When can we say that the sample mean is either good or poor estimate for the population mean?

When can we say that the sample mean is either good or poor estimate for the population mean?

Answer: If the standard deviation is large and far from zero then the sample mean is poor,and if the standard deviation is small and closer to zero,then tha sample mean is good estimate for the population mean.

What is the difference between an estimator and an estimate?

An estimator is a function of the sample, i.e., it is a rule that tells you how to calculate an estimate of a parameter from a sample. An estimate is a Рalue of an estimator calculated from a sample.

When would you use the mean or median as the point estimate?

All Answers (35) If the data are normally distributed the mean is appropriate. If the distribution is not, for example log-normal or a similar distribution, you could use the median.

In which condition we can say an estimator is a good estimator?

Point Estimates A good estimator must satisfy three conditions: Unbiased: The expected value of the estimator must be equal to the mean of the parameter. Consistent: The value of the estimator approaches the value of the parameter as the sample size increases.

Is it better to use mean or median?

When you have a symmetrical distribution for continuous data, the mean, median, and mode are equal. In this case, analysts tend to use the mean because it includes all of the data in the calculations. However, if you have a skewed distribution, the median is often the best measure of central tendency.

Is mean and sample mean the same?

“Mean” usually refers to the population mean. This is the mean of the entire population of a set. The mean of the sample group is called the sample mean.

Which is the best way to estimate the replacement cost of a home?

An independent appraiser is probably the most accurate method of figuring out a home’s replacement cost value. “An independent appraiser will actually and accurately inspect your home and knows exactly all the cost that goes into rebuilding your home.

Which is the correct definition of mean imputation?

First, a definition: mean imputation is the replacement of a missing observation with the mean of the non-missing observations for that variable. True, imputing the mean preserves the mean of the observed data.

Are there any problems with mean imputation for missing data?

First, a definition: mean imputation is the replacement of a missing observation with the mean of the non-missing observations for that variable. Problem #1: Mean imputation does not preserve the relationships among variables. True, imputing the mean preserves the mean of the observed data.

Can a statistic be imputed if standard error is too low?

Any statistic that uses the imputed data will have a standard error that’s too low. In other words, yes, you get the same mean from mean-imputed data that you would have gotten without the imputations. And yes, there are circumstances where that mean is unbiased. Even so, the standard error of that mean will be too small.