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STAT2001: Probability and Statistical Models

From first-principles probability to the statistical models used to actually analyse data: estimation, hypothesis testing, and enough regression to critique a model rather than just run one. Weekly problem sets; two of them use a dataset you didn't clean yourself, on purpose.

Learning objectives

By the end of this course, students will be able to:

• Derive and apply standard probability distributions
• Construct and interpret hypothesis tests and confidence intervals
• Fit a regression model and critique its assumptions
• Analyse a dataset you didn't clean yourself without being misled by it

Assessment

• Weekly problem sets20%Eight problem sets across probability, estimation and testing.
• Data analysis assignment30%A written report fitting and checking a model against a provided real-world dataset.
• Final exam50%Closed-book exam covering the semester's theory and methods.

The data analysis assignment is a written report; the exam is in-person and closed-book.

Timetable

Lecture: Wed 11am–12pm, Manning Clark 4

3 tutorial option(s) — pick one on the timetable page after enrolling.

• Tutorial A: Mon 9am–10am, Haydon-Allen 201
• Tutorial B: Thu 10am–11am, Haydon-Allen 201
• Tutorial C: Fri 1pm–2pm, Haydon-Allen 202

4/4 courses added.