THIS SCHEDULE IS PRELIMINARY, AND SUBJECT TO CHANGE
INFORMATION WILL BE UPDATED THROUGH THE QUARTER
DO NOT EXPECT ALL LINKS TO BE WORKING YET.
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LM denotes the class text, Larsen and Marx, 4 th. edition. Homeworks Exx refer to the Questions that are in each section of each Chapter. (Note that not all the questions are at the ends of sections.) Homework questions will be listed on the schedule, but check the Homeworks page for hints and additional details.
Homeworks/ Possible Exx. | ||
| WEEK 1 | ||
| Mon Jan 4 |
1. Introduction to Statistics and Probability Models:
LM Ch.1 |
EAT to take class Web page for STAT 340: Fall 2009 Introduction to Genetics Notes for week 1: Jan 4,8. |
| Wed Jan 6 | Review of discrete distributions, and their means and variances. |
Nick to take class Homework 1: due Jan 13. LM: 2.2.22, 2.4.11, 2.4.28, 2.5.25, 3.2.5, 3.2.19. |
| Fri, Jan 8 |
2. Estimators and estimates of parameters LM 5.1 3. Estimators based on n-samples; Binomial, Poisson and Uniform Examples. | |
| WEEK 2 | ||
| Mon, Jan 11 | Nick:: Review of continuous distributions, and their means and variances. |
Notes for week 2 (posted Jan 7): Jan 13,15. EAT Tues Office hour: 1.30-2.30 this week only. |
| Wed Jan 13 |
4. Method of Moments LM 5.2 (last bit) Uniform, Exponential and Normal examples. |
Homework 1 (see 1/06) is due. Homework 2: due 1/20 LM: 3.5.4, 3.5.7, 3.6.9, 3.6.12, 5.4.9, 5.4.18 |
| Fri Jan 15 |
5. Properties of estimators; LM 5.4 Unbiasedness and MSE. 6. Examples of estimators and their properties. |
Homework 1 solns (posted Jan 15) Notes for week 3: Jan 20,22 (posted Jan 12) |
| WEEK 3 | ||
| Monday, Jan 18 | MLK Holiday |
NO CLASSES. Notes for week 3: Jan 20,22 (posted Jan 12) Notes sheet for midterm 1 (posted Jan 15; revised Jan 22) Info for midterm 1 (posted Jan 22) |
| Wed Jan 20 | Working examples on unbiasedness and mse |
Homework 2 (see 1/13) is due. Homework 3: due 1/27 LM 5.2.16: 5.2.23: 5.4.2; 5.4.7; 5.4.10; 5.7.2 |
| Fri Jan 22 |
7. Asymptotic unbiasedness and consistency LM 5.7 8. Examples of consistency and other properties |
Homework 2 solns (posted Jan 22) Extra possible exercises (posted Jan 22) |
| WEEK 4 | ||
| Mon Jan 25 | More examples: unbiasedness, asymptotic unbiasedness, variance, mean square error, consistency. | Nick Basch section |
| Wed Jan 27 |
9. Moment generating functions: LM 3.12
(Note: Jan 27 notes are last page of "week-3" -- see under Jan 15) |
Homework 3 due (see 1/20);
Homework 3 solns (posted Jan 27) Homework 4: due 2/05 LM: 4.2.10, 4.2.26, 4.3.10, 3.12.5 (b,c,d), 3.12.14, 4.6.1 Notes for week 4: Jan 29, Feb 3 (posted Jan 20) |
| Fri Jan 29 |
10. Gamma distributions LM 4.6 Midterm review |
Notes sheet for midterm 1 (posted Jan 15; revised Jan 22)
Info for midterm 1 (posted Jan 22) EAT regular Friday office hour: 2-3.15 |
| WEEK 5 | ||
| Mon Feb 1 | Midterm-1 , and Solutions (Posted Feb 3) |
Nick Basch to proctor EAT away; No Tues office hour |
| Wed Feb 3 |
11. Sums of squares of independent Normal r.v.s; Chi-squared distributions ; LM P. 474 (Notes are last page of week-4 notes). |
Notes for week 5: Fri Feb 5 Homework 5: due 2/10; this is a 1/2-hwk LM: 3.7.11, 3.7.22, 3.7.44 Note: EAT special THURS office hour; 3.30-5 p.m. |
| Fri Feb 5 |
