|
Class date |
Chapter |
Topic |
Problems |
Lab Assignments |
|
Jan 18 |
2 |
Outcomes, events, and probability |
2.1, 2.2, 2.4, 2.9, 2.13 (due Jan 23) |
Intro Matlab, questions, answers |
|
Jan 20 |
2 |
Outcomes, events, and probability |
|
|
|
Jan 25 |
3 |
3.4, 3.9, 3.10, 3.16, 3.18 (due Jan 30) |
M3.1 (due Feb 1) | |
|
Jan 27 |
3 |
|
||
|
Feb 1 |
4 |
4.2, 4.3, 4.4, 4.11, 4.14 (due Feb 6) |
M4.1 (due Feb 8) | |
|
Feb 3 |
4 |
|
||
|
Feb 8 |
5 |
Continuous random variables, darts |
5.1, 5.5, 5.9, 5.11, 5.12 (due Feb 13) |
M4.2 (due Feb 15) |
|
Feb 10 |
5 |
|
||
|
Feb 15 |
6 |
6.1, 6.3, 6.6, 6.8 (due Feb 22) |
Gaussian Ex, sampling Ex | |
|
Feb 17 |
6 |
|
M6.1 (lab), M6.2 (due Feb 22) |
|
|
Feb 22 |
7 |
Expectation and variance |
7.1, 7.2, 7.9, 7.13, 7.14 (due Feb 29) |
|
|
Feb 24 |
7 |
|
M7.1 (due Feb 29) | |
|
Feb 29 |
9 |
Double Integral 1,
Double Integrals 2 Joint distributions and independence |
||
|
Mar 2 |
|
Review |
|
|
| Mar 12 | Midterm exam | (during Lab) | in Wachman 104 | |
|
Mar 14 |
9 |
9.1, 9.3, 9.6, 9.7, 9.14 (due Mar 23) |
||
|
Mar 16 |
|
Midterm exam discussion |
||
|
Mar 21 |
|
Midterm exam discussion |
|
|
|
Mar 23 |
10 |
10.1, 10.5, 10.8, 10.11, 10.15 (due Mar 28) |
M10.1 (due Mar 28) | |
|
Mar 28 |
12 |
Poisson Process 1,
Poisson Process 2 Poisson process |
12.2, 12.3, 12.4, 12.5, 12.7 (due Apr 4) |
|
|
Mar 30 |
13.4, 15 |
Law of Large Numbers Data analysis: graphical summaries |
--- |
Kernel Density Ex M15 (due Apr 6) |
|
Apr 4 |
16 |
Data analysis: numerical summaries |
--- |
M16 (due Apr 13) |
|
Apr 6 |
17 |
Basic statistical models |
17.1, 17.2, 17.4, 17.5 (due Apr 11) |
|
|
Apr 11 |
19 |
Unbiased estimators |
19.2, 19.3, 19.5 (due Apr 18) |
|
|
Apr 13 |
20 |
Efficiency and mean squared error |
20.1, 20.5 (due Apr 20) |
|
|
Apr 18 |
21 |
Maximum likelihood |
21.2, 21.9, 21.13 (due Apr 25) |
|
|
Apr 20 |
22 |
The method of least squares |
22.2, 22.3, 22.4, 22.8 (due Apr 27) |
|
| Apr 25 | Review | |||
|
Apr 27 |
|
Review |
|
|
|
May 9 |
|
Final Exam |
Time: 10:30-12:30 pm |
in TL 305A |