Mercury in Tuna - Mercury, if ingested can cause severe health problems. The amount of mercury in Tuna's body tissue is much higher than other fish. It is known that the amount of mercury in Albacore tuna is normally distributed with mean 10.13 micrograms and standard deviation of 2.03 micrograms per ounce. A food company has a production line for canned albacore tuna. They regularly take random samples of 10 cans of tuna and test the amount of mercury. If the sample mean amount of mercury in tuna exceeds 12 micrograms per ounce, the production line will be stopped to find the source of contamination. Let u denote the actual population mean amount of mercury in tuna per ounce. The hypotheses for this testing situation are: 10.13 vs HA: µ > 10.13. What is the chance of making a Type I error using the decision rule given above? Give your answer to 4 decimal places. Your Answer:
Mercury in Tuna - Mercury, if ingested can cause severe health problems. The amount of mercury in Tuna's body tissue is much higher than other fish. It is known that the amount of mercury in Albacore tuna is normally distributed with mean 10.13 micrograms and standard deviation of 2.03 micrograms per ounce. A food company has a production line for canned albacore tuna. They regularly take random samples of 10 cans of tuna and test the amount of mercury. If the sample mean amount of mercury in tuna exceeds 12 micrograms per ounce, the production line will be stopped to find the source of contamination. Let u denote the actual population mean amount of mercury in tuna per ounce. The hypotheses for this testing situation are: 10.13 vs HA: µ > 10.13. What is the chance of making a Type I error using the decision rule given above? Give your answer to 4 decimal places. Your Answer:
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Introduction
Any assumption about the parameter or probability function is called Hypothesis testing. There is always some contention about the values of a parameter or the relationship between parameters. When parametric values are unknown we estimate them through sample values. If the sample value is exactly the same as per our contention there is no high in accepting it.
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