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Undergraduate Study

STAT3022 Applied Linear Models

General Information

This page contains information on the senior mainstream unit of study STAT3022.

  • Taught in Semester 1.
  • Credit point value: 6.
  • Classes per week: Three lectures and one tutorial.

Please refer to the Senior Mathematics and Statistics Handbook for all questions relating to Senior Mathematics and Statistics. In particular, see the handbook entry for STAT3022 for further information relating to STAT3022.

You may also view the description of STAT3022 and the description of in the University's course search database.

For enrolled students or other authorized people only, here is a link to the Canvas page for STAT3022.

Students have the right to appeal any academic decision made by the School or Faculty: see sydney.edu.au/students/academic-appeals.html.

 

Hi! Welcome to this course. I am Jennifer Chan, your lecturer. The lecturer for advanced tutorials is Munir Hiabu.

For those who are still in China, I send my warmest welcome and look forward to see you in campus soon.  We will support you to the best we can during this period. Whenever necessary, I will upload additional note to explain things more clearly. Please note the student page for Coronavirus (Covid-19) Update. You may enquire here. We also provide you information regarding How to access student learning resources off campus in China.  You can also have Off Campus Student Support.

This website will be hosting the teaching materials for STAT3022. The same website with tutorial and lab times is also available in our school webpage. The lecture slides will be uploaded after the lecture (if not earlier). Tutorial solutions for week nn are uploaded at the end of week nn. Please complete all tutorial questions in week n by the start of week n+1.

Behind R? Go through the Datacamp courses! Sign up through here where you have unlimited access for 6 months to all the courses offered by Datacamp. You will need to sign up with your student email.

WARNING: many modelling course in datacamp are beyond this course and/or explore concepts that are not central to this course (e.g. machine learning, non-linear, GLM, GLMM). Be mindful that those concepts are not tested within this course. Recommended and relevant datacamp course are added in the Extra column below.

Please use edstem to post questions and email lecturers only if your email is confidential in nature.

The outline below shows an intended breakdown of what we will cover this semester in STAT3022.

Please see the School of Mathematics & Statistics third year website for many other information including the handbook and the timetable website for the Unit of Study timetable.

Week Lectures note Tutorial Misc / Assignment
1

Lecture 1: Getting started and R Markdown PDF

Lecture 2: Data Wrangling and Visualisation in R PDF

Lecture 3: Simple Linear Regression: Maximum Likelihood Estimation PDF

Week 1

rugby.txt

Lab Solution 

Q5 Rmd

Q5 Output

RMarkdown reference guide
RMarkdown cheat sheet1  2

dplyr cheat sheet

ggplot2 cheat sheet

Additional note

sleep.csv

2

Lecture 4: Simple Linear Regression: Diagnostics, Inference and Prediction PDF

Lecture 5: Matrix Algebra for Regression  PDF

Lecture 6: Multiple Linear Regression Part I  PDF

Week 2

olympic.txt

Tut Solution

Lab Solution

Rmd

Matrices.pdf

Interpret QQ plot

Additional note

paramo.txt

 

3

Lecture 7: Multiple Linear Regression Part II  PDF

Lecture 8: Outliers and High Leverage Points  PDF

Lecture 9: General F-test & Multicollinearity  PDF

Week 3

Tut Solution

Lab Solution

Rmd

Additional note
4

Lecture 10: Variable Selection: Backward and forward  PDF

Lecture 11: Variable Selection: Stepwise, AIC and BIC  PDF

Lecture 12: Polynomial Regression  PDF

Week 4

ratliver.txt

Tut Solution

Lab Solution

Rmd

cheese.txt

uscrime.txt

fev.txt

engine.txt

paramo.txt

Assignment 1

Additional note

5

Lecture 13: Robust Regression  PDF

Lecture 14: One-way ANOVA Part I  PDF

Lecture 15: One-way ANOVA Part II PDF

Week 5

baseball.txt

diabetes.csv

Tut Solution

Lab Solution

Rmd

star.txt

caffeine.txt

Additional note

Different estimators

6

Lecture 16: Multiple Comparisons  PDF

Lecture 17: Quantitative Factors  PDF

Lecture 18: Two-way ANOVA Part I  PDF

Week 6

companies.txt

Tut Solution

Lab Solution

Rmd

tooth.txt

batteries.txt

Additional note

7

Lecture 19: Two-way ANOVA Part II  PDF

Lecture 20: Assessing Normality  PDF

Public Holiday

Week 7

bluegills.txt

Tut Solution

Lab Solution

Rmd

ellipsoid.R

dressing.txt

8

Lecture 21: Motivating Examples  PDF

Lecture 22: Revision  PDF

Lecture 23: Design of Experiments  PDF

Week 8

Tut Solution

Lab Solution

Rmd

9

Lecture 24: Completely Randomised Designs  PDF

Lecture 25: Randomized Complete Block Designs  PDF

Lecture 26: Latin Square Design  PDF

Week 9

pain.txt

Tut Solution

Lab Solution

Rmd

10

Lecture 27: Nested Factors  PDF

Lecture 28: Nested Designs  PDF

Lecture 29: ANCOVAPDF

Week 10

Tut Solution

Lab Solution

Rmd

11

Lecture 30: Incomplete Block Designs  PDF

Online quiz: Based on lectures 1-25 and tutorial wk 1-9

Lecture 31: Random Effects Model  PDF

Week 11

rubber.txt

Tut Solution

Lab Solution

Rmd

12

Lecture 32: Linear Mixed Models  PDF

Lecture 33: Variance Component Estimation  PDF

Lecture 34: Longitudinal Data  PDF

Week 12

potato.txt

Tut Solution

Lab Solution

Rmd

13

Exam Revision

 

 

Timetable

 

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