Exeter ebart

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).

1

https://vle.exeter.ac.uk/mod/url/view.php?id=1415157
http://vle.exeter.ac.uk/course/view.php?id=9280
https://vle.exeter.ac.uk/pluginfile.php/1992879/mod_label/intro/Marking%20criteria%20BIOM4025%20Problem%20sheet.pdf
http://rmarkdown.rstudio.com
http://vle.exeter.ac.uk/course/view.php?id=9280
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.