12. Joint pmf/pdf, cdf LM 3.7 13. Independent r.v.s, Likelihood and log-likelihood LM5.2 |
Note: FRIDAY: Homework 4 due. (see 01/27). Note: Midterm solutions are posted -- see Feb 1 EAT regular Fri office hour; 2.00-3.15; |
| WEEK 6 | ||
| Mon Feb 8 | Examples, joint densities and likelihood |
Notes for week 6: Feb 10,12; (posted Feb 5) Homework 4 solns |
| Wed Feb 10 | 14. Maximum likelihood estimation LM5.2 |
Mini-homework 5 due; see Feb 3 posted. Homework 6: due 2/17; LM 5.2.4, 5.2.6, 5.2.9, 5.2.11, 5.2.12, 5.2.14 PDF of these questions Return of midterm and hwk-4. Summary of midterm scores |
| Fri Feb 12 |
15. Conditional pmf/pdf LM 3.11 16. Sufficiency: Factorization criterion LM 5.6 | |
| WEEK 7 | ||
| Monday Feb 15 | Presidents' Day |
NO CLASSES Homework 5 solns Notes for week 7: Feb 17,19; (posted Feb 9) |
| Wed Feb 17 | 17. Examples of sufficient statistics etc. |
Notes sheet for midterm 2 (posted Feb 12) Homework 6 due -- see Feb 10 posted. Homework 7: due Friday 2/26; LM 5.6.1, 5.6.2, 5.6.5, 5.5.2, 5.5.3, 5.5.4 PDF of these questions |
| Fri Feb 19 |
Minumum variance estimators, CRLB (LM 5.5) Large-sample properties of MLE: unbiased and minumum variance. |
Nick Basch to teach Nick's notes and examples solutions Homework 6 solutions |
| WEEK 8 | ||
| Mon Feb 22 |
Midterm-2 review Notes sheet for midterm 2 (posted Feb 12) Info for midterm 2 (posted Feb 21) |
EAT to teach Notes for week 8: Feb 26; (posted Feb 21) |
| Wed Feb 24 | Midterm -2 and Solutions |
Nick Basch to proctor; Homework 8: due Wed 3/3; LM 5.3.1, 5.3.10, 5.3.23. PDF of these questions |
| Fri Feb 26 | 18, 19: Interval estimation: LM 5.3 |
Notes for week 8: Feb 26; (posted Feb 21) Homework 7 due: see 2/17 posted. |
| WEEK 9 | ||
| Mon Mar 1 | Examples of interval estimation. |
Nick to do student evals Notes for week 9 Hwk 7 solutions |
| Wed Mar 3 |
20. Bayesian inference: Prior and posterior distributions. LM 5.8 |
Homework 8 due (half-homework): see 2/24 posted. Midterm 2 Solutions; Scores summary; Homework 9: due Friday 3/12; LM 5.8 Nos, 1,4*, 5*, 6,7,8 (*: see hwk page) |
| Fri Mar 5 |
21. Conjugate prior distributions. |
Hwk 8 solutions Morita 2006 Stat 341 final (posted Mar 1) EAT student evals |
| WEEK 10 | ||
| Mon Mar 8 | Examples of prior and posterior distributions. | Nick. |
| Wed Mar 10 |
22. Bayesian point and interval estimation Posterior mean as an estimate: examples Posterior median as an estimate: examples |
Notes sheet for Final (final version?) EAT Office Hour: Thurs 3-4.15 (NOT Friday this week) |
| Fri Mar 12 |
Final review for final: |
Homework 9 due Friday;
Solutions Morita 2006 Stat 341 final (posted Mar 1) |
| EXAMS WEEK | ||
| Mon/Tues Mar 15/16 |
Morita 2006 Stat 341 final (posted Mar 1) Notes sheet for Final (Posted Mar 9) | EAT Extra Office hours: MONDAY 3-4:15, TUES 2-3:15. |
|
Wed March 17 |
Final Exam: 2:30-4:20: LOW 102 (with 2 typos corrected). | Official UW time and place for this exam |
Material postponed to 342:
The mgf of a Normal distribution;
hence linear combinations of independent
Normals are Normal;
Correlation; Normals are independent iff correlation =0
-- can be done using bivariate mgf;
Independence of X-bar and S-squared. Hence distribution of S-sqd
Normal and t-distributions LM 7.2,7.3
Minimizing mse for estimation of sigma^2:
optimal is S^2/(n+2) -- cf Uniform example
done in 341.