ECON 311: Analytical Tools for Economists

Syllabus

Author
Affiliation

Dr. Alexander (Alex) Cardazzi

Old Dominion University

Course Information

Semester: Spring 2027, Pt. 1 (8-week accelerated session)

Delivery: Asynchronous online (there are no required meeting times)

Office Hours: Wednesday 03:00 PM - 05:00 PM (or by appointment)

Course Description

In this course, students will learn how to build statistical models and use data to answer real-world questions. Through hands-on work with modern statistical software, students will learn to visualize, estimate, interpret, evaluate, and articulate relationships in data. Students will obtain real, tangible skills such as experience with statistical software as well as develop their economic intuition.

ECON 311 is a hands-on introduction to data analysis for economists. You will learn to work with real datasets, summarize what you see, test hypotheses, and build regression models — all using R, a free and widely used statistical programming language. By the end of the course, you will be able to take a dataset you have never seen before, ask a meaningful question about it, and produce a clean, professional analysis. You will also learn to use AI tools thoughtfully as part of your analytical workflow, a skill that employers increasingly expect.

Prerequisites: ECON 202S or equivalent.

Textbooks and Other Materials

There is no required text for this course. All course materials are provided free on the course website.

Required Software

As this course focuses on the learning and application of econometric techniques, a computer or laptop (Mac, Windows, or Linux) with R and RStudio installed is required for this course. Students can obtain the latest version of R from r-project.org. Students can obtain a free version of RStudio from posit.co. While students can directly code in R, it is recommended that student use RStudio to facilitate interactions with the R language. RStudio also comes bundled with Quarto, which is what students will use to turn their work into .html reports. Students will also install numerous open source packages throughout the course.

Students are also required to use Claude Code, an AI coding assistant. Claude Code is not free; see the Artificial Intelligence section below for pricing and setup information.

Students need not install this software prior to beginning the course. Setup instructions are provided in Module 1.

Required Hardware

In addition to a computer, a microphone is required for the recorded presentations. A webcam is optional.

Course Learning Objectives

  1. Use R and RStudio to load, manipulate, and visualize real-world datasets.
  2. Compute and interpret descriptive statistics, probability concepts, and inferential tools including confidence intervals and hypothesis tests.
  3. Estimate, interpret, and communicate the results of linear regression models, including simple regression, multiple regression, and common extensions.
  4. Diagnose and explain common regression problems, including omitted variable bias and multicollinearity.
  5. Evaluate the limitations of correlational analysis and articulate conditions under which regression estimates do and do not support a causal interpretation.
  6. Communicate quantitative findings clearly in written and oral form, including appropriate acknowledgment of uncertainty and limitations.

Course Schedule

This is an accelerated, fully asynchronous course. Below is a schedule for course topics with corresponding assignments. Due dates are listed in the table; they are also listed under Grading Policy below. Within each module, you may work through the notes at your own pace, but plan to finish each module by the end of its week.

To help set expectations, Modules 1, 4, and 6 will likely be the most intensive modules. Module 1 requires a lot of set up, which can cause headaches for students. However, it is very important to get the software installed and working ASAP given the time constraints. Please reach out early if there are issues. Modules 4-6 are probably the most content-rich and represent discrete steps in knowledge.

Course Schedule
Module Dates Topics Lectures Assignment
Module 1 Jan 11 - Jan 17 R Bootcamp 1.1 - 1.8 Onboarding HW (40 pts), due Sun, Jan 17
Module 2 Jan 18 - Jan 24 Descriptive Statistics 2.1 - 2.3 -
Module 3 Jan 25 - Jan 31 Distributions, CLT, and Inference 3.1 - 3.3 HW 1 (90 pts), due Sun, Jan 31
Module 4 Feb 1 - Feb 7 Simple Regression 4.1 - 4.6 HW 2 (90 pts), due Sun, Feb 7
Module 5 Feb 8 - Feb 14 Multiple Regression and OVB 5.1 - 5.3 -
Module 6 Feb 15 - Feb 21 Dummies, Interactions, Categorical Variables, and Binary Outcomes 6.1 - 6.4 HW 3 (90 pts), due Sun, Feb 21
Module 7 Feb 22 - Feb 28 Fixed Effects and the Limits of Regression 7.1 - 7.4 -
Finals Week Mar 1 - Mar 5 - - HW 4 (90 pts), due Tue, Mar 2

