Math Problem Statement
A simple random sample of size nequals45 is obtained from a population that is skewed left with muequals42 and sigmaequals9. Does the population need to be normally distributed for the sampling distribution of x overbar to be approximately normally distributed? Why? What is the sampling distribution of x overbar? Question content area bottom Part 1 Does the population need to be normally distributed for the sampling distribution of x overbar to be approximately normally distributed? Why? A. Yes. The central limit theorem states that the sampling variability of nonnormal populations will increase as the sample size increases. B. No. The central limit theorem states that regardless of the shape of the underlying population, the sampling distribution of x overbar becomes approximately normal as the sample size, n, increases. C. 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 overbar become approximately normal as the sample size, n, increases. D. Yes. The central limit theorem states that only for underlying populations that are normal is the shape of the sampling distribution of x overbar normal, regardless of the sample size, n.
Solution
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Math Problem Analysis
Mathematical Concepts
Sampling Distribution
Central Limit Theorem
Formulas
Standard Error formula: \( \sigma_{\overline{x}} = \frac{\sigma}{\sqrt{n}} \)
Theorems
Central Limit Theorem
Suitable Grade Level
Undergraduate
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