7-1 INTRODUCTION

7-2 GENERAL CONCEPTS OF POINT

ESTIMATION

7-2.1 Unbiased Estimators

7-2.2 Proof that Sis a Biased Estimator

of (CD Only)

7-2.3 Variance of a Point Estimator

7-2.4 Standard Error: Reporting a

Point Estimate

7-2.5 Bootstrap Estimate of the Standard

Error (CD Only)

7-2.6 Mean Square Error of an Estimator

7-3 METHODS OF POINT ESTIMATION

7-3.1 Method of Moments

7-3.2 Method of Maximum Likelihood

7-3.3 Bayesian Estimation of

Parameters (CD Only)

7-4 SAMPLING DISTRIBUTIONS

7-5 SAMPLING DISTRIBUTIONS

OF MEANS

220

7

Point Estimation

of Parameters

CHAPTER OUTLINE

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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