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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.

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, we send our warmest welcome and will support you to the best. Whenever necessary, we 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.  

This canvas website hosts the teaching materials for STAT3022. Please note that the materials in our school webpage may not be the most updated. The lecture slides will be uploaded after the lecture (if not earlier). Tutorial solution for each week will be uploaded by the end of the week. Please attempt all tutorial questions of each week before your tutorial time. It is very important that you devote at least three hours each week in studying the course materials and completing the assignments, etc apart from attending the scheduled lectures, tutorials and labs. Quiz one is held on Week 4 to check your performance. If you have great struggle in this couse, please consider if you should drop it before the census date. 

Feeling behind in R? Sign up the Datacamp courses here and 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 courses in datacamp are beyond the curriculum of this course curriculum (e.g. machine learning, non-linear, GLM, GLMM). Be mindful that those concepts are not tested in this course and we have no control for your enrolment in Datacamp and any changes there. 

Please use edstem to post your questions but before that, please check if similar questions have been posted before. You should email the lecturers only if your email is confidential in nature.

The outline below shows an intended breakdown of what we will cover 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 (self reading)

Lecture 3: Simple Linear Regression: Maximum Likelihood Estimation PDF

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

Week 1

rugby.txt

 

RMarkdown reference guide
RMarkdown cheat sheet1  2

dplyr cheat sheet

ggplot2 cheat sheet

Additional note

sleep.csv

2

Lecture 5: Matrix Algebra for Regression  PDF

Lecture 6: Multiple Linear Regression Part I  PDF

Lecture 7: Multiple Linear Regression Part II  PDF

Week 2

olympic.txt

 

Matrices.pdf

Interpret QQ plot

Additional note

paramo.txt

 

3

Lecture 8: Outliers and High Leverage Points  PDF

Lecture 9: General F-test & Multicollinearity  PDF

Lecture 10: Variable Selection: Backward and forward  PDF

Week 3

 

Additional note
4

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

Online quiz 1: Based on lectures 1-10 and tutorial wk 1-3

Lecture 12: Polynomial Regression  PDF

Week 4

ratliver.txt

 

cheese.txt

uscrime.txt

fev.txt

engine.txt

paramo.txt

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

 

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

 

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

 

ellipsoid.R

dressing.txt

Additional note

8

Lecture 21: Motivating Examples  PDF

Lecture 22: Experimental design PDF

Lecture 23: Completely Randomised Designs PDF

Week 8

avocado.csv

phys.txt

 

antibiotics.txt

Additional note

9

Lecture 24: Randomised Complete Block Designs  PDF

Lecture 25: Latin Square Design  PDF

Lecture 26: Nested Factors  PDF

Week 9

pain.txt

 

pollen.txt

Additional note

10

Lecture 27: Nested Designs PDF

Online quiz 2: Based on lectures 1-26 and tutorial wk 1-9

Lecture 28: ANCOVA PDF

Week 10

 

Additional note
11

Lecture 29: Incomplete Block Designs PDF

Lecture 30: Higher Order Models PDF

Lecture 31: Random Effects Model  PDF

Week 11

rubber.txt

 

Additional note

12

Lecture 32: Linear Mixed Models  PDF

Lecture 33: Variance Component Estimation  PDF

Lecture 34: Longitudinal Data  PDF

Week 12

potato.txt

 

Additional note

13

Exam Revision

Exam Revision Solution

Exam Revision Q18 Design Matrices

Week 13

ant111br.csv

 

2011 Exam 

2011 Exam solution