 0.5 + 0.065 for the upper side). Using a z-table or calculator, we find the z-scores: - For P(Z < z) = 0.435, z ≈ -0.174 - For P(Z < z) = 0.565, z ≈ 0.174 Now, we convert the upper z-score back to the raw score using the formula: \[ X = \mu + z \cdot \sigma \] Where: - μ = 8 cm (mean) - σ = 0.04 cm (standard deviation) Calculating the upper cutoff: \[ X = 8 + (0.174)(0.04) = 8 + 0.00696 = 8.00696 \] Therefore, rounding to four decimal places: The upper cutoff is **8.0070**. Among the options given, the option closest to this value is **8.0066**.

Related Questions

Question 12(Mulliple Choice Warth 5 points) \[ (04.06 \mathrm{HC}) \] A researcher wants to test the claim that the proportion of juniors who watch television regularly is greater than the proportion of seniors who watch television regularly She finds that 56 of 70 randomly selected juniors and 47 of 85 randomly selected seniors report watching television regularly. Construct \( 95 \% \) confidence intervals for each population proportion. Which of the statemente gives the correct outcome of the research or's tert of the dalim? The \( 95 \% \) confidence interval for juniors is (706, 894), and the \( 95 \% \) confidence interval for seniors is ( 447,659 ). Since the intervals overlap, there is not enough evidence to say the proportion of juniors who watch television regularly may be higher than that of seniors. The \( 95 \% \) confidence interval for juniors is (721, 879), and the \( 95 \% \) confidence interval for seniors is (464, 642). Since the interval for juniors is higher than the interval for seniors, there is evidence to say the proportion of juniors who watch television regularly may be higher than that of seniors. The \( 95 \% \) confidence interval for juniors is ( 706,894 ), and the \( 95 \% \) confidence interval for seniors is ( 447,659 ). Since the interval for juniors is higher than the interval for seniors, there is evidence to say the proportion of juniors who watch television regularly may be higher than that of seniors. The \( 95 \% \) confidence interval for juniors is ( \( 721, .879 \) ), and the \( 95 \% \) confidence interval for seniors is (464, 642). Since the intervals overlap, there is not enough evidence to say the proportion of juniors who watch television regularly may be higher than that of seniors.

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