|
Class date |
Chapter |
Topic |
Problems |
Lab Assignments |
|
Sep 1 |
2 |
Outcomes, events, and probability |
2.1, 2.2, 2.4, 2.9, 2.13 |
Intro Matlab, questions, answers |
|
Sep 3 |
2 |
Outcomes, events, and probability |
|
|
|
Sep 8 |
3 |
3.4, 3.9, 3.11, 3.16, 3.18 |
M3.1 | |
|
Sep 10 |
3 |
|
||
|
Sep 15 |
4 |
4.2, 4.3, 4.4, 4.11, 4.14 |
M4.1 (due Sep 21) | |
|
Sep 17 |
4 |
|
||
|
Sep 22 |
5 |
Continuous random variables, darts |
5.1, 5.5, 5.9, 5.11, 5.12 |
M4.2, M4.3 |
|
Sep 24 |
5 |
|
||
|
Sep 29 |
6 |
6.1, 6.3, 6.6, 6.8 |
Gaussian Ex, sampling Ex | |
|
Oct 1 |
6 |
|
M6.1 (lab), M6.2 (due Oct 12) | |
|
Oct 6 |
7 |
Expectation and variance |
7.1, 7.2, 7.9, 7.13, 7.14 |
M7.1 (due Oct. 15) |
|
Oct 8 |
7 |
|
||
|
Oct 13 |
9 |
Joint distributions and independence |
9.1, 9.3, 9.6, 9.7, 9.13 |
|
|
Oct 15 |
9 |
|
||
|
Oct 20 |
|
Review |
|
|
|
Oct 21 |
|
Midterm exam |
(during Lab) |
in Wachman 447 |
|
Oct 22 |
10 |
Covariance and correlation |
10.1, 10.5, 10.8, 10.11, 10.15 |
M10.1 (due Oct. 29) |
|
Oct 27 |
|
Midterm exam discussion |
|
|
|
Oct 29 |
12 |
The Poisson process |
12.2, 12.5, 12.7 |
|
|
Nov 3 |
15 |
Exploratory data analysis: |
--- |
|
|
Nov 5 |
15 |
graphical summaries |
--- |
M15.1 to M15.4 (due Nov. 12) |
|
Nov 10 |
16 |
numerical summaries |
--- |
M16.1 to M16.3 (due Nov. 19) |
|
Nov 12 |
17 |
Basic statistical models |
17.4, 17.5, 17.6 (due Nov 19) |
|
|
Nov 17 |
19 |
Unbiased estimators |
19.6, 19.7 (Nov 24) |
|
|
Nov 19 |
20 |
Efficiency and mean squared error |
20.1, 20.7 (Nov 24) |
|
|
Nov 24 |
21 |
Maximum likelihood |
21.2, 21.3, 21.9, 21.13 (Dec 1) |
|
|
Dec 1 |
22 |
The method of least squares |
22.2, 22.3, 22.4, 22.8 (Dec 8) |
|
|
Dec 3 |
|
Review |
|
|
|
Dec 8 |
|
Review |
|
|
|
Dec 17 |
|
Final Exam |
Time: 1:00 to 3:00 pm |