# 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, 3
11 Apr 16, 18 GLM: Canonical form and estimation 14.1-2 14.9:1, 4a-d
12 Apr 23, 25 Gauss-Markov, linear regression 14.3-5 14.9:4e-f, 18
13 Apr 30, May 2 F and $\chi^2$, confidence intervals - testing in GLM. 14.6-8 14.9:4g-h, 14
14 May 7, 9 free space
15 May 14, 16 Additional topics

## Report

The source of your grades in this course will be on a written report. You have a choice of topic:

1. Evaluating the statistical methods of a research paper. You will pick a paper to review by the third meeting of the course.
2. Describing a statistical method not mentioned in the course.

Your paper report should

1. Comprehensively describe the statistical analysis methods and the data collection methods used by the authors of the paper.
2. Critically evaluate the choices made by the authors: data collection designs, analysis methods.
3. Recalculation of the statistical analyses of the authors, alternatively a comprehensive explanation of why the research paper does not provide enough information to replicate the analyses.

Your methods report should

1. Comprehensively describe the statistical analysis methods and the data collection methods.
2. Critically evaluate the applicability requirements and limitations of the method.
3. Perform an analysis on a dataset using the method.

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.

Excellent | Good | Acceptable| Total -----------+-----------+-----------+------- 3x | 0x | 0x | A 2x | 1x | 0x | B+ 2x | 0x | 1x | B 1x | 2x | 0x | B- 1x | 1x | 1x | C+ 1x | 0x | 2x | C 0x | 3x | 0x | C 0x | 2x | 1x | D 0x | 1x | 2x | D 0x | 0x | 3x | D

## 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