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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.
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When the population standard deviation is known the sampling distribution is known as what?
normal distribution
When the population standard deviation is known the sampling distribution is a?
normal distribution
When the population standard deviation is not know the sampling distribution is a?
When the population standard deviation is not known, the sampling distribution of the sample mean is typically modeled using the t-distribution instead of the normal distribution. This is because the t-distribution accounts for the additional uncertainty introduced by estimating the population standard deviation from the sample. As the sample size increases, the t-distribution approaches the normal distribution, making it more appropriate for larger samples.
What is the standard deviation of the distribution of sample means from the sampling distribution?
The standard deviation of the distribution of sample means, also known as the standard error, is calculated by dividing the population standard deviation (σ) by the square root of the sample size (n). This is expressed as ( \sigma_{\bar{x}} = \frac{\sigma}{\sqrt{n}} ). The standard error decreases as the sample size increases, indicating that larger samples provide more accurate estimates of the population mean. This concept is fundamental in inferential statistics for making predictions about the population based on sample data.
When the population standard deviation is known the sample distribution is a?
When the population standard deviation is known, the sample distribution is a normal distribution if the sample size is sufficiently large, typically due to the Central Limit Theorem. If the sample size is small and the population from which the sample is drawn is normally distributed, the sample distribution will also be normal. In such cases, statistical inference can be performed using z-scores.
When the population standard deviation is known the sampling distribution is known as what?
normal distribution
When the population standard deviation is known the sampling distribution is a?
normal distribution
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.
When the population standard deviation is not know the sampling distribution is a?
When the population standard deviation is not known, the sampling distribution of the sample mean is typically modeled using the t-distribution instead of the normal distribution. This is because the t-distribution accounts for the additional uncertainty introduced by estimating the population standard deviation from the sample. As the sample size increases, the t-distribution approaches the normal distribution, making it more appropriate for larger samples.
The standard deviation of the distribution of means is also known as the population standard deviation?
Yes.
What is the standard deviation of the distribution of sample means from the sampling distribution?
The standard deviation of the distribution of sample means, also known as the standard error, is calculated by dividing the population standard deviation (σ) by the square root of the sample size (n). This is expressed as ( \sigma_{\bar{x}} = \frac{\sigma}{\sqrt{n}} ). The standard error decreases as the sample size increases, indicating that larger samples provide more accurate estimates of the population mean. This concept is fundamental in inferential statistics for making predictions about the population based on sample data.
When to use z or t-distribution?
If the sample size is large (>30) or the population standard deviation is known, we use the z-distribution.If the sample sie is small and the population standard deviation is unknown, we use the t-distribution
Is it possible that in standard normal distribution standard deviation known and mean unknown?
it is possible to distribute standard deviation and mean but you dont have to understand how the mouse runs up the clock hicorky dickory dock.
What are importance of mean and standard deviation in the use of normal distribution?
For data sets having a normal distribution, the following properties depend on the mean and the standard deviation. This is known as the Empirical rule. About 68% of all values fall within 1 standard deviation of the mean About 95% of all values fall within 2 standard deviation of the mean About 99.7% of all values fall within 3 standard deviation of the mean. So given any value and given the mean and standard deviation, one can say right away where that value is compared to 60, 95 and 99 percent of the other values. The mean of the any distribution is a measure of centrality, but in case of the normal distribution, it is equal to the mode and median of the distribtion. The standard deviation is a measure of data dispersion or variability. In the case of the normal distribution, the mean and the standard deviation are the two parameters of the distribution, therefore they completely define the distribution. See: http://en.wikipedia.org/wiki/Normal_distribution
When the population standard deviation is known the sample distribution is a?
When the population standard deviation is known, the sample distribution is a normal distribution if the sample size is sufficiently large, typically due to the Central Limit Theorem. If the sample size is small and the population from which the sample is drawn is normally distributed, the sample distribution will also be normal. In such cases, statistical inference can be performed using z-scores.
What is the mean of the sample means that is normally distributed with a mean of 10 standard deviation of 2 and a sample size f 25?
The mean of the sample means, also known as the expected value of the sampling distribution of the sample mean, is equal to the population mean. In this case, since the population mean is 10, the mean of the sample means is also 10. The standard deviation of the sample means, or the standard error, would be the population standard deviation divided by the square root of the sample size, which is ( \frac{2}{\sqrt{25}} = 0.4 ).
When do you know when to use t-distribution opposed to the z-distribution?
z- statistics is applied under two conditions: 1. when the population standard deviation is known. 2. when the sample size is large. In the absence of the parameter sigma when we use its estimate s, the distribution of z remains no longer normal but changes to t distribution. this modification depends on the degrees of freedom available for the estimation of sigma or standard deviation. hope this will help u.... mona upreti.. :)
