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A simple random sample of size n= 40 is obtained from a population that is skewed left with u = 61 and o = 4

A simple random sample of size n= 40 is obtained from a population that is skewed left with u = 61 and o = 4

Calculus For The Life Sciences
Calculus For The Life Sciences
2nd Edition
ISBN:9780321964038
Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Chapter13: Probability And Calculus
Section13.3: Special Probability Density Functions
Problem 53E
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A simple random sample of size n= 40 is obtained from a population that is skewed left with u = 61 and o = 4.
A simple random sample of size n = 40 is obtained from a population that is skewed left with μ = 61 and o=4. Does the population need to be normally distributed for the sampling distribution of x to be approxima X? Does the population need to be normally distributed for the sampling distribution of x to be approximately normally distributed? Why? OA. Yes. The central limit theorem states that only for underlying populations that are normal is the shape of the sampling distribution of x normal, regardless of the sample size, n. OB. No. The central limit theorem states that only if the shape of the underlying population is normal or uniform does the sampling distribution of x become approximately normal as the sample size, n, increase OC. No. The central limit theorem states that regardless of the shape of the underlying population, the sampling distribution of x becomes approximately normal as the sample size, n, increases. O D. Yes. The central limit theorem states that the sampling variability of nonnormal populations will increase as the sample size increases. What is the sampling distribution of x? Select the correct choice below and fill in the answer boxes within your choice. (Type integers or decimals rounded to three decimal places as needed.). OA. The sampling distribution of x is skewed left with μ-= and o- = and ox= OB. The shape of the sampling distribution of x is unknown with u OC. The sampling distribution of x is approximately normal with μ = 61.000 and o= 0.4 and ox= OD. The sampling distribution of x is uniform with μ = H H |0| √I Vi 1. (0,0) More
Transcribed Image Text:A simple random sample of size n = 40 is obtained from a population that is skewed left with μ = 61 and o=4. Does the population need to be normally distributed for the sampling distribution of x to be approxima X? Does the population need to be normally distributed for the sampling distribution of x to be approximately normally distributed? Why? OA. Yes. The central limit theorem states that only for underlying populations that are normal is the shape of the sampling distribution of x normal, regardless of the sample size, n. OB. No. The central limit theorem states that only if the shape of the underlying population is normal or uniform does the sampling distribution of x become approximately normal as the sample size, n, increase OC. No. The central limit theorem states that regardless of the shape of the underlying population, the sampling distribution of x becomes approximately normal as the sample size, n, increases. O D. Yes. The central limit theorem states that the sampling variability of nonnormal populations will increase as the sample size increases. What is the sampling distribution of x? Select the correct choice below and fill in the answer boxes within your choice. (Type integers or decimals rounded to three decimal places as needed.). OA. The sampling distribution of x is skewed left with μ-= and o- = and ox= OB. The shape of the sampling distribution of x is unknown with u OC. The sampling distribution of x is approximately normal with μ = 61.000 and o= 0.4 and ox= OD. The sampling distribution of x is uniform with μ = H H |0| √I Vi 1. (0,0) More
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Calculus For The Life Sciences
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ISBN:
9780321964038
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