Context of this question is not clear because it is NOT a full question. However
when attempting to estimate an parameter such as µ using sample data when
the population standard deviation σ is unknown, we have to estimate the standard
deviation of the population using a stastitic called s where
_
Σ(x-x)²
s = ▬▬▬▬
n -1
_
and estimator for µ , in particular x
........................................._
has a standard deviation of s(x)= s/√n
and the statistic
_
x - hypothesized µ
T = ▬▬▬▬▬▬▬▬▬▬
s
has a student's T distribution with n-1 degrees of freedom
If n> 30 , then by the Central Limit Theorem, the T distribution
approaches the shape and form of the normal(gaussian) probability
distribution and the Z table may be used to find needed critical statistical
values for hypothesis tests , p-values, and interval estimates.
Sigma
From what ive gathered standard error is how relative to the population some data is, such as how relative an answer is to men or to women. The lower the standard error the more meaningful to the population the data is. Standard deviation is how different sets of data vary between each other, sort of like the mean. * * * * * Not true! Standard deviation is a property of the whole population or distribution. Standard error applies to a sample taken from the population and is an estimate for the standard deviation.
400
Information is not sufficient to find mean deviation and standard deviation.
In a normal distribution, approximately 95% of the population falls within 2 standard deviations of the mean. This is known as the 95% rule or the empirical rule. The empirical rule states that within one standard deviation of the mean, about 68% of the population falls, and within two standard deviations, about 95% of the population falls.
Yes.
The standard deviation of the population. the standard deviation of the population.
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.
normal distribution
normal distribution
When the standard deviation of a population is known, the sampling distribution of the sample mean will be normally distributed, regardless of the shape of the population distribution, due to the Central Limit Theorem. The mean of this sampling distribution will be equal to the population mean, while the standard deviation (known as the standard error) will be the population standard deviation divided by the square root of the sample size. This allows for the construction of confidence intervals and hypothesis testing using z-scores.
Yes
No.
The standard deviation if the data is a sample from a population is 7.7115; if it is the population the standard deviation is 7.0396.
the sample standard deviation
The standard deviation of height in the US population is approximately 3 inches.
If the population standard deviation is sigma, then the estimate for the sample standard error for a sample of size n, is s = sigma*sqrt[n/(n-1)]