STAT W21 Lecture Notes - Lecture 25: Variance, Simple Random Sample, Standard Error
Document Summary
Estimator is an assignment of a number (the estimate of the parameter) to each possible random sample of size "n" from the population. Rule that assigns values to samples is called the estimator, and the value that is assigned. Estimator is a random variable and the estimate typically will not equal the value of the population parameter. Estimator = parameter + bias + chance variability. Chance variability reflects the luck of the draw - which particular units happened to be in the sample. Bias is the systematic difference between the value the estimator takes and the value of the parameter - a tendency for the estimator to be too high or too low on the average. Typical size of the chance variability is the standard error (se) of the estimator. Bias is the long-run average difference between the parameter and the estimate if we. Standard error measures the long-run average spread of the estimated values in the.
