Abstract
This chapter focuses on the use of estimation techniques in data analysis. An estimator θ′ of a parameter θ of a random variable X is a random variable, which depends upon a random sample X1, X2,….Xn. The two most common estimators are the sample mean, also known as the arithmetic mean and the sample variance. The chapter discusses some desirable properties of estimators and shows that, for some special populations, the sample mean X− and the sample variance S2 have many of these properties. The chapter reviews that although unbiased estimators are desirable in many respects, there is not always one available for a particular estimate. In such cases, a consistent estimator θ′ with minimum mean-squared error may be considered. The method of moments estimation is also discussed, and the idea of maximum likelihood estimation is reviewed in the chapter.
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