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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.
Is it possible for sample not normal to be from normal population?
Yes. You could have a biased sample. Its distribution would not necessarily match the distribution of the parent population.
What other types of data might follow a normal distribution?
Those data that were not mentioned in the original answer but which might follow a normal distribution. Since the question does not specify which ones were already listed, it is not possible to say which were "other".
What does it mean for a population to be normally distributed?
A Gaussian distribution is the "official" term for the Normal distribution. This is a probability density function, of the exponential family, defined by the two parameters, its mean and variance. A population is said to be normally distributed if the values that a variable of interest can take have a normal or Gaussian distribution within that population.
Does the central limit theorem states that as sample size increases the population distribution more closely approximates a normal distribution?
Yes.
Intelligence scores follow what kind of distribution?
The IQs of a large enough population can be modeled with 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.
What distribution does the F distribution approach as the sample size increases?
The F distribution is used to test whether two population variances are the same. The sampled populations must follow the normal distribution. Therefore, as the sample size increases, the F distribution approaches the normal distribution.
Why normal distribution is called normal distribution?
Most random variables are found to follow the probability distribution function All this means is that most things which can be measured quantitatively, like a population's height, the accuracy of a machine, effectiveness of a drug on fighting bacteria, etc. will occur with a probability that can be calculated according to this equation. Since most things follow this equation, this equation is considered to be the "normal" probability density. "Normal" events follow a "normal" probability distribution.
Is it possible for sample not normal to be from normal population?
Yes. You could have a biased sample. Its distribution would not necessarily match the distribution of the parent population.
When the population standard deviation is known the sampling distribution is a?
normal distribution
When the population standard deviation is known the sampling distribution is known as what?
normal distribution
When a population distribution is right skewed is the sampling distribution normal?
No, as you said it is right skewed.
What other types of data might follow a normal distribution?
Those data that were not mentioned in the original answer but which might follow a normal distribution. Since the question does not specify which ones were already listed, it is not possible to say which were "other".
What are features of a population which follows normal distribution?
Height, weight, IQ,
What does it mean for a population to be normally distributed?
A Gaussian distribution is the "official" term for the Normal distribution. This is a probability density function, of the exponential family, defined by the two parameters, its mean and variance. A population is said to be normally distributed if the values that a variable of interest can take have a normal or Gaussian distribution within that population.
When the population standard deviation is unknown the sampling distribution is equal to what?
The answer will depend on the underlying distribution for the variable. You may not simply assume that the distribution is normal.
