aianjie

 

5、A population has a mean of 20,000 and a standard deviation of 1,000. Samples of size n = 2,500 are taken from this population. What is the standard error of the sample mean?

A) 0.04.

B) 20.00.

C) 400.00.

D) 8.00.

aianjie

 

The correct answer is B

The standard error of the sample mean is estimated by dividing the standard deviation of the sample by the square root of the sample size: sx = s / n1/2 = 1000 / (2500)1/2 = 1000 / 50 = 20.

aianjie

 

6、A population’s mean is 30 and the mean of a sample of size 100 is 28.5. The variance of the sample is 25. What is the standard error of the sample mean?

A) 0.05.

B) 0.50.

C) 0.25.

D) 2.50.

aianjie

 

The correct answer is B

 

 

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[2008] Topic 5: Some Important Probability Distributions 相关习题

2009-6-25 12:44

aianjie

 

7、The population mean for equity returns is 14 percent with a standard deviation of 2 percent. If a random sample of 49 returns is drawn, what is the standard error of the sample mean?

A) 0.29%.

B) 0.04%.

C) 2.00%.

D) 7.00%. 

aianjie

 

The correct answer is A

 

 

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[2008] Topic 5: Some Important Probability Distributions 相关习题

2009-6-25 12:46

aianjie

 

8、If n is large and the population standard deviation is unknown, the standard error of the sampling distribution of the sample mean is equal to the:

A) population standard deviation divided by the sample size. 

B) sample standard deviation divided by the square root of the sample size.

C) population standard deviation multiplied by the sample size.

D) sample standard deviation divided by the sample size. 

aianjie

 

The correct answer is

The formula for the standard error when the population standard deviation is unknown is:

 

 

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[2008] Topic 5: Some Important Probability Distributions 相关习题

2009-6-25 12:47

aianjie

 

AIM 3: Define the central limit theorem.

1、The central limit theorem concerns the sampling distribution of the:

A) sample mean.

B) population mean.

C) sample standard deviation.

D) population standard deviation.

aianjie

 

The correct answer is A

The central limit theorem tells us that for a population with a mean m and a finite variance σ2, the sampling distribution of the sample means of all possible samples of size n will approach a normal distribution with a mean equal to m and a variance equal to σ2 / n as n gets large.