FEEG6025: Data Analysis & Experimental Methods for Engineers Assignment
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FEEG6025 Assignment
Issue date: Wednesday 12nd October 2022
[Part 1] Submission Date: 09:00, Monday 31st October 2022
[Part 1] Submission format: Presentation (summative)
[Part 2] Submission Date: 09:00, Monday 5th December 2022
[Part 2] Submission format: Presentation (formative)
[Part 2] Submission Date: 17:00, Thursday 15th December 2022
[Part 2] Submission format: Report (summative)
Background
The aim of this coursework is to help you develop and apply your understanding of the research study design
process and a wide range of data analysis techniques. Together with study design, data analysis skills will be
essential for your dissertation and are highly likely to be important in your future careers because you may be
asked to carry out data analysis as part of research studies or to manage others (including statisticians) to do
the data analysis on your behalf. This assignment is intended to partially reflect real work conditions so we
expect you to discover, synthesise and reference appropriate background resources although we will of course
provide technical support and guidance where needed.
Your task
[Part 1] In groups of 3 you need to:
1. Select one of the datasets described below;
2. Undertake a literature review of the theory, data collection and analysis methods of the chosen topic;
3. Develop a study design including setting out the problem statement, three research questions and
associated set of hypothesis and data analysis methods to address these hypothesis;
4. Prepare a narrated group presentation by Friday 28th October (12:00), .pptx file to be uploaded on
Blackboard (~10 slides, 5 minutes group presentation).
5. Review your peer-group narrated presentation
6. Deliver a group presentation on Monday 31st October ~10 minutes per group (5 minutes group
presentation & 5 minutes feedback & change over), all group members to speak;
[Part 2] As an individual assignment, you have to complete the following:
1. Address one research question and associated set of hypotheses developed in [Part 1] by conducting the
required data analysis using R. The statistical techniques used may include descriptive statistics,
inferential statistics and modeling.
2. Prepare a narrated individual presentation by Friday 2nd December (12:00), .pptx file to be uploaded
on Blackboard (~5 slides, 3 minutes individual presentation).
3. Deliver an individual presentation on Monday 5th December ~5 minutes per student (3 minutes
individual presentation & 1-minute feedback & change over);
4. Write and submit a data analysis report by Thursday 15th December.
FEEG6025: Data Analysis & Experimental Methods for Engineers Assignment
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The data analysis projects
The data analysis projects include the following datasets which are on (or linked to on) blackboard:
1. Luo, N., Wang, Z., Blum, D. et al. A three-year dataset supporting research on building energy
management and occupancy analytics. Sci Data 9, 156 (2022). https://doi.org/10.1038/s41597-022-
01257-x
2. Pullinger, M., Kilgour, J., Goddard, N. et al. The IDEAL household energy dataset, electricity, gas,
contextual sensor data and survey data for 255 UK homes. Sci Data 8, 146 (2021).
https://doi.org/10.1038/s41597-021-00921-y
3. Long, Y., Jiang, Y., Chen, P. et al. Monthly direct and indirect greenhouse gases emissions from
household consumption in the major Japanese cities. Sci Data 8, 301 (2021).
https://doi.org/10.1038/s41597-021-01086-4
4. Moreno, J., Asensio, S., Berdugo, M. et al. Fourteen years of continuous soil moisture records from plant
and biocrust-dominated microsites. Sci Data 9, 14 (2022). https://doi.org/10.1038/s41597-021-01111-6
If you already have a dissertation topic in mind, then it would be an excellent idea to use this coursework to
analyse your dataset, as you will get formative feedback on your proposal at a very early stage.

