Statistical estimation

This is the procedure of using statistic to estimate a population parameter

This is divided into point estimation whereas an estimate of a population parameter is described by a single number and interval estimation whereas an estimate of a population is described by a range whether the parameter may be considered to lie for illustration, a bus meant to take a class of 100 students as population N for trip has a limit to the maximum weight of 600 kilogram (kg) of which it can carry, the teacher realizes he has to determine the weight of the class but without sufficient time to weigh everyone he picks 25 students selected at random as like sample n = 25.  These students are weighed and their average weight recorded as 64 kilogram(kg) (x¯ - mean of a sample) along with a standard deviation (s), now by using this the teacher intends to estimate the average weight of the whole class (µ - population mean) by utilizing the statistical parameters standard deviation (s), and mean of the sample (x¯).

Characteristic of a good estimator

(i) Unbiased: whereas the expected value of the statistic is equal to the population parameter for illustration, if the expected mean of a sample is equal to the population mean

(ii) Consistency: whereas an estimator yields values more closely approaching the population parameter like the sample increases

(iii) Efficiency: whereas the estimator has smaller variance on repeated sampling.

(iv) Sufficiency: whereas an estimator uses all the information available in the data relating to a parameter