Statistics homework help

Problem Sheet
BIOM4025 – Statistical Modelling – 2020
Introduction
For this Problem Sheet, you will be analysing some of the data provided through the Model seeks Data link
on the ELE page under Task 1.3.1).
I have exported the data for you and I have also done a bit of cleaning-up (but not too much!). You can find
the data as a Comma seperated values (.csv) file on the ELE page.
You will see that I have kept the description of the tasks deliberately general. This means that you have a lot
of freedom in how you choose to approach each one of them. What I expect from you is to show off your
statistical modelling and R skills by applying what you have learned during the lectures and especially the
practicals. I am looking for creativity, originality, clarity and comprehension. For more information, see the
marking criteria on the ELE page.
Please note that during the lecture and practical Q&A sessions we will NOT answer questions that are
directly related to this problem sheet.
What to submit
You are required to perform all of the tasks listed on the next page. Remember that there are often many
ways of achieving the same thing, but simpler is usually better. You are limited to 3 pages (including your
code and figures), so you will have to be both selective and concise, and you have to think carefully about
what you present.
You will need to provide both the R code (but of course only the code that worked. . . ) and the output. There
are two alternative ways of doing this:
1. Copy-paste everything into a Word document. Use a fixed-width font like Courier or Courier New
for both your R code and the output. Make sure that you annotate your code so that someone else
(i.e. me) can follow why you did what, and to show that you have understood what you did. To
copy-paste a figure, go to the Plots tab in the bottom-right panel and click on Export → Copy to
Clipboard…. Alternatively, you can use Save as Image… or Save as PDF… and import this file
into Word. There are no specific requirements with respect to font size, line spacing or margins, but
please use common sense and keep it clear and easy to read. Save the final document as a PDF.
2. You can use R Markdown, which provides a powerful tool to combine R code, output and any other text,
images, equations, etc., into a single document. As a matter of fact, most of the documents for this
module (including this one) are written using R Markdown! Although I think that on the long-term
learning R Markdown might be worth it, it is yet another new thing to learn and you probably have
plenty of other things to do as well. It is therefore completely up to you whether you use R Markdown
or not. If you would like to know more, have a look at the R Markdown website.
How to submit
Submit a PDF via eBART following the instructions on the ELE page. The deadline is November 30 at
12:00 (noon).
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Tasks
We have data for a diverse set of variables, including both continuous and categorical variables, and while
some are expected to be normally distributed, others probably are not.
Task 1
Choose a dependent variable and one or more predictor variables and formulate (in words) the hypothesis
that you would like to test. Be creative!
Task 2
Use (generalised) linear (mixed) modelling to test this hypothesis. Provide the code that you used, as well as
any relevant output. Add comments (using #) to your code that allow me to understand why you did what.
Task 3
Write one or more sentences for the Results section of a scientific article in which you summarise your most
important result. It does not matter if your P-value is smaller or larger than 0.05!
Task 4
Provide a publication-ready figure that illustrates this result.
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