QUESTION 1 The Central Limit Theorem tells us that: the shape of all sampling distributions of sample
Question:
QUESTION 1
The Central Limit Theorem tells us that:
the shape of all sampling distributions of sample means are normally distributed. | ||
the mean of the distribution of sample means is less than the mean of the parent population. | ||
the standard deviation of the distribution of sample means is the same as the standard deviation of the population. | ||
all of the above are true. | ||
none of the above are true. |
1 points
QUESTION 2
The Central Limit Theorem
says that s(x) approaches sigma(x) as sample size increases. | ||
says that any sampling distribution of a sample mean will be approximately normal regardless of the shape of the population distribution. | ||
says that both a and b will occur. | ||
says that x-bar approaches mu as sample size increases. |
1 points
QUESTION 3
The Central Limit Theorem is of most value when we sample from a normal distribution.
True | ||
False |
1 points
QUESTION 4
As the sample size increases, the distribution of the sample mean approaches a normal distribution.
True | ||
False |
1 points
QUESTION 5
The Central Limit Theorem applies to the case of sampling from a normal distribution as well as other cases.
True | ||
False |
1 points
QUESTION 6
According to the Central Limit Theorem, the shape of the sampling distribution of x-bar (given that n = 30) will be normal, whether or not the shape of the population is normal.
True | ||
False |