Assertion: sample's mean | /r/HasProperty | equal
Facets:
value: to the population
type: OTHER
value: always
type: DEGREE
value: to the median
type: OTHER
value: to μ
type: OTHER
value: to the degrees of freedom
type: OTHER
value: to the parameter
type: OTHER
value: ( µ )
type: OTHER
value: regardless of sample size
type: OTHER
value: to 200
type: OTHER
value: to 0.167
type: OTHER
value: to v1
type: OTHER
value: exactly
type: DEGREE
value: approximately
type: DEGREE
value: theoretically
type: DEGREE
Source sentences:
text: While the mean of a sampling distribution is equal to the mean of the population, the standard error depends on the standard deviation of the population, the size of the population and the size of the sample.
text: The mean of the sampling distribution is the mean of the sample means, and is theoretically equal to the population
text: However, the study can be said to be unbiased if the mean of its sampling distribution is equal to the true value of the parameter being estimated.
text: The mean of the distribution of differences between sample means is equal to the difference between population means.
text: Learning Objectives Discover that the mean of the distribution of differences between sample means is equal to the difference between population means Key Takeaways Key Points Statistical analysis are very often concerned with the difference between means.
text: μ 1 − 2, which says that the mean of the distribution of differences between sample means is equal to the difference between population means.
text: The mean of is equal to , i.e.
text: As you might expect, the mean of the sampling distribution of the difference between means is: which says that the mean of the distribution of differences between sample means is equal to the difference between population means.
text: The mean of a sampling distribution of the means (called mu x bar) is always equal to the mean of the parent population.
text: The mean of the SDM is equal to the mean of
text: The mean of the distribution is equal to v1 / ( v2 - 2 ).
text: Said another way, CLT is a statistical theory stating that given a sufficiently large sample size from a population with a finite level of variance, the mean of all samples from the same population will be approximately equal to the mean of the population.
text: 1) the mean of a sampling distribution of means will always be equal to the population mean.
text: Or you might be able to say that, if the means of two populations are equal, the difference between sample means should fall between certain values.
text: The mean of the proportion of sixes in the 20 rolls, X/20, is equal to p = 1/6 = 0.167, and the variance of the proportion is equal to (1/6*5/6)/20 = 0.007.
text: The mean of the distribution is equal to 200*0.4 = 80, and the variance is equal to 200*0.4*0.6 = 48.
text: A statistic used to estimate a parameter is unbiased if the mean of its sampling distribution is exactly equal to the true value of the parameter being estimated.
text: We will carry out tests of the null hypothesis that the means of the populations from which the two samples were taken are equal.
text: A statistic used to estimate a parameter is an ... if the mean of its ... is equal to the true value of the parameter being measured
text: The mean of the sampling distribution of the sample mean is equal to the mean of the underlying population, and the standard deviation of the sampling distribution of the sample mean is standard deviation/√(n), regardless of sample size.
text: The means of samples of size n, randomly drawn from a normally distributed source population, belong to a normally distributed sampling distribution whose overall mean is equal to the mean of the source population and whose standard deviation ("standard error") is equal to the standard deviation of the source population divided by the square root of n. To calculate the standard error of any particular sampling distribution of sample means, enter the mean and standard deviation (sd) of the source population, along with the value of n, and then click the "Calculate" button.
text: The means of samples of size n, randomly drawn from a normally distributed source population, belong to a normally distributed sampling distribution whose overall mean is equal to the mean of the source population and whose standard deviation ("standard error") is equal to the standard deviation of the source population divided by the square root of n. To calculate the standard error of any particular sampling distribution of sample means, enter the mean and standard deviation (sd) of the source population, along with the value of n, and then click the "Calculate" button.
text: With " infinite " numbers of successive random samples, the sampling distributions all have a normal distribution with a mean that is equal to the population mean ( µ ).
text: With " infinite " numbers of successive random samples, the mean of the sampling distribution is equal to the population mean ( µ ).
text: Because the mean of the sampling distribution of (p hat) is always equal to the parameter
text: The mean of the chi-square distribution is equal to the degrees of freedom, i.e. E(χ2)
text: The mean of the sampling distribution of the sample mean is equal to μ.
text: If the distribution is symmetric, then the mean is equal to the median, and the distribution has zero skewness.
text: Note, however, that the converse is not true in general, i.e. zero skewness does not imply that the mean is equal to the median.