Welcome to ECON 2250!

Week 2

Maghfira Ramadhani

Aug 25, 2025

Welcome!

Meet Afi Ramadhani!

  • Education and career journey
    • BS and MS in Petroleum Engineering from Institute of Technology Bandung (QS Top 100 in Petroleum Engineering)
    • Academic professional and consultant for think tanks in Indonesia
    • MS in Economics from Georgia Tech
    • PhD Candidate in Economics at Georgia Tech
  • I am an energy and environmental economist interested in examining the broad impact of climate change and energy transition 🙂
  • You call me Afi or Professor or Prof. Afi or Prof. Ramadhani (no Dr yet)

Meet the Teaching Assistant (TA)!

  • Rohit Borah: Head TA

Short Survey

Scan the QR code to fill out the survey!

https://gatech.instructure.com/courses/482358/quizzes/717963


Topics

  • Introduction to the course

  • Syllabus activity

  • Reproducibility

What is Statisics?

Statistics is the branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. It helps us understand and describe patterns, relationships, and trends in data, and it allows us to make informed decisions in the presence of uncertainty

Source: ChatGPT (with modification)

What is Statisics?

There are two main branches of statistics:

  1. Descriptive Statistics – Summarizes and organizes data using measures

  2. Inferential Statistics – Makes inference or generalizations about a population based on a sample of data.

Source: ChatGPT (with modification)

Statistics in practices

Statistics in practice: Economics

Economic measurement

  • GDP = C + I + G + (X - M), Unemployment rate, Inflation, Consumer Spending, Median Income, Demographics

  • Typical process to publish these data:

  1. Collect data from sample of population (Census, Survey, etc),

  2. Create a measure from processing the data and then publish data,

  3. Economist use the data to inform policy making

Statistics in practice: Economics

Testing Economic Theory

  • Do men and women have the same average wage?

  • Do people with college degrees earn more than those without?

  • Does increasing minimum wage reduce employment?

  • Are poverty rates the same in urbal and rural areas?

  • Does conservation nudge reduce electricity consumption?

Statistics in practice: Data Science

Source: R for Data Science with additions from The Art of Statistics: How to Learn from Data.

ECON2250

What is ECON2250?


MATH

Probability theory

+

DATA

Sampling and uncertainty

=

STATISTICS

Descriptive and Inference


Course goal: As student’s first statistics course, will equip students with fundamental concepts and tools for more advanced course, e.g. Econometrics, Data Science, or Machine Learning

Prerequisites: No prerequisites, but some math course are encouraged, e.g. MATH1551 and MATH1552

Course learning objectives

By the end of the semester, you will be able to…

  • master the language and the fundamental concepts of probability theory.
  • apply basic statistical inference techniques in empirical research settings with an understanding of their utility and limitations.
  • implement a reproducible workflow using R for statistical analysis and simulation, Quarto to write reports and GitHub for version control and collaboration.
  • understand data in economics/social science, including data types, data generating processes, data analysis, and how to communicate with data (data literacy).
  • acquire foundation knowledge of statistical concepts to prepare for more advanced data analysis or econometrics courses.

Course topics

Probability

  • Probability
  • Conditional probability and independence
  • Counting methods
  • Random variables
  • Expectation and moments

Data and Distributions

  • Data and sampling
  • Descriptive statistics
  • Discrete random variables
  • Continuous random variables

Statistical Inference

  • Sampling distributions
  • Estimation and confidence intervals
  • Hypothesis testing
  • Multiple hypothesis testing
  • Simple linear regression
  • Advanced topics (optional)

General topics

  • Computing using R and GitHub
  • Presenting statistical results
  • Collaboration and teamwork

Course overview

Course toolkit

Hardware requirement

Computing toolkit

RStudio logo

  • All analyses using R, a statistical programming language

  • Write reproducible reports in Quarto

  • Access RStudio through IAC VLab

GitHub logo

Classroom community

It is my intent that students from all diverse backgrounds and perspectives be well-served by this course, that students’ learning needs be addressed both in and out of class, and that the diversity that the students bring to this class be viewed as a resource, strength and benefit.

  • If you have a name that differs from those that appear in your official Tech records, please let me know.

  • Please let me know your preferred pronouns, if you are comfortable sharing.

  • If you feel like your performance in the class is being impacted by your experiences outside of class, please don’t hesitate to come and talk with me. If you prefer to speak with someone outside of the course, your advisers and deans are excellent resources.

  • I (like many people) am still in the process of learning about diverse perspectives and identities. If something was said or done in class (by anyone) that made you feel uncomfortable, please talk to me about it.

Accessibility

  • The Office of Disability Services (ODS) is available to ensure that students are able to engage with their courses and related assignments.

  • If you have documented accommodations from ODS, please send the documentation as soon as possible.

