0
Show that in simple random sampling the sample variance is an unbiased estimator of population variance?
Wiki User
∙ 10y ago
It is a biased estimator. S.R.S leads to a biased sample variance but i.i.d random sampling leads to a unbiased sample variance.
Wiki User
∙ 10y ago
Add your answer:
Earn +20 pts
Standard deviation considered a crude measure of variance?
No. Well not exactly. The square of the standard deviation of a sample, when squared (s2) is an unbiased estimate of the variance of the population. I would not call it crude, but just an estimate. An estimate is an approximate value of the parameter of the population you would like to know (estimand) which in this case is the variance.
(b)For a set of numbers 1,4,7. Find population mean and Variance. Draw up frequency distributions of means of all possible samples of size 3 when sampling is carried out with replacement. Find mean and variance of distribution?
lowest
Is sample standard deviation the same as standard deviation?
No, the standard deviation is a measure of the entire population. The sample standard deviation is an unbiased estimator of the population. It is different in notation and is written as 's' as opposed to the greek letter sigma. Mathematically the difference is a factor of n/(n-1) in the variance of the sample. As you can see the value is greater than 1 so it will increase the value you get for your sample mean. Essentially, this covers for the fact that you are unlikely to obtain the full population variation when you sample.
How is it that a random samples gives a fairly accurate representation of public opinion?
The main point here is that the Sample Mean can be used to estimate the Population Mean. What I mean by that is that on average, the Sample Mean is a good estimator of the Population Mean. There are two reasons for this, the first is that the Bias of the estimator, in this case the Sample Mean, is zero. A Bias other than zero overestimates or underestimates the Population Mean depending on its value. Bias = Expected value of estimator - mean. This can be expressed as EX(pheta) - mu (pheta) As the Sample Mean has an expected value (what value it expects to take on average) of itself then the greek letter mu which stands for the Sample Mean will provide a Bias of 0. Bias = mu - mu = 0 Secondly as the Variance of the the Sample Mean is mu/(n-1) this leads us to believe that the Variance will fall as we increase the sample size. Variance is a measure of the dispersion of values collected from the centre of the data. Where the centre of the data is a fixed value equal to the median. Put Bias and Variance together and you get the Mean Squared Error which is the error associated with using an estimator of the Population Mean. The formula for Mean Squared Error = Bias^2 + Variance With our estimator we can see that as the Bias = zero, it has no relevance to the error and as the variance falls as the sample size increases then we can conclude that the error associated with using the sample mean will fall as the sample size increases. Conclusions: The Random Sample of public opinon will on average lead to a true representation of the Population Mean and therefore the random samle you have will represnt the public opinion to a fairly high degree of accuracy. Finally, this degree of accuracy will rise incredibly quickly as the sample size rises thus leading to a very accurate representation (on average)
What is replicated or interpenetrating sampling?
It involves selection of a certain number of sub-samples rather than one full sample from a population. All the sub-samples should be drawn using the same sampling technique and each is a self-contained and adequate sample of the population. Replicated sampling can be used with any basic sampling technique: simple or stratified, single or multi-stage or single or multiphase sampling. It provides a simple means of calculating the sampling error. It is practical. The replicated samples can throw light on variable non-sampling errors. But disadvantage is that it limits the amount of stratification that can be employed. IPS(interpenetrating sampling) provides a quick, simple, and effective way of estimating the variance of an estimator even in a complex survey. In fact, IPS is the foundation of modern resampling methods like Jackknife, bootstrap, and replication methods. In IPS, three basic principles of experimental designs, namely, randomization, replication, and local control, are used. IPS is used extensively not only in agriculture, but also in social sciences, demography, epidemiology, public health, and many other fields.
Is sample variance unbiased estimator of population variance?
No, it is biased.
Which of the following best describes the condition necessary to justify using a pooled estimator of the population variance?
