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:
- Explain the general concepts of estimating the parameters of a population or a probability distribution
- Explain important properties of point estimators, including bias, variance, and mean square error
- Know how to construct point estimators using the method of moments and the method of maxi-
mum likelihood - Know how to compute and explain the precision with which a parameter is estimated
- Understand the central limit theorem
- Explain the important role of the normal distribution as a sampling distribution
CD MATERIAL
- Use bootstrapping to find the standard error of a point estimate
- Know how to construct a point estimator using the Bayesian approach
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