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Grand Canyon University *

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MIS-665

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Economics

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Apr 3, 2024

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docx

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In order to determine if the manufacturer's inventory level is significantly different from the industry norm, we can calculate a confidence interval around the sample mean and see if it includes the industry average. For a 95% confidence interval, we can use a standard normal distribution (Z) with a critical value of 1.96, as the sample size is large (n=120) and the standard deviation is known (Sullivan & Artino, 2013). The margin of error (E) can be calculated using the formula: E = Z * (standard deviation / sqrt(n)). In this case, Z = 1.96, standard deviation = 72, and n = 120. Plugging in these values, we get: E = 1.96 * (72 / sqrt(120)) E = 10.45 The confidence interval is then calculated by subtracting and adding the margin of error to the sample mean: CI = sample mean ± margin of error CI = 310 ± 10.45 CI = (299.55, 320.45) Since the industry average of 325 is not within the confidence interval, we can conclude that the manufacturer's inventory level is significantly different from the industry norm at a 95% confidence level (Sullivan & Artino, 2013). If we were to use a 99% confidence interval, the critical value for a standard normal distribution would be 2.58. Using the same formula and plugging in the new values, we find: E = 2.58 * (72 / sqrt(120)) E = 13.85 The 99% confidence interval would be:
CI = sample mean ± margin of error CI = 310 ± 13.85 CI = (296.15, 323.85) Since the industry average of 325 is still not within the 99% confidence interval, our conclusion remains the same - the manufacturer's inventory level is significantly different from the industry norm at a 99% confidence level as well (Sullivan & Artino, 2013). In summary, based on the calculated confidence intervals, we can confidently say that the manufacturers retail tire inventory level is considerably different from the industry norm at 95% and 99% confidence levels, indicating that their inventory level is not representative of the inventory average. References: Sullivan, G. M., & Artino, A. R. (2013). Analyzing and interpreting data from Likert-type scales. Journal of Graduate Medical Education, 5(4), 541-542. doi:10.4300/JGME-D-13-00154.1
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