STAT2001 Revision Summaries
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  1. STAT2001 Revision Hub
  • CH02 : Probability
  • CH03 : Discrete R.V.
    • Known Discrete Distributions
    • Measures Related to Distribution
  • CH04 : Continuous R.V.
    • Known Continuous Distributions
    • Expectations
  • CH05 : Multivariate Distributions
    • Introduction
    • Joint Continuous Distribution & Expectations (2)
  • CH06 : Functions of Random Variables
  • CH07 : Sampling Distributions and Central Limit Theorem
    • Sum of Continuous Random Variables [Optional]
  • CH08 : Estimation
    • Point Estimation
    • Evaluation Metrics
    • Interval Estimation
    • Estimation via Monte Carlo Methods
    • Coverage Test (Optional)
  • CH09 : Properties of Point Estimators & Methods of Estimation
    • Properties of Point Estimators
    • Methods of Estimations
  • CH16 : Bayesian Methods
    • Simulation

On this page

  • 1 ๐Ÿ“Š Welcome to the STAT2001 Revision Hub
    • 1.1 ๐Ÿ“š Topics Covered
    • 1.2 ๐Ÿ› ๏ธ Tools and Resources
    • 1.3 ๐Ÿ“Œ Getting Started
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STAT2001 Revision Hub

Published

Sunday Jun 8, 2025

1 ๐Ÿ“Š Welcome to the STAT2001 Revision Hub

This site is your companion for revising STAT2001: Introductory Mathematical Statistics at ANU. Whether youโ€™re preparing for exams or reinforcing weekly concepts, youโ€™ll find concise summaries, worked examples, and practice materials here.


1.1 ๐Ÿ“š Topics Covered

The content is structured to mirror the STAT2001 curriculum:

  • Probability Foundations: Set theory, combinatorics, and Bayesโ€™ theorem.
  • Random Variables: Discrete and continuous distributions, including moment-generating functions and correlation.
  • Multivariate Distributions: Joint, marginal, and conditional distributions.
  • Sampling Distributions: Understanding the central limit theorem and its applications.
  • Estimation Techniques: Methods of moments and maximum likelihood estimation.
  • Hypothesis Testing: Formulating and testing statistical hypotheses.
  • Bayesian Statistics: Introduction to Bayesian inference and estimators.

1.2 ๐Ÿ› ๏ธ Tools and Resources

All examples and exercises are implemented using R, the primary statistical computing tool for this course. Youโ€™ll also find:

  • Interactive visualizations to aid understanding.
  • Cheatsheets summarizing key formulas and concepts.
  • Practice questions with step-by-step solutions.

1.3 ๐Ÿ“Œ Getting Started

Navigate to the Topics page to begin exploring specific areas of the course. For more information about this site, visit the About page.


Note: This site is an independent student-led initiative and is not officially affiliated with ANU.

Copyright 2025, Isaac Leong

 
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