Answer: The standard deviation of the sampling distribution of M is equal to the standard deviation of the population divided by the square root of the sample size.
You can assume that the sampling distribution of M is normally distributed for any sample size.
Step-by-step explanation:
- According to the central limit theorem , if we have a population with mean
and standard deviation
, then if we take a sufficiently large random samples from the population with replacement , the distribution of the sample means will be approximately normally distributed. - When population is normally distributed , then the mean of the sampling distribution = Population mean

- Standard deviation of the sampling distribution =
, where
= standard deviation of the population , n= sample size.
So, the correct statements are:
- You can assume that the sampling distribution of M is normally distributed for any sample size.
- The standard deviation of the sampling distribution of M is equal to the standard deviation of the population divided by the square root of the sample size.
Answer:
32
Step-by-step explanation:
P=2(l+w)
so 2(11+5)=P
2(11+5)=32
Replace x with -2
2 * 1/9= 2/9
The answer is
X^2+y^2+Dx+Ey+F=0