QTM 100: Intro to Statistical Inference

Serving 160 Students with 4 Lab Graduate Instructors and 13 Undergraduate Learning Assistants

Selected Lecture Slides

• Midterm I Review

Syllabus

• Syllabus

• Course Objectives:

  How do we transform numbers and data into meaningful evidence and persuasive arguments? This course is designed to guide you on the first step of a lifelong journey toward mastering this skill. Our lecture series emphasizes core concepts, complemented by lab sessions (centered on practice and computational applications; required) and learning assistance sessions (focused on conceptual reinforcement and applications; recommended). The course is organized into three conceptual parts (see below). In the first three weeks, we will establish the foundations of inference and get tools for your initial data exploration. We will then step back to see the forest, thinking seriously about the idea of making claims about the world based on the handful of observed sample data we have. Depending on the nature of the data and what you aim to understand about the world, different statistical methods are required. By learning these different methods, when to apply which, and how to interpret results, we will reinforce the idea of ``inference” (transforming numbers into meaningful arguments) across various problem contexts.