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7

Point Estimation

of Parameters

CHAPTER OUTLINE

7-1 INTRODUCTION 7-3 METHODS OF POINT ESTIMATION
7-2 GENERAL CONCEPTS OF POINT 7-3.1 Method of Moments
ESTIMATION
7-3.2 Method of Maximum Likelihood
7-2.1 Unbiased Estimators
7-3.3 Bayesian Estimation of
7-2.2 Proof that S is a Biased Estimator Parameters (CD Only)
of (CD Only)
7-4 SAMPLING DISTRIBUTIONS
7-2.3 Variance of a Point Estimator
7-5 SAMPLING DISTRIBUTIONS
7-2.4 Standard Error: Reporting a OF MEANS
Point Estimate
7-2.5 Bootstrap Estimate of the Standard
Error (CD Only)
7-2.6 Mean Square Error of an Estimator

LEARNING OBJECTIVES

After careful study of this chapter you should be able to do the following:
1. Explain the general concepts of estimating the parameters of a population or a probability distribution
2. Explain important properties of point estimators, including bias, variance, and mean square error
3. Know how to construct point estimators using the method of moments and the method of maxi-
mum likelihood
4. Know how to compute and explain the precision with which a parameter is estimated
5. Understand the central limit theorem
6. Explain the important role of the normal distribution as a sampling distribution

CD MATERIAL
7. Use bootstrapping to find the standard error of a point estimate
8. Know how to construct a point estimator using the Bayesian approach

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