  • I am committed to making all course activities and materials accessible. If any course component is not accessible to you in any way, please don’t hesitate to let me know.

Syllabus activity

  1. Read the portion of the syllabus assigned to your group.
  2. Discuss the key points and questions you my have with your neighbors.
  3. We’ll ask for volunteers to share a summary with the class.

Syllabus activity assignments

Syllabus activity report out

Grading

Category Percentage
Homework 15%
Final project 20%
Lab 15%
Exams (2 midterms) 40%
Participation (AEs + Teamwork) 10%
Total 100%

Five tips for success in ECON2250

  1. Complete all the preparation work before class.

  2. Ask questions in class, office hours, and on Ed Discussion.

  3. Do the homework and labs; get started on homework early when possible.

  4. Don’t procrastinate and don’t let a week pass by with lingering questions.

  5. Stay up-to-date on announcements on Ed Discussion and sent via email.

Questions?

Reproducible workflow

The perils of bad data cleaning

Published in the American Economic Review (2007):

Adapted from Nick Hagerty’s Course Materials

The perils of bad data cleaning

DG’s baseline climate measure (dd89_7000) has a value of zero degree days for 163 counties. If correct, this measure implies temperatures do not exceed 8°C (46.4°F) in those counties during the growing season of April through September. Temperatures this low would seem implausible in any state, yet many of these counties are in warm southern states such as Texas.

The perils of bad data cleaning

The perils of bad data cleaning

Contrary to the results in DG (2007), the corrected data suggest that an immediate shift to the projected end-of-the-century climate would reduce agricultural profits.

Another example

  • Originally reported “the intervention, compared with usual care, resulted in a fewer number of mean COPD-related hospitalizations and emergency department visits at 6 months per participant.”

  • There were actually more COPD-related hospitalizations and emergency department visits in the intervention group compared to the control group

  • Mixed up the intervention vs. control group using “0/1” coding

Transparency and reproducibility

Avoiding errors is only the first step. It’s also critical to make your work reproducible.

In the private sector, the benefits may be more obvious.

  • Your code has to work together with other people’s code.

  • Eventually, someone else will take over your code.

In academic research, it’s equally important.

  • To trust the results – many research findings fail to replicate.

  • To build on your work and collaborate with others.

  • Many journals now require a full “replication package” of data and code.

  • The push for transparency and reproducibility is known as the open science movement.

Reproducibility: Can someone else run your code and get the exact same results?

Replication: If another analyst attempts the same question, do they get the same answer?

Transparency: Can everyone see what choices you made and how you got your results?

Reproducibility checklist

What does it mean for an analysis to be reproducible?

Near term goals:

✔️ Can the tables and figures be exactly reproduced from the code and data?

✔️ Does the code actually do what you think it does?

✔️ In addition to what was done, is it clear why it was done?

Long term goals:

✔️ Can the code be used for other data?

✔️ Can you extend the code to do other things?

Toolkit

  • Scriptability \(\rightarrow\) R

  • Literate programming (code, narrative, output in one place) \(\rightarrow\) Quarto

  • Version control \(\rightarrow\) Git / GitHub

R and RStudio

  • R is a statistical programming language

  • RStudio is a convenient interface for R (an integrated development environment, IDE)

Source: Statistical Inference via Data Science

RStudio IDE

Quarto

  • Fully reproducible reports – the analysis is run from the beginning each time you render

  • Code goes in chunks and narrative goes outside of chunks

  • Visual editor to make document editing experience similar to a word processor (Google docs, Word, Pages, etc.)

Quarto

How will we use Quarto?

  • Every application exercise and assignment is written in a Quarto document

  • You’ll have a template Quarto document to start with

  • The amount of scaffolding in the template will decrease over the semester

Version control with git and GitHub

What is versioning?


What is versioning?

with human readable messages

Why do we need version control?

Provides a clear record of how the analysis methods evolved. This makes analysis auditable and thus more trustworthy and reliable. (Ostblom and Timbers 2022)

git and GitHub

  • git is a version control system – like “Track Changes” features from Microsoft Word.
  • GitHub is the home for your git-based projects on the internet (like DropBox but much better).
  • There are a lot of git commands and very few people know them all. 99% of the time you will use git to add, commit, push, and pull.

Caveat

Image from xkcd (source)

Before next class

  • Complete JA Chapter 4

  • Review the updated syllabus

  • Office hours start today, Monday, August 25 (5-6pm)

Reference

ECON2250 Statistics for Economics - Fall 2025 - Maghfira Ramadhani

Ostblom, Joel, and Tiffany Timbers. 2022. “Opinionated Practices for Teaching Reproducibility: Motivation, Guided Instruction and Practice.” Journal of Statistics and Data Science Education 30 (3): 241–50. https://doi.org/10.1080/26939169.2022.2074922.