The final examination will be held Tuesday, May 10th at 1:30PM in Duffy 207.
The exam will consist of two sections:
All problems in the second section will be constructed so that they can be solved fairly quickly by a person with sufficient underestanding of the course material.
The terms will be taken from the following list:
| Term | Text Reference |
| estimator | Definition 8.1 |
| point estimator | Section 8.1 |
| confidence interval | Section 8.5 |
| unbiased estimator | Definition 8.2 |
| biased estimator | Definition 8.2 |
| mean square error of a point estimator | Definition 8.1 |
| pivotal method of constructing confidence intervals | Section 8.5 |
| large sample confidence intervals | Section 8.6 |
| small sample confidence intervals | Section 8.8 |
| confidence intervals for sigma2 | Section 8.9 |
| relative efficiency | Section 9.2 |
| consistent estimator | Definition 9.2 |
| convergence in probability | class notes |
| convergence in distribution | class notes |
| convergence in the rth mean | class notes |
| almost sure convergence | class notes |
| sufficient statistic | Definition 9.3 |
| likelihood of a sample | Definition 9.4 |
| method of moments estimator | Section 9.6 |
| maximum likelihood estimate | Section 9.7 |
| efficient estimator | Problem 9.8 |
| null hypothesis | Section 10.2 |
| alternative hypothesis | Section 10.2 |
| test statistic | Section 10.2 |
| rejection region | Section 10.2 |
| Type I error | Section 10.2 |
| Type II error | Section 10.2 |
| power of a test | Shaded box on page 541 |
| p-value | Definition 10.2 |
| likelihood ratio test | Section 10.11 |
| linear statistical model | Definition 11.1 |
| column space of a matrix | class notes |
| left null space of a matrix | class notes |
| simple regression | class notes |
| multiple regression | class notes |
| analysis of variance | class notes |
| analysis of covariance | class notes |
The theorems (on the matching section) will be taken from the following list:
| Term | Text Reference |
| Central Limit Theorem | Theorem 7.4 |
| Cramer-Rao inquality | Problem 9.8 |
| consistency theorem | Theorem 9.1 |
| convergence in probability theorems | Theorem 9.2, class notes, Theorem 9.3 |
| Neyman factorization theorem | Theorem 9.4 |
| Rao-Blackwell theorem | Theorem 9.5 |
| Neyman-Pearson Lemma | Theorem 10.1 |
| Asymptotic distribution of likelihood ratio | Theorem 10.2 |
The following formulas will be provided. In general, you do not have to memorize formulas, but you should know how to use them.