Suppose that Sandy is an analyst for the bicyling industry and wants to estimate the asking price of used entry-level road bikes advertised online in the southeastern part of the United States. She obtains a random sample of n = 10 online advertisements of entry-level road bikes. She determines that the mean price for these 10 bikes is $659.87 and that the sample standard deviation is s = $197.02. She uses this information to construct a 95% confidence interval for u, the mean price of a used road bike. What is the upper limit of this confidence interval? Please give your answer to the nearest cent.

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**Estimation of the Asking Price of Used Entry-Level Road Bikes**

**Problem Statement:**

Sandy, an analyst in the bicycling industry, aims to estimate the asking price of used entry-level road bikes advertised online in the southeastern part of the United States. She collects a random sample of \( n = 10 \) online advertisements for entry-level road bikes. From this sample, Sandy determines that the mean price is \( \$659.87 \) and the sample standard deviation is \( s = \$197.02 \). Sandy utilizes this data to construct a 95% confidence interval for \( \mu \), the mean price of a used road bike.

**Question:**

What is the upper limit of this confidence interval? Please provide your answer to the nearest cent.

**Solution Framework:**

To find the confidence interval, Sandy will use the formula for the confidence interval for the mean:
\[ \text{CI} = \bar{x} \pm t_{\alpha/2} \left( \frac{s}{\sqrt{n}} \right) \]

Where:
- \( \bar{x} \) is the sample mean.
- \( t_{\alpha/2} \) is the t-value for the 95% confidence level with \( n-1 \) degrees of freedom.
- \( s \) is the sample standard deviation.
- \( n \) is the sample size.

Given the sample size of 10, Sandy will use the t-distribution to find the critical value \( t_{\alpha/2} \). Since the sample size is small (n < 30), using the t-distribution is appropriate for better accuracy compared to the z-distribution.

After calculating the values, the upper limit of the confidence interval will give us the higher end of the estimated range for the mean price of these used bikes.

**Graph/Diagram Explanation:**

There are no graphs or diagrams associated with this text. All necessary calculations can be carried out using statistical tables or computational tools to find the required t-value for constructing the confidence interval.
Transcribed Image Text:**Estimation of the Asking Price of Used Entry-Level Road Bikes** **Problem Statement:** Sandy, an analyst in the bicycling industry, aims to estimate the asking price of used entry-level road bikes advertised online in the southeastern part of the United States. She collects a random sample of \( n = 10 \) online advertisements for entry-level road bikes. From this sample, Sandy determines that the mean price is \( \$659.87 \) and the sample standard deviation is \( s = \$197.02 \). Sandy utilizes this data to construct a 95% confidence interval for \( \mu \), the mean price of a used road bike. **Question:** What is the upper limit of this confidence interval? Please provide your answer to the nearest cent. **Solution Framework:** To find the confidence interval, Sandy will use the formula for the confidence interval for the mean: \[ \text{CI} = \bar{x} \pm t_{\alpha/2} \left( \frac{s}{\sqrt{n}} \right) \] Where: - \( \bar{x} \) is the sample mean. - \( t_{\alpha/2} \) is the t-value for the 95% confidence level with \( n-1 \) degrees of freedom. - \( s \) is the sample standard deviation. - \( n \) is the sample size. Given the sample size of 10, Sandy will use the t-distribution to find the critical value \( t_{\alpha/2} \). Since the sample size is small (n < 30), using the t-distribution is appropriate for better accuracy compared to the z-distribution. After calculating the values, the upper limit of the confidence interval will give us the higher end of the estimated range for the mean price of these used bikes. **Graph/Diagram Explanation:** There are no graphs or diagrams associated with this text. All necessary calculations can be carried out using statistical tables or computational tools to find the required t-value for constructing the confidence interval.
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