8. [Chapter 15] Find the sampling distribution of the mean i for n=2 from the population (1, 3, 5, 7, 9}, with µy = 5 and o, * 2.83. Provide a stemplot of the sampling distribution of X; report ug to ± 0.01 and ag to ±0.001. [Hint: There are (:) = 10 samples of size n=2 possible from the given population.] Sample (n=2) Stemplot of & He = obs 1 obs 2 2 4 5 7 leaf unit = 9. (Chapter 15] Explain clearly how the sampling distribution graphed above demonstrates that i is an unbiased estimator of the population mean u. 10. [Chapter 15] Explain clearly how the sampling distribution graphed above demonstrates the Law of Large Numbers. 11. [Chapter 15] Suppose that a random variable in the population is distributed X~(µy = 100, ox = 96), and that the distribution of X is heavily skewed to the right. Describe the sampling distribution of the mean X given samples of size n= 64. Clearly explain the rationale behind your answer. 6.

8. [Chapter 15] Find the sampling distribution of the mean i for n=2 from the population (1, 3, 5, 7, 9}, with µy = 5 and o, * 2.83. Provide a stemplot of the sampling distribution of X; report ug to ± 0.01 and ag to ±0.001. [Hint: There are (:) = 10 samples of size n=2 possible from the given population.] Sample (n=2) Stemplot of & He = obs 1 obs 2 2 4 5 7 leaf unit = 9. (Chapter 15] Explain clearly how the sampling distribution graphed above demonstrates that i is an unbiased estimator of the population mean u. 10. [Chapter 15] Explain clearly how the sampling distribution graphed above demonstrates the Law of Large Numbers. 11. [Chapter 15] Suppose that a random variable in the population is distributed X~(µy = 100, ox = 96), and that the distribution of X is heavily skewed to the right. Describe the sampling distribution of the mean X given samples of size n= 64. Clearly explain the rationale behind your answer. 6.

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Description: question attached below They go together but labled different numbers but it is all part of question 8 8. [Chapter 15] Find the sampling distribution of the mean i for n=2 from the population (1, 3, 5, 7, 9}, with µy = 5 ando, * 2.83. Provide a stemp

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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Suppose we take a sample of 2,500 blood donors from a population for which 50% (0.5) have type O+ blood. (a) Into what range of possible values should the sample proportion fall 95% of the time, according to the Empirical Rule? to (b) If the sample included only 625 donors instead of 2,500, would the range of possible sample proportions be wider, more narrow, or the same as with a sample of 2,500 donors? Explain your answer, and explain why it makes intuitive sense. The range would be      with 625 donors compared to a sample of 2,500 donors since the standard deviation of the sampling distribution would be      . This makes intuitive sense because if fewer donors are included in the sample, the     proportion will be      reliable as an estimate of the     proportion.
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2. Consider a queuing system with a Poisson input and exponential service times. Suppose there is one server and the expected service time is exactly one minute. Compare Ls for the cases where the mean arrival rate is 0.5, 0.9 and 0.99 customers per minute, respectively. Do the same for Lq, Ws, Wq and P(W>5) or the probability of waiting time in the system.
Suppose x has a distribution with μ = 78 and σ = 8. (a) If random samples of size n = 16 are selected, can we say anything about the x distribution of sample means? Yes, the x distribution is normal with mean μ x = 78 and σ x = 2. Yes, the x distribution is normal with mean μ x = 78 and σ x = 0.5. Yes, the x distribution is normal with mean μ x = 78 and σ x = 8. No, the sample size is too small. (b) If the original x distribution is normal, can we say anything about the x distribution of random samples of size 16? Yes, the x distribution is normal with mean μ x = 78 and σ x = 2. Yes, the x distribution is normal with mean μ x = 78 and σ x = 0.5. Yes, the x distribution is normal with mean μ x = 78 and σ x = 8. No, the sample size is too small. Find P(74 ≤ x ≤ 79). (Round your answer to four decimal places.)
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1.Random sample of size 36 were drawn with replacement from a popolation with mean and variance 0.9, and in each case the sample mean was calculated. Find the variance of X. 2.Random sample of size 9 were drawn with replacement from a popolation with mean and variance 4.2, and in each case the sample mean was calculated. Find the variance of X. 3. Random samples of size 10 are drawn with replacement from a population with mean 4 and variance 6. Let X be the sample mean. Find E(X) and Variance(X).
The desired percentage of SiO2 in a certain type of aluminous cement is 5.5. To test whether the true average percentage is 5.5 for a particular production facility, 16 independently obtained samples are analyzed. Suppose that the percentage of SiO2 in a sample is normally distributed with σ = .3. Assume that the sample mean is 5.25. (a) Does this indicate that the true average percentage differs from 5.5? Carry out a test at α = .01. (b) If the true average percentage is µ = 5.6, what is the probability of type II error?
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