Generalised Linear Modelling
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STAT8130/4030
Generalised Linear Modelling
1. This final project is worth 60% of your final grade and is compulsory.
2. Maximum marks: 60.
3. Project reports can only be submitted via Turnitin on the Wattle.
4. Please sign the declaration form on the Wattle and include it as the cover page
in your submission. Please be aware of the quality of the file when you are preparing
the submission, such that the file is legible to read.
5. File size limit for Turnitin submission: 50MB.
6. Several trials of submission are strongly recommended before the due date. If
there is any problem in your trials, please send an email to report before the due
date. Late submission will not be accepted and your final project will be marked 0.
Please prepare to submit your report at least 30 minutes before the end of due time.
7. While you may use course material, computer software, internet, or other
resources, you must complete the final project individually. Identical submissions
even only for one sentence are treated as cheating.
8. You can use any result, formula or statement from the course material without
proof. In fact, doing this will help your project.
1
DATA
This individual final project is designed to apply materials in this course to analyse
any one or two real-world datasets chosen by yourself. It is worth noting some broad
types of real data that are available without charge.
cannot be the same as any datasets used in the lectures, tutorials, assignments and
other assessments of this course and your other courses.
Based on the one or two datasets that you choose, you need to consider at least
two types of models to fit your data from the list below (each bullet point can only
count as one type):
• Linear Mixed Effects Model;
• Binary Regression/Binomial Logistic Regression;
• Poisson Log-Linear Regression/Log-linear Regression with Extra-Poisson Vari-
ation;
• Multicategory Logistic Regression; and
• Gamma/Exponential Generalised Linear Model.
When you work on the fitting of the two types of models that you choose, you
may consider only one dataset but select two different variables as response variables
respectively, such that your two types of models can be applied to each of them; or you
can consider two datasets respectively corresponding to the two types of models that
you select. Note that the selection of the response variables needs to be meaningful
and useful in real practice. If you include the fitting results for only one type
of models in your report, you will be deducted 30 marks for this final
project.
REPORT (60 marks)
Report Format – PDF or Word Upload
Written reports for this project (10 pages maximum for the main manuscript and
20 pages maximum for the appendix based on the format below, and all the R code
should be relegated to the appendix) are expected to be submitted via Turnitin.
Turnitin similarity check will be conducted for all the submitted reports.
Please use Australian English spelling.