Grading Policy

The evaluation for this course consists of an onboarding homework and four larger homework assignments, each of which includes a recorded presentation. The final grade is comprised of the following elements. All assignments are due at 11:59 p.m. on the date listed.

Assignment Point Totals and Due Dates
Assignment Points Due
Onboarding HW 40 Sun, Jan 17
HW 1 90 Sun, Jan 31
HW 2 90 Sun, Feb 7
HW 3 90 Sun, Feb 21
HW 4 90 Tue, Mar 2
Total 400


Grades will be determined by the sum of points earned, and then converted using this table:

Final Grade Conversion Table
Letter Minimum Maximum
A 372 400
A- 360 371
B+ 348 359
B 332 347
B- 320 331
C+ 308 319
C 292 307
C- 280 291
D+ 268 279
D 252 267
D- 240 251
F 0 239

Except for grades of “Incomplete”, all grades are considered final when reported by a faculty member at the end of a semester. A change in grade may only be requested when a calculation, clerical, administrative, or recording error is discovered in the original assignment of a course grade or when a decision is made by the faculty member to change the course grade because of the disputed academic evaluation procedures.

Grade changes necessitated by a calculation, administrative, or recording error must be reported within a period of six months from the time the grade is awarded. No grade may be changed as the result of a re-evaluation of a student’s work or the submission of supplemental work following the close of a semester.

Onboarding Homework

The Onboarding HW verifies that you have your technical environment configured and can produce and submit course deliverables before substantive graded work begins. It is more about logistics rather than content. You will install R and RStudio, install Claude Code and initialize it with the course’s CLAUDE.md, set up a student ePortfolio account, render the provided Quarto template to .html, and upload it to your ePortfolio. You will submit a link to your ePortfolio via Canvas. The Onboarding HW is graded on completion: full credit for a submission that renders correctly and shows evidence of completing all steps, partial credit for a good-faith attempt with rendering issues, and zero for non-submission.

Homework

Each of the four homework assignments provides a pre-supplied dataset (or datasets) and a set of open-ended research questions. You will conduct full analyses in R, render all output to .html using Quarto, and submit the rendered file on Canvas along with a recorded presentation (described below). The .html file must render without error, include properly labeled figures, and contain a Process Reflection section. Please see the following sections about Artificial Intelligence for additional details on how AI fits into the homework.

Each homework is worth 90 points: a 60-point written report and a 30-point recorded presentation.

  • Written report (60 points): the report is scored on four dimensions, each worth 15 points: Presentation (the .html renders cleanly, figures are labeled, code is organized, and the document is structured), Analysis (the approach is appropriate for the question, uses methods from the course material, and documents and explains any out-of-scope methods in simple terms), Interpretation (results are interpreted correctly in plain language, with direction, magnitude, and units stated and limitations acknowledged), and Reflection (the Process Reflection shows specific, iterative engagement with Claude, describing what was tried, changed, and why).
  • Recorded presentation (30 points): you will record yourself presenting your work, as if you were presenting to relevant stakeholders (e.g., your boss, clients, investors, etc.). The presentation itself is worth 20 points (graded on content and articulation), and 10 points come from answering two questions asked by Claude, playing the role of the audience (5 points each). You need not submit the materials you use for your presentation, since you will submit a recording.

The full rubric is posted on the course website.

ePortfolio

In an effort to help students reflect on and synthesize their learning experiences, as well as demonstrate their skills to potential employers, certain courses taught by faculty in the Economics department will require the creation of, or addition to, an ePortfolio. Given the status of this course as the capstone of the Economics major, this course will contain an ePortfolio component.

