Mercury in Freshwater Fish According to an article from Huffington Post.com, some experts believe that 20% of all freshwater fish in the United States have such high levels of mercury that they are dangerous to eat. Suppose a fish market has 250 fish we consider randomly sampled from the population of edible freshwater fish. Use the Central Limit Theorem (and the Empirical Rule ) to find the approximate probability that the market will have a proportion of fish with dangerously high levels of mercury that is more than two standard errors above 0.20. You can use the Central Limit Theorem because the fish were randomly sampled; the population is more than 10 times 250; and n times p is 50, and n times (1 minus p ) is 200, and both are more than 10.
Mercury in Freshwater Fish According to an article from Huffington Post.com, some experts believe that 20% of all freshwater fish in the United States have such high levels of mercury that they are dangerous to eat. Suppose a fish market has 250 fish we consider randomly sampled from the population of edible freshwater fish. Use the Central Limit Theorem (and the Empirical Rule ) to find the approximate probability that the market will have a proportion of fish with dangerously high levels of mercury that is more than two standard errors above 0.20. You can use the Central Limit Theorem because the fish were randomly sampled; the population is more than 10 times 250; and n times p is 50, and n times (1 minus p ) is 200, and both are more than 10.
Mercury in Freshwater Fish According to an article from Huffington Post.com, some experts believe that 20% of all freshwater fish in the United States have such high levels of mercury that they are dangerous to eat. Suppose a fish market has 250 fish we consider randomly sampled from the population of edible freshwater fish.
Use the Central Limit Theorem (and the Empirical Rule) to find the approximate probability that the market will have a proportion of fish with dangerously high levels of mercury that is more than two standard errors above 0.20.
You can use the Central Limit Theorem because the fish were randomly sampled; the population is more than 10 times 250; and n times p is 50, and n times (1 minus p) is 200, and both are more than 10.
Features Features Normal distribution is characterized by two parameters, mean (µ) and standard deviation (σ). When graphed, the mean represents the center of the bell curve and the graph is perfectly symmetric about the center. The mean, median, and mode are all equal for a normal distribution. The standard deviation measures the data's spread from the center. The higher the standard deviation, the more the data is spread out and the flatter the bell curve looks. Variance is another commonly used measure of the spread of the distribution and is equal to the square of the standard deviation.
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