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N is neither the sample or population mean. The letter N represents the population size while the small case letter n represents sample size. The symbol of sample mean is x̄ ,while the symbol for population mean is µ.
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If the sample mean is 10 the hypothesized population mean is 9 and the population standard deviation is 4 compute the test value needed for the z test?
n = sample sizen1 = sample 1 sizen2 = sample 2 size= sample meanμ0 = hypothesized population meanμ1 = population 1 meanμ2 = population 2 meanσ = population standard deviationσ2 = population variance
What is the difference between calculating the sample mean and the population mean?
You calculate the actual sample mean, and from that number, you then estimate the probable mean (or the range) of the population from which that sample was drawn.
When the population is normally distributed population standard deviation s is unknown and the sample size is n equals 15 the confidence interval for the population mean s is based on the?
The Z test.
Is the sampling of error larger when the sample mean is closer to the population mean?
No.
Is population mean always larger than sample mean?
Yes
Why is the sample mean an unbiased estimator of the population mean?
The sample mean is an unbiased estimator of the population mean because the average of all the possible sample means of size n is equal to the population mean.
When you draw a sample from a normal distribution what can you conclude about the sample distribution?
The answer depends on how the sample is selected. If it is a simple random sample, of size n, then it is distributed approximately normally with the same mean as the population mean.The answer depends on how the sample is selected. If it is a simple random sample, of size n, then it is distributed approximately normally with the same mean as the population mean.The answer depends on how the sample is selected. If it is a simple random sample, of size n, then it is distributed approximately normally with the same mean as the population mean.The answer depends on how the sample is selected. If it is a simple random sample, of size n, then it is distributed approximately normally with the same mean as the population mean.
What biased estimator will have a reduced bias based on an increased sample size?
The standard deviation. There are many, and it's easy to construct one. The mean of a sample from a normal population is an unbiased estimator of the population mean. Let me call the sample mean xbar. If the sample size is n then n * xbar / ( n + 1 ) is a biased estimator of the mean with the property that its bias becomes smaller as the sample size rises.
A population that consists of 500 observations has a mean of 40 and a standard deviation of 15 A sample of size 100 is taken at random from this population The standard error of the sample mean equa?
The formula for calculating the standard error (or some call it the standard deviation) is almost the same as for the population; except the denominator in the equation is n-1, not N (n = number in your sample, N = number in population). See the formulas in the related link.
What does n-1 indicate in a calculation for variance?
The n-1 indicates that the calculation is being expanded from a sample of a population to the entire population. Bessel's correction(the use of n − 1 instead of n in the formula) is where n is the number of observations in a sample: it corrects the bias in the estimation of the population variance, and some (but not all) of the bias in the estimation of the population standard deviation. That is, when estimating the population variance and standard deviation from a sample when the population mean is unknown, the sample variance is a biased estimator of the population variance, and systematically underestimates it.
If the sample mean is 10 the hypothesized population mean is 9 and the population standard deviation is 4 compute the test value needed for the z test?
n = sample sizen1 = sample 1 sizen2 = sample 2 size= sample meanμ0 = hypothesized population meanμ1 = population 1 meanμ2 = population 2 meanσ = population standard deviationσ2 = population variance
Why is the sample deviation divided by n-1in business statistics?
The purpose in computing the sample standard deviation is to estimate the amount of spread in the population from which the samples are drawn. Ideally, therefore, we would compute deviations from the mean of all the items in the population, rather than the deviations from the sample mean. However the population mean is generally unknown, so the sample mean would be used in place. It is a mathematical fact that the deviations around the sample mean tend to be a bit smaller than the deviations around the population mean and by dividing by n-1 rather than n provide the exactly the right amount of correction.
What is population mean in statistics?
The population mean is the mean value of the entire population. Contrast this with sample mean, which is the mean value of a sample of the population.
How do you find the degrees of freedom when using the t distribution to estimate or test the mean of a sample from a single population?
If the sample consisted of n observations, then the degrees of freedom is (n-1).
The closer the sample mean is to the population mean?
Your question is a bit difficult to understand. I will rephrase: In hypothesis testing, when the sample mean is close to the assumed mean of the population (null hypotheses), what does that tell you? Answer: For a given sample size n and an alpha value, the closer the calculated mean is to the assumed mean of the population, the higher chance that null hypothesis will not be rejected in favor of the alternative hypothesis.
N equals 36 with a population mean of 74 and a mean score of 79.4 with a standard deviation of 18?
Can someone help me find the answer for a sample n=36 with a population mean of of 76 and a mean of 79.4 with a standard deviation of 18?
Which sample would produce an expected value of 20?
n= 25 scores from a population with mean =20
