Course Syllabus
Contact Information
Course Details
For a list of course activities, scroll to the bottom of this page.
Course Delivery
This course is designed to be multi-modal. While participation in live sessions via Zoom is op- tional, these sessions will be offered for those interested. Otherwise, the course can be completed entirely asynchronously, with all materials accessible online. Please note, however, that there will still be specific deadlines to follow.
Course Prerequisites
The course is open to all students in the program, with no prerequisites. However, a basic understanding of college algebra, computer skills, and some quantitative experience would be beneficial. If you would like to refresh your algebra skills, I recommend this short course on Khan Academy.
Course Materials
Required Textbook
Walpole, R., Myers, R., Myers, S. & Ye, K. (2019). Probability Statistics for Engineers Scientists, 9th ed. Boston: Pearson.
Supplementary Materials
The following articles, books, and notes are also very useful references for a wide range of material.
- Michael Bailey (2019), real econometrics, 2nd Edition. Oxford University Press.
- Jeffrey M. Woodridge (2019). Introductory Econometrics: A Modern Approach by Jeffrey M. Wooldridge
- Jay L. Devore (2015). Probability and Statistics for Engineering and the Science. 9th edition
- Rossant, Cyrille, (2015). Learning IPython for Interactive Computing and Data Visualization, Second Edition. Packt Publishing.
- Has a 20-page long crash course on Python
- Tufte, Edward R., (2006). Beautiful Evidence. Cheshire, CT: Graphics Press.
- NumPy User Guide
- Pandas User Guide
- Matplotlib Examples
Program Learning Outcomes (PLO)
-
Use econometrics models to analyze complex economic phenomena, providing valuable insights for decision-making and policy formulation in various domains, such as economics, finance, and social sciences.
-
Use advanced econometric methods to solve real-world economic problems and make evidence-based decisions across various domains based on advanced statistical models, to improve outcomes for individuals and society.
- Convert intricate data into easily comprehensible visuals for effective data analysis, improving decision-making, and communicating insights to mass audiences.
-
Effectively communicate research findings through written reports, presentations, and visuals to ensure that both specialized audiences and the general public understand the research.
Course Learning Outcomes (CLO) Alignment
| CLO | Description | PLO |
|---|---|---|
| A |
Apply probability, random variables, and distribution functions to analyze and understand fundamental statistical concepts |
1,2,3 |
| B | Explain and analyze joint probability distributions and statistical inference to evaluate meaningful measures and solve statistical problems | 1,2,3 |
| C | Understand and apply fundamental statistical models by analyzing point estimation and evaluating hypothesis testing. | 1,2 |
| D | Analyze causal reasoning and apply Linear Regression Models | 1,2,3 |
| E | Apply multiple linear regression models and evaluate model misspecification | 1,2,3 |
| F | Apply methods for selection based on unobservables to analyze and evaluate data | 1,2,3 |
Grades
Breakdown
| Quizzes | Out of 14, the lowest 4 will be dropped. | 40% |
|---|---|---|
| Midterm Exam | 30% | |
| Final Exam | 30% | |
| Total | 100% |
Scale
| Percentage | Letter Grade |
|---|---|
| 90–100% | A |
| 80–89.99% | B |
| 70–79.99% | C |
| 60–69.99% | D |
| Below 60% | F |
Course Components
Quizzes
Every week, you will have a homework assignment which you need to submit through Canvas. Each problem set which will:
- Cover current study materials
- Assess your mastery of the course content
- Contain only multiple-choice questions
- Provide instant feedback and grades
- Evaluate your performance on Course Learning Outcomes B & C
This approach ensures regular practice and timely self-assessment throughout the course.
Live Sessions
Each week, we will have optional synchronous Live Sessions on Zoom. The Zoom Live Sessions offer:
- Q&A time
- Workshops
- Interactive discussions on course concepts
- Opportunities to engage with instructor and peers
These sessions provide valuable real-time interaction to supplement your learning.
Exams
You will have 2 exams: a midterm and a final. Exam format:
- Open-book and open-note
- Questions randomly selected from module topics
- Unique question set for each student
These exams will assess Course Learning Outcomes A, B, & D.
Course Policies
Communication
Communication will primarily occur via email at pallab.ghosh@ou.edu and through Zoom. I will respond to all student emails within 24 to 48 hours, unless otherwise notified of a delay. To help expedite communication, please include the course prefix and number in the subject line (e.g., ECON 5213: Question about the Syllabus).
You may address me as Professor Ghosh. This formality helps maintain a respectful and pro- fessional atmosphere. As your instructor, my primary role is to facilitate your learning and ensure you grasp the mathematical concepts and their applications in economics. I will guide
you through the course material, provide feedback on your assignments, and be available to answer your questions and support you throughout the course.
Please feel free to reach out via email or during office hours if you have questions or need clarifi- cation on the material. I encourage open communication, so don’t hesitate to voice any concerns or seek additional support. Active participation in live sessions, discussion boards, and group activities is essential. Engage with the material, ask questions, and contribute to discussions, as your insights can enhance everyone’s learning experience. Additionally, providing constructive feedback to your peers during discussions fosters a collaborative learning environment where everyone can grow and improve.
Feedback
Feedback and grading timeline:
- Within one week of submission deadline
- Includes detailed, constructive feedback
- Aims to improve understanding and performance
This prompt turnaround enables you to quickly learn from your work and apply insights to future assignments.
Late Policy
Late work will not be accepted except for exceptional circumstances - family bereavement, sick- ness, etc. All work will be given a zero if not submitted on time.
Plagiarism
Plagiarism is the most prevalent form of academic misconduct at OU. No college-level assignment can be satisfactorily completed through copying. OU assumes that all written assignments reflect the student’s own understanding, expressed in their own words. This means that all writing assignments, whether completed in class or outside of it, should consist entirely of words generated by the student, unless other authors’ words are properly cited.
Incorporating others’ words into your work can be beneficial when done honestly and correctly; failure to do so constitutes plagiarism.
For more information about plagiarism, watch the OU Libraries Plagiarism Tutorial video and take the short Avoiding Plagiarism Tutorial Canvas course offered by University Libraries.
University Academic Policies and Student Support
Access the University Academic Policies Document.
Course Summary:
| Date | Details | Due |
|---|---|---|