Your Onboarding HW asks you to set up an ePortfolio and upload a rendered document to it. Beyond that, the extent to which students use their ePortfolio is ultimately up to them, but adding your homework reports to it should help to differentiate you from competing job seekers. As a note, all material generated in this course will be portable .html files that can easily be uploaded to ePortfolios.

Online ePortfolio resources for ODU students can be found at odu.edu/asis/eportfolio.

Disclaimer: this course incorporates various online software and other technologies. Some technologies require you to either create an account on an external site or develop assignment content using them. The content, as well as your name/username or other personally identifying information may be publicly available as a result. While the purpose of these assignments is to engage with technology as a means for representing the content we are covering in class, please see me for an alternative activity if you object to potentially sharing your account, name, or other content you create in these technologies.

Incomplete Grades

A grade of “I” indicates assigned work yet to be completed in a given course, or absence from the final examination, and is assigned only upon instructor approval of a student request. The “I” grade may be awarded only in exceptional circumstances beyond the student’s control. The “I” grade becomes an “F” if not removed by the day grades are due for following term based on specific criteria: Incomplete, Withdraws and Z grades.

Expectations

What you can expect from me, as your instructor:

  • I will ensure the accuracy of the course content.
  • I will provide clear guidelines regarding the course assignments.
  • I will be available to answer your questions.
  • I will provide meaningful feedback in a timely manner.
  • I will do my best to create a worthwhile learning experience for all my students.
  • I will follow fair and clear evaluation guidelines for all the course assignments.
  • I will do my best to prepare you for assignments.

What I expect from you, as my student:

  • You will complete all course readings and assignments in a timely manner.
  • You will follow the ODU honor pledge.
  • You will follow proper “netiquette.”
  • You will interact with your faculty and classmates professionally and respectfully.
  • You will avoid sarcasm and inappropriate language, including the use of ALL CAPS.
  • You will acknowledge your classmates’ ideas and build on them to contribute to the discussion.

Course Policies

Communication

Students should feel welcome to contact me via email (acardazz@odu.edu). Generally, I respond to well-crafted emails within 48 business hours. I have an ‘open door’ policy for student questions and strongly encourage students to communicate with me. Of course, since this is an online course, I will be available over Zoom as well.

Students should take the time to craft complete, professional emails. The more information that you can provide about a question or problem, the more likely that my response will be helpful. Avoid non-professional language and practice communicating in the corporate workplace. Emails that are unprofessional will be returned with no action. There are many guides on how to compose a professional email which you can easily find online.

Attendance and Participation

This is a fully asynchronous course with no required meeting times. Students are expected to engage with the lecture materials and complete assignments within the designated windows each week. There are no attendance points or participation grades, as students are responsible for their own learning. Students are expected to monitor Canvas regularly for announcements.

Late Assignments

All due dates are firm. Late submissions of any assignment will receive a score of zero unless discussed at least forty-eight hours prior to the deadline. Special circumstances that are communicated in advanced will be handled on a case by case basis.

Plagiarism

Plagiarism and turning in work that is not yours is grounds for being assigned a zero on an assignment, is a violation of the University Honor Code, and could result in failure in the course and/or academic action by the university.

Artificial Intelligence

You are currently enrolled in a course you can think of as a simultaneous introduction to econometrics, the programming language R, and (properly using) AI tools. You are enrolled in said course during a time in which artificial intelligence (AI) is booming. It is quite possible that AI will end up being the most transformative technology since the internet, and ignoring it would be foolish. As we get started in this course, I want to provide a few additional thoughts on AI and its use in this course.

As I am sure you understand by now, education is having to rapidly adjust to AI. This means that much of what previously worked, especially with regards to assessment, no longer does. In a fully asynchronous course, I cannot (and will not try to) police what you do on your own computer. At this point, the only path forward is to embrace the idea that AI will forever be part of the “toolkit,” much like how calculators, spell-check, and search engines are. Therefore, Claude Code is a required tool in this course, and thoughtful AI use is expected. Reckless AI use, much like reckless use of other tools (e.g., copying and pasting text from Wikipedia and claiming it as your own), is not.

