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1. A. The Central Limit Theorem is used to approximate the distribution of the sample means over the population means. B. If the sample size n, where n is sufficiently large is drawn from any population with mean u and a standard deviation o, then the sampling distribution of sample means approximates the normal distribution. C. Whenever the population is not normally distributed, or if we do not know of its distribution, the Central Limit Theorem allows us to conclude that the distribution of sample means will be normal if the sample size is sufficiently large.

1. A. The Central Limit Theorem is used to approximate the distribution of the sample means over the population means. B. If the sample size n, where n is sufficiently large is drawn from any population with mean u and a standard deviation o, then the sampling distribution of sample means approximates the normal distribution. C. Whenever the population is not normally distributed, or if we do not know of its distribution, the Central Limit Theorem allows us to conclude that the distribution of sample means will be normal if the sample size is sufficiently large.

MATLAB: An Introduction with Applications
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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1. Which of the following does NOT illustrate the Central Limit Theorem? A. The Central Limit Theorem is used to approximate the distribution of the sample means over the population means. B. If the sample size n, where n is sufficiently large is drawn from any population with mean u and a standard deviation o, then the sampling distribution of sample means approximates the normal distribution. C. Whenever the population is not normally distributed, or if we do not know of its distribution, the Central Limit Theorem allows us to conclude that the distribution of sample means will be normal if the sample size is sufficiently large. D. Given a random variable X with meanu and variance o2, then regardless of whether the population distribution of X is normally distributed or not, the shape of the distribution of the sample means taken from the population approaches a normal distribution. 2. The Central Limit Theorem states that: A. the sample size is large. B. all possible samples are selected. C. the standard error of the sampling distribution is small. D. the standard deviation is sufficiently large than the normal. 3. The Central Limit Theorem states that the mean of the sampling distribution of the sample mean is A. larger than the population mean. B. exactly equal to the population mean. C. equal to the population mean divided by the square root of the sample size. D. close to the population means if the sample size is large. 4. Which of the following descriptions about Central Limit Theorem is NOT essential? A. The larger the sample, the better approximation will be. B. The smaller the sample, the bigger the approximation will be. C. When the original variable is normally distributed, the distribution of the Las of ng
Transcribed Image Text:1. Which of the following does NOT illustrate the Central Limit Theorem? A. The Central Limit Theorem is used to approximate the distribution of the sample means over the population means. B. If the sample size n, where n is sufficiently large is drawn from any population with mean u and a standard deviation o, then the sampling distribution of sample means approximates the normal distribution. C. Whenever the population is not normally distributed, or if we do not know of its distribution, the Central Limit Theorem allows us to conclude that the distribution of sample means will be normal if the sample size is sufficiently large. D. Given a random variable X with meanu and variance o2, then regardless of whether the population distribution of X is normally distributed or not, the shape of the distribution of the sample means taken from the population approaches a normal distribution. 2. The Central Limit Theorem states that: A. the sample size is large. B. all possible samples are selected. C. the standard error of the sampling distribution is small. D. the standard deviation is sufficiently large than the normal. 3. The Central Limit Theorem states that the mean of the sampling distribution of the sample mean is A. larger than the population mean. B. exactly equal to the population mean. C. equal to the population mean divided by the square root of the sample size. D. close to the population means if the sample size is large. 4. Which of the following descriptions about Central Limit Theorem is NOT essential? A. The larger the sample, the better approximation will be. B. The smaller the sample, the bigger the approximation will be. C. When the original variable is normally distributed, the distribution of the Las of ng
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