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An engineer wishes to determine the width of a particular electronic component. If she knows that the standard deviation is 3.3 mm, how many of these components should she consider to be 90% sure of knowing the mean will be within ± 0.3 mm

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The engineer should consider a sample size of 325 electronic components to be 90% sure that the mean width will be within ± 0.3 mm.

To determine the required sample size, we can use the formula for the margin of error (ME) in the context of the normal distribution:

ME = (Z * σ) / √n

Where:

ME = Margin of error

Z = Z-score, which corresponds to the desired level of confidence (in this case, 90%)

σ = Standard deviation (3.3 mm in this problem)

n = Sample size

First, we need to find the Z-score corresponding to a 90% confidence level. This can be found using a Z-table or statistical software. The Z-score for a 90% confidence interval is approximately 1.645.

Now, we will rearrange the formula to solve for the sample size (n):

n = (Z * σ / ME)^2

We are given that the margin of error should be within ± 0.3 mm:

n = (1.645 * 3.3 / 0.3)^2

n ≈ (18.015)^2

n ≈ 324.54

Since we cannot have a fraction of a component, we round up to the nearest whole number to ensure the desired level of confidence:

n ≈ 325

The engineer should consider a sample size of 325 electronic components to be 90% sure that the mean width will be within ± 0.3 mm.

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