i dont no the answer
in order to calculate the mean of the sample's mean and also to calculate the standard deviation of the sample's
The sampling distribution of the sample proportion of adults with credit card debts greater than $2000 can be described using the population proportion, which is 36% (or 0.36). For a simple random sample of 200 adults, the mean of the sampling distribution will be equal to the population proportion (0.36), and the standard error can be calculated using the formula ( \sqrt{\frac{p(1-p)}{n}} ), where ( p ) is the population proportion and ( n ) is the sample size. In this case, the standard error would be approximately ( \sqrt{\frac{0.36(0.64)}{200}} ), leading to a normal distribution centered at 0.36, assuming the sample size is sufficiently large.
The sampling distribution of the sample mean (( \bar{x} )) will be approximately normally distributed if the sample size is sufficiently large, typically due to the Central Limit Theorem. This theorem states that regardless of the population's distribution, the sampling distribution of the sample mean will tend to be normal as the sample size increases, generally n ≥ 30 is considered adequate. However, if the population distribution is already normal, the sampling distribution of ( \bar{x} ) will be normally distributed for any sample size.
A set of probabilities over the sampling distribution of the mean.
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.
in order to calculate the mean of the sample's mean and also to calculate the standard deviation of the sample's
The sampling proportion may be used to scale up the results from a sample to that of the population. It is also used for designing stratified sampling.
it is the test one tail
i THINK IT IS .05
a) T or F The sampling distribution will be normal. Explain your answer. b) Find the mean and standard deviation of the sampling distribution. c) We pick one of our samples from the sampling distribution what is the probability that this sample has a mean that is greater than 109 ? Is this a usual or unusual event? these are the rest of the question.
A sample of 24 observations is taken from a population that has 150 elements. The sampling distribution of is
The sampling distribution of the sample mean (( \bar{x} )) will be approximately normally distributed if the sample size is sufficiently large, typically due to the Central Limit Theorem. This theorem states that regardless of the population's distribution, the sampling distribution of the sample mean will tend to be normal as the sample size increases, generally n ≥ 30 is considered adequate. However, if the population distribution is already normal, the sampling distribution of ( \bar{x} ) will be normally distributed for any sample size.
NO
In a stratified sample, the sampling proportion is the same for each stratum. In a random sample it should be but, due to randomness, need not be.
A set of probabilities over the sampling distribution of the mean.
A half.
Population distribution refers to the patterns that a population creates as they spread within an area. A sampling distribution is a representative, random sample of that population.