Math 313: Probability and Statistics
| Instructor: |
Neil Martinsen-Burrell
SC 363
319-352-8420
nmb@wartburg.edu
|
| Text: |
Wackerly, Mendenhall, Scheaffer, Mathematical
Statistics with Applications, Seventh Edition. |
| Class Meets: |
SC 128, MWF 2:30-3:35 |
| Office hours: |
MWF 12-1, T 2:45-3:45 |
| Syllabus |
News
Project
You will study in pairs a particular item of statistical interest that we
have not covered in this course. You will write a report on what you find,
including your sources and any significant results as well as give a 5
minute group presentation about your topic in class. Reports will be due
Wednesday, December 10 by 5:00 pm and presentations will be in class on
December 8 and 10.
Here are some ideas for possible projects:
- Statistical Graphics
- There has been a lot written about the display of statistical
information by people such as Edward Tufte and Darrell Huff
- Two-way ANOVA
- We will not talk about it in class, but there is an ANOVA for
comparing two different sets of criteria.
- Non-parametric Statistics
- Our statistical tests require that things have specified
distributions, usually normal. What does one do if that isn't the
case?
- Statistics in Sports
- Statistics and sports have a long association. Interesting topics
include predicting winners and analyzing past performance data.
- U.S. Census
- The U.S. Census is a tremendous statistical undertaking. What is it
and what are some of the techniques that they use?
- Polling
- As we enter another election cycle, people pay lots of attention to
who is where in the polls. How are polls done and how are they
interpreted?
- Random Number Generation
- How do people draw random numbers from a specified distribution?
- Random Walks
- What is a random walk and why do we care?
- Markov Chains
- What is a Markov chain and why do we care?
- Monte Carlo Simulation
- Clever gambling or just a way to compute complicated integrals?
- Bayesian Statistics
- Bayes Rule can be applied to estimate quantities. What is a prior? A
posterior? Who cares?
- Multivariate Statistics
- What is covariance? Multivariate normal distribution? Other
multivariate distributions? Linear Algebra?
- Chaos and Predictability
- While not strictly statistical, chaos leads to questions of randomness
and estimating error.
Homework assignments
- HW 1 (Due 9/10)
- pp. 6-7: 1.4, 1.6
- pp. 11-12: 1.7, 1.8, 1.9, 1.10, 1.17, 1.18
- HW 2 (Due 9/15)
- p. 32-35: 2.9, 2.10, 2.16, 2.21
- pp. 39-40: 2.30
- pp. 47-49: 2.41, 2.43, 2.50, 2.54, 2.63
- pp. 55-56: 2.72, 2.75, 2.78
- HW 3 (Due 9/24)
- pp. 59-61: 2.90, 2.93, 2.97, 2.103
- pp. 68-69: 2.110, 2.116, 2.117
- pp. 73-74: 2.124, 2.125, 2.132, 2.138
- pp. 80: 2.146, 2.153
- HW 4 (Due 10/1)
- pp. 90-91: 3.4, 3.11
- pp. 98-100: 3.14, 3.15, 3.33
- pp. 110-113: 3.37, 3.61
- pp. 119-120: 3.73, 3.75
- p. 124: 3.96, 3.97
- p. 130: 3.113, 3.118
- p. 137: 3.135, 3.137
- HW 5 (Due 10/8)
- p. 142: 3.145, 3.146, 3.151
- pp. 166-168: 4.3, 4.14, 4.15
- pp. 172-174: 4.24, 4.30, 4.37
- HW 6 (Due 10/22)
- pp. 177-178: 4.45, 4.54, 4.55
- pp. 182-184: 4.59, 4.64a, 4.71, 4.79
- pp. 190-193: 4.88, 4.89, 4.95, 4.108
- pp. 198-199: 4.123, 4.128
- p. 206: 4.136, 4.140
- HW 7 (Due 11/12)
- pp. 365-366: 7.13, 7.14, 7.21
- pp. 374-375: 7.42, 7.43, 7.52
- p. 384: 7.73, 7.74
- pp. 394-396: 8.1, 8.4, 8.12, 8.17
- p. 405: 8.32
- p. 409: 8.40
- HW 8 (Due 11/21)
- pp. 417-419: 8.56, 8.60, 8.63, 8.64
- p. 424: 8.72, 8.74
- pp. 431-434: 8.82, 8.90, 8.94
- p. 436: 8.95, 8.97, 8.99, 8.100
- p. 495: 10.6
- pp. 504-: 10.18, 10.20, 10.23, 10.25, 10.33
- HW 9 (Due 12/5)
- p. 512: 10.45, 10.46
- pp. 516-517: 10.50, 10.54, 10.56
- pp. 526-530: 10.65, 10.71, 10.76
- pp. 539-540: 10.83, 10.86
- HW 10 (Due NEVER)
- pp. 572-575: 11.1, 11.5, 11.10
- p. 583: 11.16, 11.19
- pp. 673-677: 13.8, 13.15
