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An unbiased estimator is a person who gives a price for a service or goods and that person has no ulterior motives that would influence the price either way. A person who is biased might reflect the estimated price to show favor to one person more than another. For example: If my uncle was to bid on a job and I was the estimator for the person who wanted the work done, then I would have a bias in that I would reflect the price so that my uncle would get the job. This is unethical and illegal. An unbiased person has no preference as to who would get the job and would do the estimate honestly. An unbiased estimator has a very specific meaning in statistics and a good statistician needs to answer this meaning of the term.
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Why median is not a consistent estimator?
Because it is easily influenced by extreme values (i.e. it is not unbiased).
What is the uses of ratio estimator?
what is the use and application of ratio estimator?
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.
Difference between estimate estimator?
I think, the estimate is a numerical value, wile the estimator is a function or operator, which can be generate more estimates according to some factors. For example (xbar) is estimator for (meu), which can be various when the sample size in various, the value that will be produced is an (estimate), but (xbar) is estimator.
Not to take sides is to remain what?
unbiased.
What is the proof that demonstrates the unbiased estimator of variance?
The proof that demonstrates the unbiased estimator of variance involves showing that the expected value of the estimator equals the true variance of the population. This is typically done through mathematical calculations and statistical principles to ensure that the estimator provides an accurate and unbiased estimate of the variance.
What is the proof that the sample variance is an unbiased estimator?
The proof that the sample variance is an unbiased estimator involves showing that, on average, the sample variance accurately estimates the true variance of the population from which the sample was drawn. This is achieved by demonstrating that the expected value of the sample variance equals the population variance, making it an unbiased estimator.
Is sample variance unbiased estimator of population variance?
No, it is biased.
Meaning of BLUE in least square?
Best Linear Unbiased Estimator.
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.
Show that in simple random sampling the sample variance is an unbiased estimator of population variance?
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.
Why median is not a consistent estimator?
Because it is easily influenced by extreme values (i.e. it is not unbiased).
What is the best estimator of population mean?
The best estimator of the population mean is the sample mean. It is unbiased and efficient, making it a reliable estimator when looking to estimate the population mean from a sample.
Differentiate estimate and estimator?
It can get a bit confusing! The estimate is the value obtained from a sample. The estimator, as used in statistics, is the method used. There's one more, the estimand, which is the population parameter. If we have an unbiased estimator, then after sampling many times, or with a large sample, we should have an estimate which is close to the estimand. I will give you an example. I have a sample of 5 numbers and I take the average. The estimator is taking the average of the sample. It is the estimator of the mean of the population. The average = 4 (for example), this is my estmate.
What do you understand by BLUE in statistics?
The related link below is the best I could find on BLUE statitics. Blue means Best Linear Unbiased Estimator
Is 500 a reasonable estimate for the difference 749 259?
It depends on what a 'reasonable estimator' is. A good estimator is usually one which is considered unbiased. That is Expected value of the estimator = E[x] = the answer In your case, the E[x] = 500 but the answer is actually 490. This represents a 2% margin of error. Since it's a pretty simple computation, I personally think that error is not reasonable.
What is the Gauss-Markov theory?
In statistics, the Gauss-Markov theorem states that in a linear regression model in which the errors have expectation zero and are uncorrelated and have equal variances, the best linear unbiased estimator (BLUE) of the coefficients is given by the ordinary least squares (OLS) estimator, provided it exists.
