STAT 356
Time Series Analysis Spring 2021
Division III Quantative/Formal Reasoning
This is not the current course catalog

Class Details

Time series — data collected over time — crop up in applications from economics to engineering to transit. But because the observations are generally not independent, we need special methods to investigate them. This course will include exploratory methods and modeling for time series, including descriptive methods and checking for significance, and a foray into the frequency domain. We will emphasize applications to a variety of real data, explored using R.
The Class: Format: lecture; Introductory lectures will be available asynchronously as text and video; synchronous sessions will discuss questions from lecture, dive further into the material, and work on examples. You'll use chat and discussion boards to build community, study with classmates, and ask questions outside of class time. There will also be optional synchronous office hours/review sessions.
Limit: 15
Expected: 15
Class#: 5383
Grading: yes pass/fail option, yes fifth course option
Requirements/Evaluation: Evaluation is primarily based on quizzes and projects (on topics that interest you!). You'll be given the opportunity to assess your own work and resubmit/reattempt assignments as you gain mastery of a topic. Participation matters! Engagement with your peers is an important part of learning, of being a statistician in the Real World...and of your evaluation in this course. While most assignments will be submitted (and graded) individually, you'll be responsible for giving and receiving peer feedback, contributing to live and online discussions, and working together with classmates on practice problems.
Prerequisites: STAT 346 (may be taken concurrently) or permission of instructor
Enrollment Preferences: Statistics majors, seniors
Distributions: Division III Quantative/Formal Reasoning
QFR Notes: This course uses mathematical tools and computing programs to create models, make predictions, assess uncertainty, and describe data. We'll also emphasize choosing appropriate mathematical tools and interpreting their results in a real-world context.

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