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1 TIMEREMAINING \( 46: 43 \) Julian is a manager at a clothing store for teens. He is analyzing the order for next seas̈on. Data for the previous 10 years suggests that teens are willing to spend an average of \( \$ 75 \) for a pair of designer jeans with a standard deviation of \( \$ 5 \). However, Julian thinks the average may have changed due to a recession. He finds that the last three seasons of data show that teens spent an average of \( \$ 68 \) on a pair of jeans. Therefore, he performed a hypothesis test to see if the recent average is the same. Julian used a significance level of \( 5 \% \) to perform the test. Which of the following statements is valid based on the results? Julian's data shows that the recent seasons' average jean price is not \( \$ 75 \). Julian's data shows that he should order fewer jeans than before. Julian's data shows that the recent seasons' average jean price is still \( \$ 75 \). Julian's data is inconclusive, so he should order the same number of jeans.

Ask by Norris Watkins. in the United States
Feb 25,2025

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Answer

Julian's data shows that the recent seasons' average jean price is not \$75.

Solution

To analyze Julian's hypothesis test, we need to follow these steps: 1. **Identify the null and alternative hypotheses:** - Null Hypothesis (\(H_0\)): The average price teens are willing to spend on jeans is \( \$ 75 \) (i.e., \( \mu = 75 \)). - Alternative Hypothesis (\(H_a\)): The average price teens are willing to spend on jeans is not \( \$ 75 \) (i.e., \( \mu \neq 75 \)). 2. **Determine the significance level:** - The significance level (\(\alpha\)) is \( 0.05 \) (5%). 3. **Calculate the test statistic:** - We will use the formula for the z-test since we know the population standard deviation: \[ z = \frac{\bar{x} - \mu}{\frac{\sigma}{\sqrt{n}}} \] where: - \(\bar{x} = 68\) (sample mean), - \(\mu = 75\) (population mean under the null hypothesis), - \(\sigma = 5\) (population standard deviation), - \(n\) is the sample size. Since the sample size is not provided, we will assume it is large enough for the z-test to be valid. 4. **Determine the critical z-values:** - For a two-tailed test at the \(0.05\) significance level, the critical z-values are approximately \(-1.96\) and \(1.96\). 5. **Make a decision:** - If the calculated z-value falls outside the range of \(-1.96\) to \(1.96\), we reject the null hypothesis. Now, let's calculate the z-value using the provided data. Since we don't have the sample size, we will proceed with the calculation assuming a large sample size. Let's calculate the z-value. Given: - \(\bar{x} = 68\) - \(\mu = 75\) - \(\sigma = 5\) We will assume \(n\) is sufficiently large for the calculation. Now, let's calculate the z-value. \[ z = \frac{68 - 75}{\frac{5}{\sqrt{n}}} \] Since we don't have \(n\), we will express the z-value in terms of \(n\): \[ z = \frac{-7}{\frac{5}{\sqrt{n}}} = -\frac{7\sqrt{n}}{5} \] Now, we need to analyze the implications of this z-value. ### Conclusion: - If \(n\) is large enough such that \(z < -1.96\), we reject \(H_0\) and conclude that the average price is not \( \$ 75 \). - If \(z\) is between \(-1.96\) and \(1.96\), we fail to reject \(H_0\) and conclude that there is not enough evidence to say the average price is different from \( \$ 75 \). Based on the information provided, if Julian's data shows that the recent seasons' average jean price is not \( \$ 75 \), he should order fewer jeans than before. Thus, the valid statement based on the results is: **Julian's data shows that the recent seasons' average jean price is not \( \$ 75 \).**

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Julian's hypothesis test aims to see if the recent average price teens are willing to pay for jeans has indeed changed from the historical average of $75. If he found a statistically significant result at the 5% significance level, it would indicate a true shift in consumer behavior, suggesting that ordering fewer jeans might be wise. However, if his data does not reject the null hypothesis, that implies the average price is still considered to be $75, and he should maintain current ordering levels. In the world of statistics, understanding the distinction between Type I and Type II errors is crucial. A Type I error occurs when Julian wrongly concludes that the recent average price has changed, leading to unnecessary adjustments in stock. Conversely, a Type II error would imply missing out on genuine shifts in consumer spending. Recognizing these errors can help Julian avoid costly mistakes in his ordering strategy!

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