Introduction to STA 235
Lecture 0
Dr. Emre Yucel
2026-01-13
Course Goals
- Use regression in all its forms to build models for inference and for prediction
- Understand the benefits and limitations of the models we build
- Given a new business situation, select an appropriate analysis, carry it out, and effectively communicate the results
- This is a practical course!
Contact Information
Instructor: Emre Yucel, Ph.D.
- Office hours: W 5:00pm-6:00pm (Zoom) and Th 10:00am-11:00am (CBA 3.436)
- Email: emre.yucel@utexas.edu
- Phone: 832-408-1686
Course assistants:
- Lead course assistant: Paige Jansky
- You can also attend any other TA/CAs office hours
- If you have section-specific questions go to Paige
Statistical Computing
- We will use R for statistical analysis throughout the course
- We will access R through the RStudio graphical interface; make sure both are installed on your laptop and bring it to every class
- We will use posit Cloud for quizzes and exams, so you may want to familiarize yourself with it before hand, the good news is that it looks exactly like R Studio
Weekly Cadence
- Due by start of class on Tuesday: Perusall pre-class reading
- During class on Tuesday: Lecture, activities, practice topic
- Due by 11:59pm the following Monday: Homework covering topic
- Following Tuesday at the beginning of class: Checkpoint quiz on topic
Pre-Class Reading Assignments
- This course moves quickly—review material before class.
- Use Perusall to ask questions, help others, and share your insights.
- Your participation shapes class discussion and deepens your understanding.
- Aim for at least 3 thoughtful posts (questions, replies, or comments) per reading.
- Grading is based on effort and thoughtfulness; full effort earns full credit.
In-Class Practice
- Understanding the concepts really comes from practice
- Class will be synchronous and in person; class time will be divided between lecture and practice
- We will use Learning Catalytics so you can practice the concepts during class
- Graded on participation, not correctness; answer 75% of the questions to get 100% of the credit
Homework
- Homework due each week Monday at 11:59 pm
- Automatically graded, three attempts
- OK to work together, but try the problems on your own first for maximum benefit
- If you use AI, make sure that you’re understanding what its doing
Checkpoint Quizzes
- It is critical in this course to stay on top of things and not fall behind
- Checkpoint Quiz at the start of each class will help you ensure that you are really learning the material, and give you an early heads up if you aren’t
- You can use RStudio and an index card “cheat sheet” during quizzes (so don’t spend time memorizing anything!)
- Unit A has 7 Checkpoint Quizzes and Unit B has 6 Checkpoint Quizzes
- In each unit, we will drop the lowest quiz grade
Mastery Exams
Each unit concludes with a Mastery Exam:
- Unit A: March 9 or 10, 7-9 PM
- Unit B: April 30, makeup 8-10 AM, regular 12-2 PM
Grading Breakdown
| Assignment Type | Points per Item | Total Points |
|---|---|---|
| Pre-class Preparation | — | 44 |
| Class Participation | — | 86 |
| Homework | 15 | 195 |
| Checkpoint Quizzes | 25 | 275 |
| Mastery Exams | 200 | 400 |
| Total | 1,000 |
Getting Help
- My office hours
- CA/TA office hours
- Post questions about readings in Perusall (for questions about the reading)
- Post questions in group chats in Perusall (for general questions about the course, or homework questions)
- Email me directly (emre.yucel@utexas.edu) anytime, or text if urgent