What constitutes appropriate AI use? Appropriate AI use is collaborative rather than substitutive. Using AI to help you understand why your code is not working is appropriate (collaborative), but having it write an entire analysis for you, which you then submit with no meaningful engagement, is not (substitutive). When determining the appropriateness of their AI use, students may find it helpful to ask themselves whether someone with no training, but access to an AI, could have produced the same thing. If the answer is yes, then the student has not added any value, and their AI use was substitutive. Students may also frame this question through the lens of employment and ask themselves whether someone would hire them for what they produced, or if this is instead something an AI could produce (for much a lower cost)? Companies are chomping at the bit to cut labor costs by replacing employees with AI; do something that makes this a difficult decision for them.

Rather than policing AI use, the course is designed so that the work itself shows whether you understand it. There are two mechanisms for this:

  • Process Reflection. Claude Code does not produce shareable conversation transcripts. Instead, every homework must include a Process Reflection section, written by you, that answers: What did you try first? What did Claude suggest? What did you change, and why? Where did you push back on the AI’s suggestion? (Claude Code is not allowed to write this section, and it knows that.)
  • Recorded presentation. Every homework also requires a recorded presentation of your work, followed by two questions from Claude playing the role of your audience. Students who cannot explain their work intuitively will receive reduced scores.

Note that I reserve the right to request a short individual meeting with any student to discuss a submitted assignment. This is entirely at my discretion; it is not scheduled, not guaranteed for any student, and not a graded assignment in its own right. It is most likely to be invoked when a submission reads as though it relies too heavily on AI output rather than the student’s own understanding. In that meeting, the student will be asked to explain their work and answer follow-up questions. A student who cannot do so satisfactorily should expect their grade on the underlying assignment to be revised accordingly.

Directions for using AI

Claude Code requires a paid subscription or per-token billing. Plans are currently available at $20/month, $100/month, or $200/month, and per-token billing is also an option. The $20/month plan is a reasonable starting point (and what I personally subscribe to); if you find that you need more capacity, you can upgrade at your own discretion.

Students will use Claude Code with a starter CLAUDE.md file provided on the course website. This file initializes Claude Code with the constraints of this course. For example, it tells Claude to use the same (base R) approach as the course notes, and to help you without doing your thinking for you. It also has Claude keep session notes in an ai_logs/ folder, which are for your own use (so Claude remembers what’s going on between sessions) and are not submitted. Setup instructions are in Module 1.

For example, consider the following good and bad uses of AI:

I am working on a homework problem and my regression keeps returning NA for one of my coefficients. Here is my code and the error. Can you help me figure out what is going on?

or

I have been able to clean the data and estimate my model, but I’m not sure how to interpret the coefficient on my interaction term. Can you talk through it with me, and then I will write up the interpretation myself?

Here is my homework. Please do the whole analysis and write it up for me.

or

Please write my Process Reflection.

Steps to Set Up Claude Code

  1. Follow the instructions in Module 1.4 to install Claude Code.
  2. Download the starter CLAUDE.md file from the course website and save it in your course folder.
  3. Optionally, save the course notes (each lecture page has a plain-text .md version) in a notes/ folder inside your course folder so that Claude Code can read them.

Course Disclaimer

The course schedule and activities are subject to change. Changes will be posted as Announcements in Canvas. All instructional materials and homework assignments can be found here.

University Policies

Code of Student Conduct and Academic Integrity

The Office of Student Accountability & Academic Integrity (OSAAI) oversees the administration of the student conduct system, as outlined in the Code of Student Conduct. Old Dominion University is committed to fostering an environment that is: safe and secure, inclusive, and conducive to academic integrity, student engagement, and student success. The University expects students and student organizations/groups to uphold and abide by standards included in the Code of Student Conduct. These standards are embodied within a set of core values that include personal and academic integrity, fairness, respect, community, and responsibility.

