Suppose that x has a distribution with μ=9 and o=4 and it is not normally distributed (bell shaped). If you take many samples of size n= 53, will the sampling distribution of samplemeans be approximately normally distributed? (Hint: Central limit Theorem)O NoO Not possible to knowO Yes, because sample size is large enough.

According to tje CLT , for larger n(>30) , sampling distribution of sample mean approximately…

Answered: Suppose that x has a distribution with…

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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If a sample size is 84, comes from a population that is normally distributed, and the data is ordinal ranging from 135 to 154, can one say that the data satisfies one of the distribution assumptions for a t-test, namely that the data is ordinal?
Plzz explain.
just subparts 4 and 5 please thank you!
As sample size decreases, the shape of the t distribution gets. the t distribution gets wider, more narrow but as the sample size increases the shape of skewed, leptokuric platokuric, skewed more narrow, wider
What would happen to the sampling distribution if you increased the size of the samples taken from the population? O The center of the sampling distribution will get further away from the parameter O The spread of the sampling distribution will get larger O The center of the sampling distribution will get closer to the parameter O The spread of the sampling distribution will get smaller
please state the approximate distribution of the sample mean
A simple random sample of size n=10 is obtained from a population that is normally distributed with a mean of 40 and a standard deviation of 3. Is the sampling distribution normally distributed? Why? Yes, the sampling distribution is normally distributed because the population is normally distributed. Yes, the sampling distribution is normally distributed because the population mean is greater than 30. No, the sampling distribution is not normally distributed because the population is not normally distributed. No, the sampling distribution is not normally distributed because the sample size is less than 30.
5. You form a distribution of the means of all samples of size 9 drawn from an infinite population that is skewed to the left. The population from which the samples are drawn has a mean of 50 and a standard deviation of 12. Which one of the following statements is true of this sampling distribution? (a) µz = 50, ơ, = 12 , the sampling distribution is skewed somewhat to the left. (b) u, = 50, ơ, = 4, the sampling distribution is skewed somewhat to the left. (c) u, = 50, o, =12 , the sampling distribution is approximately normal. (d) µ, = 50, o, = 4 , the sampling distribution is approximately normal.
According to the central limit theorem (CLT), no matter which distribution the data is collected from, as long as some conditions are met, we can always expect the sample average to behave like a normal random variable, i.e., the sampling distribution can be approximated by a normal distribution. Which is NOT a CLT condition for sample mean? Group of answer choices The original distribution of the data is Normal. A random sample with sample size is larger than 30 with no extreme outlier A random sample with sample size is less than 30 with no outliers. The sample size has to be larger than 10.
Last Question
A simple random sample of size n =66, is obtained from a population that is skewed left with =33 and =3. . Does the population need to be normally distributed for the sampling distribution of x to be approximately normally distributed? Why? What is the sampling distribution of x? Does the population need to be normally distributed for the sampling distribution of x 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) 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. (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, become approximately normal as the sample size, n, increases. (D) No. The central limit theorem…
For all samples of the same size n with n < 30 the sampling distribution of x can be approximated by a normal distribution with mean if the original population multiple choice answers: Normal Uniform U-shaped Skewed All of the above

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