Course schedule
Math 410 meets Monday and Wednesday 2.30pm - 4.25pm.
Topic sequence
Homework is due on the first lecture the week after the week the homework is given: the first homework questions are all due on February 5.
Week | 2018 dates | Topic | Chapter | Homework |
---|---|---|---|---|
1 | Jan 29, 31 | Welcome, intro, structure, background, crash course in probability and measures | 1.1 - 1.11 (skim) | 1.11: 5, 10, 17, 20, 27, 45 |
2 | Feb 5, 7 | Models and estimators, sufficiency | 3.1-4 | 3.7: 2, 4, 7, 11 |
3 | Feb 14 | Completeness | 3.5 | 3.7: 16 |
4 | Feb 20, 21 | Rao-Blackwell; Unbiased estimators | 3.6, 4.1-2 | 3.7: 31; 4.7: 1, 4, 5, 22 |
5 | Feb 26, 28 | Standard normals and Variance bounds | 4.4-6 (skim proofs) | 4.7: 26, 30, |
6 | Mar 5, 7 | Hypothesis testing | 12.1-3 | 12.8: 3, 4, 8, 17, 22 |
7 | Mar 12, 14 | Confidence intervals | 9.4, 12.4 | 9.10:10, 12, 14; 12.8: 28 |
8 | Mar 19, 21 | Two-sided and unbiased tests | 12.6-7 | |
9 | Mar 26, 28 | More estimators: Maximum Likelihood & Method of Moments | 9.2-3, 6, 9.10:2 | 9.10:8, 9 |
10 | Apr 9 | Large sample testing | 17.1-4 | 17.5:2, 4a |
11 | Apr 16, 18 | GLM: Canonical form and estimation | 14.1-2 | 14.9:1a-d, 4a-d |
12 | Apr 23, 25 | Generalized linear models | 14.3-5 | 14.9:4e-f, 18 |
13 | Apr 30, May 2 | Categorical data and goodness of fit. | 14.6-8 | 14.9:4g-h, 14 |
14 | May 7, 9 | Nonparametric testing | 9th deadline draft | |
15 | May 14, 16 | Additional topics | 16th deadline report |
Presentation schedule
Name | Topic | Presentation Time |
---|---|---|
Renisa Myrtezaj | Bayesian Estimation | May 14, 14.30 |
Joseph Murgolo | Polynomial Regression | May 14, 15.00 |
Robert Ferrando | Nonparametric Regression | May 14, 15.30 |
Fatme Chour | ARMA | May 16, 14.30 |
Michael Khanis | Optimal Stopping | May 16, 15.00 |
Steve Gad | Critical Path Analysis | May 16, 15.30 |
Report
The source of your grades in this course will be on a written report. You have a choice of topic:
- Evaluating the statistical methods of a research paper. You will pick a paper to review by the third meeting of the course.
- Describing a statistical method not mentioned in the course.
Your paper report should contain
- A description of the objective of the paper
- A comprehensive description of the statistical methods chosen by the authors
- An evaluation of whether the methods chosen were appropriate. If you find that the authors made mistakes in choosing their analysis methods, you should suggest better choices.
- A reproduction of the statistical analyses made by the authors: compute the estimators they computed, and compare with the values the authors reported.
Your methods report should
- Comprehensively describe the statistical analysis methods and the data collection methods.
- Critically evaluate the applicability requirements and limitations of the method.
- Perform an analysis on a dataset using the method.
- Be presented in class
Each of these tasks is graded on a scale of Excellent / Good / Acceptable:
- Excellent is awarded for Good plus an additional requirement of suggestions and evaluations of alternatives
- Good is awarded for comprehensive coverage without any formal flaws.
- Acceptable is awarded for up to 4 minor flaws:
- language errors to the point of ambiguous text
- arithmetic errors that do not essentially change the qualitative argument.
Failing any one of the 4 tasks will fail the course.
Excellent | Good | Acceptable | Total |
---|---|---|---|
4x | 0x | 0x | A |
3x | 1x | 0x | A- |
3x | 0x | 1x | A- |
2x | 2x | 0x | A- |
2x | 1x | 1x | B+ |
2x | 0x | 2x | B |
1x | 3x | 0x | B+ |
1x | 2x | 1x | B |
1x | 1x | 2x | B- |
1x | 0x | 3x | B- |
0x | 4x | 0x | B |
0x | 3x | 1x | B- |
0x | 2x | 2x | B- |
0x | 1x | 3x | C+ |
0x | 0x | 4x | C |
Final Examination
The final will contribute with a chance to raise your grade by two grade steps along the sequence D → C → C+ → B- → B → B+ → A- → A