STAT0012 In-Course Assessment 2020, Term 2
• Your submission for this exercise should be your own work. It (a pdf) is to be handed in electronically (on Moodle) by Thursday 26th March, 16.00. You will get some of the marks by submitting an outline of your work prior to the workshop session on 6th March, where you are expected to discuss your project.
• Late work will incur penalties.
• Your submitted work needs to be typed. Do not use handwriting. The length should be at most five (5) pages, with a letter size of 11pt, including references, graphs and pictures. Plain computer output and listings (for which there is no length limit) can be attached to the back of the work.
• Over length submissions will be penalised, normally by ignoring for marking all material beyond the maximum length.
• Any plagiarism will normally result in zero marks for all students involved. Guidelines as to what constitutes plagiarism may be found in the Departmental Student Handbooks and on Moodle.
• You will receive a provisional grade and feedback during the start of next term – grades are provisional until confirmed by the Statistics Examiners’ Meeting in June 2020.
• This assessment will be marked anonymously. Please ensure that your name does not appear anywhere on the submission. You should name the file you submit: ”99999999 STAT0012 ICA.pdf” where 999999999 represents your student number.
The exercise
Design, carry out, analyse and write a short report on a 2k (k > 2) factorial experiment. You should also include blocking
Additional information Do it yourself!
Yes I expect you to actually DO the experiment. There are lots of possibilities. Kitchens offer a lot of scope. For example, one might study the success rate of popping corn as a function of pan size, amount of oil, and heat level. However, please do not devise an experiment which looks at the effect of adding various substances to the time it takes to boil water (or other liquids) or to melt ice. Also, the blocking should not refer to the time of day that the experiment takes place.
There is no need to do anything particularly fancy, although of course you can if you wish. I’m happy with other “laboratories” than the kitchen, too. I’d be perfectly happy with a 23 with either replication or a few centre points to estimate error. The point of the exercise is to make you think about some of the practical issues involved in experimenting.
Take note: if the planned experiment involves exposing people to treatment, then it is compulsory to get approval from the course lecturer. As an example, no approval will be given to an experiment that involves investigating the effect of levels of alcohol consumption on cognitive function.
Perhaps a few words on what might count as plagiarism might not go amiss. I’d be pretty amazed if you didn’t discuss amongst yourselves ideas for what kind of example to use. If in consequence the same basic idea gets used by more than one person I don’t see that as a problem. You might find an idea in a book, and that is also fine, though you should reference the book in your report, and do your own experiment, not take the data from the book. What I would see as unacceptable is exactly the same experiment from two people. You need to decide the details, e.g., levels of factors and other implementation details, for yourself, not by copying your friend’s experiment.
Masters students will also have done an experiment for STAT0029. Please do not do the same experiment (even if the analysis is different – I will be checking).
Observational studies will receive 0 marks.
Finally, because of misunderstandings in the past when students have analysed data from a book, I repeat the opening sentence: I expect you to do the experiment, i.e., carry out the runs and collect your own data. If you do not understand what this means please come and ask.
What to write
You are expected to
1. explain the aim of the experiment and what was done,
2. discuss your choice of factors, factor levels and blocking, and how the response variable was measured,
3. analyse the data (which should be fully listed) and interpret the results in some detail (I also expect at least one graph to help visualise the data),
4. discuss the design and the model assumptions critically, and indicate what more could be done,
5. write a summary containing the most important results that is understandable to a non- statistician, which should be self-contained, i.e., assume that the non-statistician only reads the summary.
You can use this list to structure your report, but you don’t have to. However, you should in any case end with the summary for a non-statistician as the final section.
Some comments on marking
Each of the five items listed above will carry 20% marking weight (but see below). Here are some aspects that I will take into account when marking.
• There will be some marks for how interesting / original your experiment is.
• The experiment should be designed in such a way that it can show something that is informative and not entirely trivial (I know without experimentation that my cake will be burned if I bake it at 500 ◦C for eight hours).
• Regarding the quality of the experiment, I will check whether what was done was reasonable, given that your time for doing this is limited and it only counts for 20% of your overall marks for the course. You should think about possible improvements and mention them even if you don’t have the time to do them. If there is something obvious that should have been done and you don’t mention it, this will cost you marks.
• As said before, you don’t need to use more than three factors, but again, if there are obviously relevant factors that could have been used without a lot of effort, I expect you to at least mention this. There will be some credit for good and original ideas.
• I don’t expect you to use any of the material after Section 7 in the notes, but it’s not forbidden either and if you do it right and it is reasonable, it will be rewarded.
• Regarding the writing, it is important that you explain properly what was done and why. You should give interpretations in terms of the subject matter of the experiment, not just anonymous technical statements such as “there is no evidence against the H0”.
• Regarding the data analysis I will ignore raw computer output that isn’t explained and inter- preted, so make sure that you explain and interpret all results that seem relevant in proper sentences (items 3 and 4 above don’t need to be understandable to a non-statistician, so you are allowed to be a bit technical there). It is acceptable not to use a computer software for the calculations and to do them manually. However, you should estimate an error variance and carry out suitable tests and/or confidence intervals.
• Of course, mathematical correctness is important, as is correctness of the interpretations. You don’t need to write down all your calculations (or computer syntax used) but if results are wrong, it may win you back marks if I see that you did the right thing but just typed a wrong number in somewhere, although I’d expect you to find and, if necessary, correct grossly counter- intuitive results yourself. It should be clear what assumptions you make and you should say why you think that they make sense.
• Please be aware of the length restrictions given on the first page. Marks may be subtracted for pointless and irrelevant statements. Particularly it will not help you to show or discuss several equivalent analyses (such as computing the same things manually and with a computer; you may do this for yourself checking your results and it could be a useful exercise, but I don’t want to see this in your submission).
Pre-submission feedback session
Half of the workshop time on Friday 6th March, 14.00 – 16.00, will be devoted to a pre-submission feedback session. You will get 5% (part of the 20% for describing the aim of the experiment) for submitting in advance (by midnight Thursday 5th March) an outline of length between one and two (reasonably full) pages of the experiment that you’re planning to carry out or that you have already carried out. This can be made up of material that you plan to submit later (see “What to Write”, particularly the first two items). The 5 marks are given for providing the outline, not for its quality.
These outlines will be discussed in groups (“peer-to-peer feedback”) with some input from the lecturer.
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