1- Assuming this represents a random sample from the population, the sample mean is an unbiased estimator of the population mean. 2-Because they are robust, t procedures are justified in this case. 3- We would use z procedures here, since we are interested in the population mean.
Why sampling needed?
Sampling is needed in order to determine the properties of a distribution or a population. Sampling allows the scientist to determine the variance in an estimate.
What does it mean to say that the sample variance provides an unbiased estimate of the population variance?
It means you can take a measure of the variance of the sample and expect that result to be consistent for the entire population, and the sample is a valid representation for/of the population and does not influence that measure of the population.
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.
What are the properties of good estimators?
There are four main properties associated with a "good" estimator. These are: 1) Unbiasedness: the expected value of the estimator (or the mean of the estimator) is simply the figure being estimated. In statistical terms, E(estimate of Y) = Y. 2) Consistency: the estimator converges in probability with the estimated figure. In other words, as the sample size approaches the population size, the estimator gets closer and closer to the estimated. 3) Efficiency: The estimator has a low variance, usually relative to other estimators, which is called relative efficiency. Otherwise, the variance of the estimator is minimized. 4) Robustness: The mean-squared errors of the estimator are minimized relative to other estimators.
When do you use a z-value rejection region when testing a claim about a population mean?
If you know the population variance or if you have a very large sample then you could reliably use a Z test. Otherwise you should use a t test and use s^2 as an estimator for the population variance.
Standard deviation considered a crude measure of variance?
No. Well not exactly. The square of the standard deviation of a sample, when squared (s2) is an unbiased estimate of the variance of the population. I would not call it crude, but just an estimate. An estimate is an approximate value of the parameter of the population you would like to know (estimand) which in this case is the variance.
(b)For a set of numbers 1,4,7. Find population mean and Variance. Draw up frequency distributions of means of all possible samples of size 3 when sampling is carried out with replacement. Find mean and variance of distribution?
lowest
Is sample standard deviation the same as standard deviation?
No, the standard deviation is a measure of the entire population. The sample standard deviation is an unbiased estimator of the population. It is different in notation and is written as 's' as opposed to the greek letter sigma. Mathematically the difference is a factor of n/(n-1) in the variance of the sample. As you can see the value is greater than 1 so it will increase the value you get for your sample mean. Essentially, this covers for the fact that you are unlikely to obtain the full population variation when you sample.
How is it that a random samples gives a fairly accurate representation of public opinion?
The main point here is that the Sample Mean can be used to estimate the Population Mean. What I mean by that is that on average, the Sample Mean is a good estimator of the Population Mean. There are two reasons for this, the first is that the Bias of the estimator, in this case the Sample Mean, is zero. A Bias other than zero overestimates or underestimates the Population Mean depending on its value. Bias = Expected value of estimator - mean. This can be expressed as EX(pheta) - mu (pheta) As the Sample Mean has an expected value (what value it expects to take on average) of itself then the greek letter mu which stands for the Sample Mean will provide a Bias of 0. Bias = mu - mu = 0 Secondly as the Variance of the the Sample Mean is mu/(n-1) this leads us to believe that the Variance will fall as we increase the sample size. Variance is a measure of the dispersion of values collected from the centre of the data. Where the centre of the data is a fixed value equal to the median. Put Bias and Variance together and you get the Mean Squared Error which is the error associated with using an estimator of the Population Mean. The formula for Mean Squared Error = Bias^2 + Variance With our estimator we can see that as the Bias = zero, it has no relevance to the error and as the variance falls as the sample size increases then we can conclude that the error associated with using the sample mean will fall as the sample size increases. Conclusions: The Random Sample of public opinon will on average lead to a true representation of the Population Mean and therefore the random samle you have will represnt the public opinion to a fairly high degree of accuracy. Finally, this degree of accuracy will rise incredibly quickly as the sample size rises thus leading to a very accurate representation (on average)
Will running more trials affect results of a sampling distribution?
Yes. It should reduce the variance.
