Finding a confidence interval.

In summary, to find the 99% confidence interval for the mean diameter of metal pieces produced by a machine, we use a sample of 9 pieces with diameters of 1.01, 0.97, 1.03, 1.04, 0.99, 0.98, 0.99, 1.01, and 1.03 centimeters. From this data, we find the sample mean to be 1.0056 and the standard deviation to be calculated using the formula s^2 = (1/(n-1)) * ∑ (x_i - x(bar))^2. With a normal population but unknown variance, we use the t statistic with α =
  • #1
Hiche
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Homework Statement



A machine produces metal pieces that are cylindrical in shape. A sample of pieces is taken and the diameters are found to be 1.01, 0.97, 1.03, 1.04, 0.99, 0.98, 0.99, 1.01, 1.03 centimeters. Find a 99% confidence interval for the mean diameter of pieces from this machine, assuming an approximately normal distribution.

Homework Equations





The Attempt at a Solution



From our data, we know N = 9. We find the sample mean X(bar) = 1.0056 and standard deviation s = ? (how exactly do we find this? Do we use the equation (1/n-1) * Ʃ (Xi - X(bar))2?. Moving on..

Since we have a normal population but UNKNOWN population variance, we muse use the t statistic: α = 0.01 and tα/2 = 3.355 from the t-distribution table.

1.0056 ± (3.355) * s / √9 and then compute. Is this the way? Also, like I asked before, how do we find s exactly from our data?
 
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  • #2
Hiche said:

Homework Statement



A machine produces metal pieces that are cylindrical in shape. A sample of pieces is taken and the diameters are found to be 1.01, 0.97, 1.03, 1.04, 0.99, 0.98, 0.99, 1.01, 1.03 centimeters. Find a 99% confidence interval for the mean diameter of pieces from this machine, assuming an approximately normal distribution.

Homework Equations


The Attempt at a Solution



From our data, we know N = 9. We find the sample mean X(bar) = 1.0056 and standard deviation s = ? (how exactly do we find this? Do we use the equation (1/n-1) * Ʃ (Xi - X(bar))2?. Moving on..

Since we have a normal population but UNKNOWN population variance, we muse use the t statistic: α = 0.01 and tα/2 = 3.355 from the t-distribution table.

1.0056 ± (3.355) * s / √9 and then compute. Is this the way? Also, like I asked before, how do we find s exactly from our data?

Just use the standard formulas that can be found in your textbook or in hundreds of sources on-line. The formula you wrote above is almost correct, but you wrote
[tex] s^2 = \left( \frac{1}{n}-1\right)\sum (x_i - \bar{x})^2[/tex]
instead of the correct
[tex] s^2 = \frac{1}{n-1} \sum (x_i - \bar{x})^2 .[/tex] In other words, use (1/(n-1))!
 

FAQ: Finding a confidence interval.

What is a confidence interval?

A confidence interval is a range of values that is likely to contain the true value of a population parameter with a certain level of confidence. It is often used in statistics to estimate the true value of a population parameter based on a sample of data.

How is a confidence interval calculated?

A confidence interval is calculated using a formula that takes into account the sample size, the standard deviation of the data, and the desired level of confidence. The most commonly used formula for calculating a confidence interval is the t-distribution formula.

What is the significance of the confidence level in a confidence interval?

The confidence level in a confidence interval represents the probability that the true value of the population parameter falls within the calculated interval. For example, a 95% confidence level means that there is a 95% chance that the true population parameter falls within the calculated interval.

How does the sample size affect the width of a confidence interval?

The sample size has an inverse relationship with the width of a confidence interval. As the sample size increases, the width of the confidence interval decreases. This is because larger sample sizes provide more precise estimates of the true population parameter, resulting in a narrower interval.

Can a confidence interval be used to make predictions?

No, a confidence interval should not be used to make predictions about individual data points. It is only meant to provide a range of values that is likely to contain the true population parameter. Predictions should be made using other statistical methods, such as regression analysis.

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