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When a large number of samples are drawn from a negatively skewed population the distribution of the sample means?
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∙ 11y ago
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Wiki User
∙ 11y ago
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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
Is the distribution of sample means always a normal distribution If not why?
It need not be if: the number of samples is small; the elements within each sample, and the samples themselves are not selected independently.
What does age distribution of population mean?
The age distribution of a population is, the number of individuals of each age in the population
What does the age distribution of a population mean?
The age distribution of a population is, the number of individuals of each age in the population.
What does age distribution of a population mean?
The age distribution of a population is, the number of individuals of each age in the population.
The distribution of sample means is not always a normal distribution Under what circumstances will the distribution of sample means not be normal?
The distribution of sample means will not be normal if the number of samples does not reach 30.
Central Limit Theorem holds that the mean of a sampling distribution taken from a single population approaches the actual population mean as the number of samples increases Is that true?
Yes, as long as the amount of sampled variables, n >=30.
Explain how you could create a distribution of means by taking a large number of samples of four individuals each?
As the sample size increases, and the number of samples taken increases, the distribution of the means will tend to a normal distribution. This is the Central Limit Theorem (CLT). Try out the applet and you will have a better understanding of the CLT.
The standard error of the mean is the standard deviation of the sampling distribution of the sample mean?
a large number of samples of size 50 were selected at random from a normal population with mean and variance.The mean and standard error of the sampling distribution of the sample mean were obtain 2500 and 4 respectivly.Find the mean and varince of the population?
Will sample means be nearly normally distributed if the distribution of the measurement among the individuals are not from a normal distribution?
Yes, as you keep drawing more and more samples and the number of samples become sufficiently large. This is known as the Central Limit Theorem.
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 characteristic of a normal distribution?
The Normal (or Gaussian) distribution is a symmetrical probability function whose shape is determined by two values: the mean and variance (or standard deviation).According to the law of large numbers, if you take repeated independent samples from any distribution, the means of those samples are distributed approximately normally. The greater the size of each sample, or the greater the number of samples, the more closely the results will match the normal distribution. This characteristic makes the Normal distribution central to statistical theory.
