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?

Before next class

  • Complete JA Chapter 4

  • Review the updated syllabus

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

Reference