Course Description
Statistical methods requiring significant computing or specialized software. Simulation, randomization, bootstrap, Monte Carlo techniques; numerical optimization. Extensive computer programming involved. This course does not cover the use of statistical software packages.
Syllabus
Student Learning Outcomes, Goals, Objectives:
Students successfully completing this course will be able to:
- Write efficient transparent programs in R to perform routine statistical analyses
- Produce clear and effective graphical descriptions of data
- Generate random variables and conduct statistical simulations
- Apply Monte Carlo methods for numerical integration and statistical inference.
- Design simulation studies to evaluate statistical methods.
- Use and apply nonparametric methods.
Course Grading Information:
| Activity/Performance Measure | Percentage/Points |
|---|---|
| Assignments | 60% |
| Final Project | 40% |
Grading Scale
| Percentage | Letter Grade |
|---|---|
| 93-100 | A |
| 90-92 | A- |
| 87-89 | B+ |
| 83-86 | B |
| 80-82 | B- |
| 77-79 | C+ |
| 73-76 | C |
| 70-72 | C- |
| 67-69 | D+ |
| 63-66 | D |
| 60-62 | D- |
| 0-59 | F |
Attendance/Participation:
Attendance/participation is not a graded component included in the student's final grade for this course.
Course Materials Purchased by the Students:
No course materials are required.
Scholarly Perspectives
This course engages diverse scholarly perspectives to develop critical thinking, analysis, and debate and inclusion of a reading does not imply endorsement.