Honor Pledge

By attending Old Dominion University, you have accepted the responsibility to abide by the Honor Pledge:

I pledge to support the Honor System of Old Dominion University. I will refrain from any form of academic dishonesty or deception, such as cheating or plagiarism. I am aware that as a member of the academic community it is my responsibility to turn in all suspected violations of the Honor Code. I will report to a hearing if summoned.

Discrimination Policy

The purpose of this policy is to establish uniform guidelines to promote a work and education environment that is free from harassment and discrimination, as defined below, and to affirm the University’s commitment to foster an environment that emphasizes the dignity and worth of every member of the Old Dominion University community. The Discrimination Policy details the process to address complaints or reports of retaliation, as defined by this policy.

Diversity and Inclusion

The Division of Student & Campus Life values the uniqueness of our Monarch community. The word “engagement” reflects our commitment to embrace the differences in our cultural backgrounds, perceptions, beliefs, traditions, world views, socio-economic status, cognitive and physical abilities.

We will strive to serve as the pre-eminent model for engaging every student to achieve their own success. Our core values are fueled by our responsibility and actions toward community development and engagement, cultural competence and understanding, physical and mental wellness and inclusion for every member of ODU. We will embrace our greatest strength - the diverse composition of our student body and workforce. For more information regarding diversity and inclusion, please visit the Office of Intercultural Relations.

Educational Accessibility and Accommodations

Old Dominion University is committed to ensuring equal access to all qualified students with disabilities in accordance with the Americans with Disabilities Act. The Office of Educational Accessibility (OEA) is the campus office that works with students who have disabilities to provide and/or arrange reasonable accommodations.

The Accommodations for Students with Disabilities define the procedures used to accommodate student with disabilities. Students are encouraged to self-disclose disabilities that the Office of Educational Accessibility has verified by providing Accommodation Letters to their instructors early in the semester in order to start receiving accommodations. Accommodations will not be made until the Accommodation Letters are provided to instructors each semester

University Email Policy

With the increasing reliance and acceptance of electronic communication, email is considered an official means for University communication. Old Dominion University provides each student an email account for the purposes of teaching and learning, research, administration, and service. It is the responsibility of every eligible student to activate MIDAS, the Monarch Identification and Authorization System, to obtain email access. It is important that all students are aware of the expectations associated with email use as outlined in the Student Email Standard. The email account provided by the University is considered to be an official point of contact for correspondence. Students are expected to check their official e-mail account on a frequent and consistent basis in order to stay current with University communications. Mail sent to the ODU email address may include notification of University-related actions, including academic, financial, and disciplinary actions. For more information about student email, please visit Student Computing.

Withdrawal

A syllabus constitutes an agreement between the student and the course instructor about course requirements. Participation in this course indicates your acceptance of its teaching focus, requirements, and policies. Please review the syllabus and the course requirements as soon as possible. If you believe that the nature of this course does not meet your interests, needs or expectations, if you are not prepared for the amount of work involved – or if you anticipate assignment deadlines or abiding by the course policies will constitute an unacceptable hardship for you – you should drop the course by the drop/add deadline, which is listed in the ODU Academic Calendar. For more information, please visit the Office of the University Registrar.

Privacy of Student Information

Old Dominion University recognizes its duty to uphold the public’s trust and confidence, not only in following laws and regulations, but in following high standards of ethical behavior. Members of the Old Dominion University community are responsible for maintaining the highest ethical standards and principles of integrity. The Code of Ethics is a set of values-based statements that demonstrate the University’s commitment to this goal. The Privacy of Student Information details Family Educational Rights & Privacy Act (FERPA), along with other information regarding privacy.

Other Academic Policies

Please see the following link for other academic policies at the university level: https://catalog.odu.edu/undergraduate/policies/academic-policies/