Ians estimate has a 7% error. What are the possible estimates lab could have made?

What is the significance of standard error of estimates?

Standard error is an indicator of the expected level of variation from the predicted outcome in an estimate. So even though the mean is mostly likely the outcome, the actual range the outcome could call into is a region which is measured by the standard error.

What is the absolute value of the difference between the point estimate and the population parameter it estimates?

B. The sampling error

What are the main qualities of a good estimator?

A "Good" estimator is the one which provides an estimate with the following qualities:Unbiasedness: An estimate is said to be an unbiased estimate of a given parameter when the expected value of that estimator can be shown to be equal to the parameter being estimated. For example, the mean of a sample is an unbiased estimate of the mean of the population from which the sample was drawn. Unbiasedness is a good quality for an estimate, since, in such a case, using weighted average of several estimates provides a better estimate than each one of those estimates. Therefore, unbiasedness allows us to upgrade our estimates. For example, if your estimates of the population mean µ are say, 10, and 11.2 from two independent samples of sizes 20, and 30 respectively, then a better estimate of the population mean µ based on both samples is [20 (10) + 30 (11.2)] (20 + 30) = 10.75.Consistency: The standard deviation of an estimate is called the standard error of that estimate. The larger the standard error the more error in your estimate. The standard deviation of an estimate is a commonly used index of the error entailed in estimating a population parameter based on the information in a random sample of size n from the entire population.An estimator is said to be "consistent" if increasing the sample size produces an estimate with smaller standard error. Therefore, your estimate is "consistent" with the sample size. That is, spending more money to obtain a larger sample produces a better estimate.Efficiency: An efficient estimate is one which has the smallest standard error among all unbiased estimators.The "best" estimator is the one which is the closest to the population parameter being estimated.

What are the quality of a good estimator?

A "Good" estimator is the one which provides an estimate with the following qualities:Unbiasedness: An estimate is said to be an unbiased estimate of a given parameter when the expected value of that estimator can be shown to be equal to the parameter being estimated. For example, the mean of a sample is an unbiased estimate of the mean of the population from which the sample was drawn. Unbiasedness is a good quality for an estimate, since, in such a case, using weighted average of several estimates provides a better estimate than each one of those estimates. Therefore, unbiasedness allows us to upgrade our estimates. For example, if your estimates of the population mean µ are say, 10, and 11.2 from two independent samples of sizes 20, and 30 respectively, then a better estimate of the population mean µ based on both samples is [20 (10) + 30 (11.2)] (20 + 30) = 10.75.Consistency: The standard deviation of an estimate is called the standard error of that estimate. The larger the standard error the more error in your estimate. The standard deviation of an estimate is a commonly used index of the error entailed in estimating a population parameter based on the information in a random sample of size n from the entire population.An estimator is said to be "consistent" if increasing the sample size produces an estimate with smaller standard error. Therefore, your estimate is "consistent" with the sample size. That is, spending more money to obtain a larger sample produces a better estimate.Efficiency: An efficient estimate is one which has the smallest standard error among all unbiased estimators.The "best" estimator is the one which is the closest to the population parameter being estimated.

What does the standard error of the difference between means mean?

An estimate for the mean of a set of observations is just that - an estimate. Another set of observations will give a different estimates. These estimates for the mean will have a distribution which will have a standard error. If you have two sub-populations, the mean of each sub-population will have a standards error and the se of the difference between the means is a measure of the variability of the estimates of the difference.A typical school work example: the heights of men and of women. There will be a mean height for men, Hm, with a se for men's heights and a mean height for women, Hw, with its own se. The difference in mean heights is Hm - Hw and which will have an estimated se.

When is margin of error used?

Margin of error is used in statistics to express the uncertainty associated with survey results. It indicates the range within which the true population value is likely to fall. Margin of error helps to measure the reliability and accuracy of the survey findings.

What is the greatest possible error of 1.20m?

The greatest possible error is 0.005

Is it possible to have a cell phone number that can't receive incoming calls?

Yes It Is Possible Something could be wrong with your phone or there could be a signal error

What is the greatest possible error of 0.991g?

The greatest possible error is 0.0005

What is the greatest possible error of 11.125 oz?

The answer depends on the measurement precision. If the figure is correct to the last decimal, the greatest possible error is 0.0005 oz. However, the figure could refer to 11 oz + 32 drachms. If accurate to the nearest drachm, the greatest possible error is 0.00195 oz.

How do you find the greatest possible error for 19.2?

what is the greatest possible error of 350mi

What are the qualities of a good statisticians?

A "Good" estimator is the one which provides an estimate with the following qualities:Unbiasedness: An estimate is said to be an unbiased estimate of a given parameter when the expected value of that estimator can be shown to be equal to the parameter being estimated. For example, the mean of a sample is an unbiased estimate of the mean of the population from which the sample was drawn. Unbiasedness is a good quality for an estimate, since, in such a case, using weighted average of several estimates provides a better estimate than each one of those estimates. Therefore, unbiasedness allows us to upgrade our estimates. For example, if your estimates of the population mean µ are say, 10, and 11.2 from two independent samples of sizes 20, and 30 respectively, then a better estimate of the population mean µ based on both samples is [20 (10) + 30 (11.2)] (20 + 30) = 10.75.Consistency: The standard deviation of an estimate is called the standard error of that estimate. The larger the standard error the more error in your estimate. The standard deviation of an estimate is a commonly used index of the error entailed in estimating a population parameter based on the information in a random sample of size n from the entire population.An estimator is said to be "consistent" if increasing the sample size produces an estimate with smaller standard error. Therefore, your estimate is "consistent" with the sample size. That is, spending more money to obtain a larger sample produces a better estimate.Efficiency: An efficient estimate is one which has the smallest standard error among all unbiased estimators.The "best" estimator is the one which is the closest to the population parameter being estimated.