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What else can I help you with?
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.
What is a subset from a larger population?
in statistics a sample is a subset of population..
What is the best defiition of satiistic?
A statistic is a numerical value that represents a characteristic or measure of a dataset. It can be used to summarize data, identify trends, or make inferences about a population based on a sample. Statistics can include measures such as mean, median, mode, variance, and standard deviation, among others. Overall, they are critical tools for data analysis in various fields, including economics, social sciences, and healthcare.
How does halving a sample affect the weight of the sample?
If the sample is homogeneous, then half of its volume has half of its mass and half of its weight.
Why are CD sample rates 44.1 KHz?
because human hearing is about 20kHz. so according to sample theorem, it has to be twice..
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.
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 the sample variance is an unbiased estimator of the population variance?
The sample variance is considered an unbiased estimator of the population variance because it corrects for the bias introduced by estimating the population variance from a sample. When calculating the sample variance, we use ( n-1 ) (where ( n ) is the sample size) instead of ( n ) in the denominator, which compensates for the degree of freedom lost when estimating the population mean from the sample. This adjustment ensures that the expected value of the sample variance equals the true population variance, making it an unbiased estimator.
Is there a proof that demonstrates the unbiasedness of the sample variance?
Yes, there is a mathematical proof that demonstrates the unbiasedness of the sample variance. This proof shows that the expected value of the sample variance is equal to the population variance, making it an 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.
What is the best estimator of population mean?
The best point estimator of the population mean would be the sample mean.
Is mean an unbiased estimator of a population?
Yes, the sample mean is an unbiased estimator of the population mean. This means that, on average, the sample mean will equal the true population mean when taken from a large number of random samples. In other words, as the sample size increases, the expected value of the sample mean converges to the population mean, making it a reliable estimator in statistical analysis.
What is n-1in statistics?
In statistics, "n-1" refers to the degrees of freedom used in the calculation of sample variance and sample standard deviation. When estimating variance from a sample rather than a whole population, we divide by n-1 (the sample size minus one) instead of n to account for the fact that we are using a sample to estimate a population parameter. This adjustment corrects for bias, making the sample variance an unbiased estimator of the population variance. It is known as Bessel's correction.
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.
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 in the case of population is n and in sample is n-1?
In statistics, when calculating variance or standard deviation for a population, we use ( n ) (the total number of observations) because we have complete data. However, for a sample, we use ( n-1 ) (the degrees of freedom) to account for the fact that we are estimating a population parameter from a sample. This adjustment helps to correct for bias and provides a more accurate estimate of the population variance or standard deviation, ensuring that the sample statistic is an unbiased estimator.
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.
