Question

4.
is the difference between the true value and estimated vale
study​

Answer 1

Bias in Statistics is defined as the difference between the expected value of a statistic and the true value of the corresponding parameter. Therefore, the bias is a measure of the systematic error of an estimatorIn other words, the bias (sampling error) is systematic error in measurement or sampling and it tells how far off on the average the model is from the truth.

Gauss, C.F. (1821) during his work on the least squares method gave the concept of an unbiased estimator.

The bias of an estimator of a parameter should not be confused with its degree of precision as the degree of precision is a measure of the sampling error. The bias is favoring of one group or outcome intentionally or unintentionally over other groups or outcomes available in the population under study. Unlike random errors, bias is a serious problem and bias can be reduced by increasing the sample size and averaging the outcomes.

Answer 2

but I'm going back next time no matter where are the same way you do the dishes done