0
Add your answer:
Earn +20 pts
What is the distribution of sample means constructed by sampling 5 items from a population of 15?
the standard error will be 1
The Central Limit Theorem is important in statistics because?
According to the central limit theorem, as the sample size gets larger, the sampling distribution becomes closer to the Gaussian (Normal) regardless of the distribution of the original population. Equivalently, the sampling distribution of the means of a number of samples also becomes closer to the Gaussian distribution. This is the justification for using the Gaussian distribution for statistical procedures such as estimation and hypothesis testing.
Suppose that a population that a population has mean equals 64 and standard deviation equals 18. A sample of size 36 is selected. What is the mean of the sampling distribution of means?
64.
What does the Central Limit Theorem say about the traditional sample size that separates a large sample size from a small sample size?
The Central Limit Theorem states that the sampling distribution of the sample means approaches a normal distribution as the sample size gets larger — no matter what the shape of the population distribution. This fact holds especially true for sample sizes over 30.
The sum of the differences between sample observations and sample means is what?
always zero
The usual sampling distributionof the difference between means is a what?
There is no such thing as "the usual sampling distribution". Different distributions of the original random variables will give different distributions for the difference between their means.There is no such thing as "the usual sampling distribution". Different distributions of the original random variables will give different distributions for the difference between their means.There is no such thing as "the usual sampling distribution". Different distributions of the original random variables will give different distributions for the difference between their means.There is no such thing as "the usual sampling distribution". Different distributions of the original random variables will give different distributions for the difference between their means.
The distribution of sample means consists of?
A set of probabilities over the sampling distribution of the mean.
When the population standard deviation is not known the sampling distribution is a?
If the samples are drawn frm a normal population, when the population standard deviation is unknown and estimated by the sample standard deviation, the sampling distribution of the sample means follow a t-distribution.
What name do you give to the standard deviation of the sampling distribution of sample means?
the central limit theorem
What is the distribution of sample means constructed by sampling 5 items from a population of 15?
the standard error will be 1
The Central Limit Theorem is important in statistics because?
According to the central limit theorem, as the sample size gets larger, the sampling distribution becomes closer to the Gaussian (Normal) regardless of the distribution of the original population. Equivalently, the sampling distribution of the means of a number of samples also becomes closer to the Gaussian distribution. This is the justification for using the Gaussian distribution for statistical procedures such as estimation and hypothesis testing.
What is the number of samples for a distribution of sample means constructed by sampling 5 items from a population of 15?
the sampe mean cannot be comoputed
If repeating an experiment many times with different samples what values for the sample means would be possible?
The answer depends on the population and is described by the sampling distribution of the mean.
What is the difference between sample and sampling?
sample is a noun. sampling is a verb. Statistically speaking, a sample is where we gather and examine part of a population. A sampling is where we take the means of samples in order to gather info about the whole...
What is the difference between population and sample distribution?
Sampling distribution is the probability distribution of a given sample statistic. For example, the sample mean. We could take many samples of size k and look at the mean of each of those. The means would form a distribution and that distribution has a mean, a variance and standard deviation. Now the population only has one mean, so we can't do this. Population distribution can refer to how some quality of the population is distributed among the population.
What is the difference between the Monte Carlo method and Latin Hypercube Sampling?
actually montecarlo is based on random selection (of cours randamness is expected to be random means to cover tjhe whole interval so the more the better )along the CDF(cumilative distribution function ) to extract the input that expected to keep the original distribution to some degree. in latin hypercube this extraction of input has been made uniform along the CDF so we can ensure the recreation of the original distribution in the extracted sample of inputs. hypercube is enhancement of montecarlo sampling . and it is much better in low density sampling means low number of iteration . high number of iterartion both methodes are good they tend to be the same .
Suppose that a population that a population has mean equals 64 and standard deviation equals 18. A sample of size 36 is selected. What is the mean of the sampling distribution of means?
64.
