Answer the following 1. Which of the following does NOT illustrate the Central Limit Theorem?A. The Central Limit Theorem is used to approximate the distribution of thesample means over the population means.B. If the sample size n, where n is sufficiently large is drawn from any populationwith mean u and a standard deviation o, then the sampling distribution ofsample means approximates the normal distribution.C. Whenever the population is not normally distributed, or if we do not know ofits distribution, the Central Limit Theorem allows us to conclude that thedistribution of sample means will be normal if the sample size is sufficientlylarge.D. Given a random variable X with meanu and variance o2, then regardless ofwhether the population distribution of X is normally distributed or not, theshape of the distribution of the sample means taken from the populationapproaches 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 ofthe sample mean isA. 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 theLasofng

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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

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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