Statistical Learning II · Fall 2026
This guide contains the day-to-day procedures, resources, and practical expectations for the course. The official syllabus provides the formal course requirements; this guide is designed to help you navigate the semester successfully.
Tuesday & Thursday
11:00 AM – 12:15 PM
Tuesday & Thursday
9:15 – 10:45 AM
Edwin Duncan Hall
No appointment needed.
Individual meetings may be scheduled when regular office hours do not work.
The course is organized around five major areas:
R will be used throughout the semester for computation, modeling, diagnostics, visualization, and reproducible reporting.
Before class, review the assigned reading or posted notes. During class, work through examples and R code actively. After class, revisit the examples, complete assigned problems, and make sure you can explain both the statistical method and the R output.
Policies, grading, learning outcomes, required materials, and formal course expectations.
Weekly topics, readings, homework, quizzes, exams, projects, and University breaks.
Homework is intended to build fluency with the statistical ideas, calculations, interpretations, and R workflows introduced in class.
Quizzes are designed to encourage steady preparation rather than last-minute studying.
The midterm assesses the first major portion of the course. Expect questions that require both understanding of statistical concepts and interpretation of models or output.
The final examination is comprehensive and follows the University’s official examination schedule.
Specific exam instructions, permitted resources, and any proctoring requirements will be announced before each exam.
The project is an opportunity to bring together statistical modeling, R, interpretation, and professional communication.
Students will be expected to:
Detailed project instructions, data requirements, grading criteria, and submission procedures will be provided separately.
Students should become comfortable using R as part of the statistical reasoning process—not simply as a calculator.
Good practice includes:
Regular office hours are Tuesday and Thursday, 9:15–10:45 AM in Edwin Duncan Hall. No appointment is necessary during these hours.
Office hours are a good time to ask questions about concepts, review examples, discuss R code, clarify feedback, talk through a project idea, or get advice about study strategies.
Class meetings are an important part of the course because examples, interpretation, R demonstrations, quizzes, and announcements may occur during class.
If you miss class:
For religious observances, University-sponsored activities, extended medical circumstances, or emergency absences, follow the applicable University procedures and communicate as early as reasonably possible.
Generative AI tools can sometimes help explain general concepts or assist with debugging, but they can also produce incorrect statistical reasoning, fabricated references, or code that appears plausible while giving the wrong analysis.
Unless an assignment explicitly states otherwise:
Assignment-specific directions take precedence over this general guidance.
For each method, be able to answer:
This Course Guide supplements the official syllabus. If there is an unintended conflict between this guide and the official syllabus regarding grading percentages, formal course requirements, or University policy, the official syllabus and applicable University policy govern.
Operational instructions in this guide may be updated during the semester as course needs evolve. Significant changes will be communicated to students.