[Part 1] Group work
The smooth running of this coursework is very dependent on everyone within each group contributing to the task,
particularly since we award the same mark to each member of the group. We run joint projects because we feel
they are good learning exercises. For example, we believe you learn from each other, as much as from our tutors
and the subject texts, and you see at first hand the issues that are critically important to other professionals in
your field. You also learn about real world working, where almost everyone has to interact with others (and make
compromises) in carrying out their everyday work. We cannot hope to replicate the professional situation, but
we feel you can learn some of the skills you will need in your future professional careers by doing this project.
To encourage cultural and cross-discipline exchanges, you are free to decide who is in which group subject to
some ‘rules’:
• Nationality: ideally, no more than two students from the same nationality per group.
• Expertise: ideally, no more than two students from the same expertise per group.
To encourage all group members to contribute to the project, you should include a short introduction on
individual contributions in the work during the presentation - refer to Section 1 'Presentation structure' below
and see template on Blackboard. In the unlikely event of a group dispute, please let your tutors know.
The group presentation should include the following slides:
1. First slide: topic, names of all group members and very short description of their contributions
2. Second slide: summary of the presentation
3. Content slides: summary of the literature review, dataset, collection methods, ethical considerations
and risk assessment related to data collection
4. Last slides: list of three research questions and associated hypothesis and who in the group will be
addressing those research questions
Each group will be paired with another group. They will have to review the peer group presentation between
Friday and Monday and to give peer-review feedback during the presentation.
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[Part 2] Individual work
The individual work consists of a formative timeline, a formative presentation and a summative report. The main
body of the report should be no more than 2,200 words excluding the abstract, tables, references and
appendices. The report should be no more than 15 pages in total (excluding Appendices). The report should be
formatted as a research paper (see template on Blackboard).
The timeline will consist of a self-reflective weekly review of individual progress. It will summaries what was done
during the past week and what will need to be done the following week in order to complete the assignment.
The individual presentation should include the following slides:
1. First slide: topic, name
2. Content slides: study design and results
3. Last slide: conclusions
The report should include the following sections:
1. Abstract - to provide a high-level summary of your proposed design (500 words maximum);
2. Introduction - to explain the requirement for and significance of the study; introduce your dataset;
3. Literature review - to review what is already known about the topic (theory) and how it has been
previously studied (methodology);
4. Study design - to propose a study design, which covers: sampling, data collection methods, data analysis
methods, legal & ethical issues;
5. Results - to provide a structured report of your data analysis stating the steps you took and the results
you found; to include tables of results and graphs where appropriate;
6. Discussion - to criticality review the results and explain how they answer the questions you have been
set;
7. Summary - to provide a summary of your findings, review the limitations of your study and highlight
implications and future research;
8. Feedback review - to provide a summary of how the report addresses the feedback received
throughout the module;
9. References - to provide a bibliography of all resources referred to in the text including any online
statistical resources or other sources of data you may have used;
10. Appendices - to provide any background material, to include individual timeline and all R code used as
an RMarkdown (.Rmd) file

How to submit
You will need to submit a copy of your complete report in PDF format via Blackboard Turnitin. Standard
departmental penalties for late submission will apply.

Tips
• Back up your work regularly - or better still, work in the cloud using Dropbox, Office365, etc.;
• Assume that the reader is educated but not necessarily a specialist in the field;
• Go through the report before you submit it and identify the purpose of each paragraph - be a
tough editor on yourself!
• Have someone else (such as another student or a friend who is unfamiliar with the project) read
the report before you submit it.
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Assessment schedule

Type Title of
assessment
Set
date
Due
date
Submission
method
Feed
back
date
Feedback
method
Weight
ed
mark
Purpose
1 Short
Present
ation
Assignment
Part 1
(Group
work)
12/10/
2022
31/10/
2022
Presentation
online
02/11
/2022
Written
comments
on the
work
5% To develop
experimental
skills &
To test oral
communication
2 Short
Present
ation
Assignment
Part 2
(Individual
work)
02/11/
2022
05/12/
2022
Presentation
online
05/12
/2022
Oral
comments
on the
work
Format
ive
To develop data
analysis skills &
To test oral
communication
3 Written
report
Assignment
Part 2
(Individual
work)
02/11/
2022
15/12/
2022
Turnitin 11/01
/2023
Written
comments
on the
work
35% To develop data
analysis skills &
To test written
communication
of technical
information

Marking criteria
[Part 1] Marks will be awarded according to the following criteria:

Criteria Mark % Indicative
Grade
Boundaries

Conveying a research question and hypothesis 10% Distinction 70
Evaluating and synthesising literature 30% Merit 60
Clarity and depth of the experimental research design 30% Pass 50
Reviewing ethical considerations & risk
assessment of data collection
10%
Clarity of the overall presentation 10%
Peer review feedback 10%
FEEG6025: Data Analysis & Experimental Methods for Engineers Assignment
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[Part 2] Marks will be awarded according to the following criteria:

Criteria Mark % Indicative
Grade
Boundaries

[Abstract & Introduction] Conveying a research question
and hypothesis
5% Distinction 70
[Literature review & References] Evaluating and
synthesising literature
10% Merit 60
[Study design] Clarity and depth of methods discussion 20% Pass 50
[Results] Present the analyse the dataset 35%
[Discussion & conclusion] Reflect upon the analysis and
the appropriateness of statistical inference and
modelling
20%
Reflect on feedback 5%
Clarity and overall presentation of the report 5%