Assessment (non-exam) Brief
|
Module code/name |
MSIN0154 Statistics for Business Research |
|
Academic year |
2024/25 |
|
Term |
1 |
|
Assessment title |
Individual Coursework 2 |
|
Individual/group assessment |
Individual |
Submission deadlines: Students should submit all work by the published deadline date and time. Students
experiencing sudden or unexpected events beyond your control which impact your ability to complete assessed
work by the set deadlines may request mitigation via theextenuating circumstances procedure. Students with
disabilities or ongoing, long-term conditions should explore aSummary of Reasonable Adjustments. Students may use thedelayed assessment schemefor pre-determined mitigation on a limited number of assessments in a year. Check the Delayed Assessment Scheme area on Portico to see if this assessment is eligible.
Return and status of marked assessments: Students should expect to receive feedback within 20 working days of the submission deadline, as per UCL guidelines. The module team will update you if there are delays through
unforeseen circumstances (e.g. ill health). All results when first published are provisional until confirmed by the Examination Board.
Copyright Note to students: Copyright of this assessment brief is with UCL and the module leader(s) named above. If this brief draws upon work by third parties (e.g. Case Study publishers) such third parties also hold copyright. It must not be copied, reproduced, transferred, distributed, leased, licensed or shared with any other individual(s) and/or
organisations, including web-based organisations, without permission of the copyright holder(s) at any point in time.
Academic Misconduct: Academic Misconduct is defined as any action or attempted action that may result in a
student obtaining an unfair academic advantage. Academic misconduct includes plagiarism, self-plagiarism,
obtaining help from/sharing work with others be they individuals and/or organisations or any other form of
cheating that may result in a student obtaining an unfair academic advantage. Refer toAcademic Manual Chapter 6, Section 9: Student Academic Misconduct Procedure – 9.2 Definitions.
Referencing: You must reference and provide full citation for ALL sources used, including AI sources, articles, text books, lecture slides and module materials. This includes any direct quotes and paraphrased text. If in doubt,
reference it. If you need further guidance on referencing please seeUCL’s referencing tutorial for students. Failure to cite references correctly may result in your work being referred to the Academic Misconduct Panel.
Use of Artificial Intelligence (AI) Tools in your Assessment: Your module leader will explain to you if and how AI
tools can be used to support your assessment. In some assessments, the use of generative AI is not permitted at all. In others, AI maybe used in an assistive role which means students are permitted to use AI tools to support the
development of specific skills required for the assessment as specified by the module leader. In others, the use of AI tools maybe an integral component of the assessment; in these cases the assessment will provide an opportunity to demonstrate effective and responsible use of AI. See page 3 of this brief to check which category use of AI falls into
for this assessment. Students should refer to theUCL guidance on acknowledging use of AI and referencing AI.
Failure to correctly reference use of AI in assessments may result in students being reported via the Academic
Misconduct procedure. Refer to the section of the UCL Assessment success guide onEngaging with AI in your education and assessment.
Content of this assessment brief
|
Section |
Content |
|
A |
Core information |
|
B |
Coursework brief and requirements |
|
C |
Module learning outcomes covered in this assessment |
|
D |
Groupwork instructions (if applicable) |
|
E |
How your work is assessed |
|
F |
Additional information |
Section A: Core information
|
Submission date |
04/12/2024 |
|
Submission time |
10am |
|
Assessment is marked out of: |
100 |
|
% weighting of this assessment within total module mark |
70% |
|
Maximum word count/page length/duration |
25 pages |
Section B: Assessment Brief and Requirements
MSIN0154 Individual Coursework 2
This individual assignment is a data analysis project. You are given a data set to demonstrate the skills you have learnt from the class, including hypothesis testing, t-tests and/or regression. This dataset contains sales transaction records for an electronics company over a one-year period, spanning from September 2023 to September 2024. It includes detailed information about customer demographics, product types, and purchase behaviours. This dataset is publicly available and is adapted from the Customer purchase behavior. – Electronic Sales Data. For more information, please visit:
https://www.kaggle.com/datasets/cameronseamons/electronic-sales-sep2023-sep2024
Below is the information about the data set for this assignment, and the data set is available in Excel format on Moodle.
Key Features:
• Customer ID: Unique identifier for each customer.
• Age: Age of the customer (numeric)
• Gender: Gender of the customer (Male or Female)
• Loyalty Member: (Yes/No) (Values change by time, so pay attention to who cancelled and who signed up)
• Product Type: Type of electronic product sold (e.g., Smartphone, Laptop, Tablet)
• SKU: a unique code for each product.
• Rating: Customer rating of the product (1-5 stars) (Should have no Null Ratings)
• Order Status: Status of the order (Completed, Cancelled)
• Payment Method: Method used for payment (e.g., Cash, Credit Card, Paypal)
• Total Price: Total price of the transaction (numeric)
• Unit Price: Price per unit of the product (numeric)
• Quantity: Number of units purchased (numeric)
• Purchase Date: Date of the purchase (format: YYYY-MM-DD)
• Shipping Type: Type of shipping chosen (e.g., Standard, Overnight, Express)
• Add-ons Purchased: List of any additional items purchased (e.g., Accessories, Extended Warranty)
• Add-on Total: Total price of add-ons purchased (numeric)
As a business analyst, you can freely explore the data set and find insights through data analysis. You need to complete a report to summarize all insights supported by data analysis and discussions. In your report, you should study and answer up to three research questions, namely you can focus on one research question in detail, or study 2 or 3 different questions in your report. The page limit is 25 pages, including everything, i.e., figures, tables, references and appendix. It should be produced in a serif font, such as Times New Roman, size 12 and single line spacing. The assignment should be completed independently.
A typical report should include the following key parts, and you are free to decide the structure of your own report:
• Introduction: Briefly introduce the questions you are studying in the report with justifications, i.e., through a brief literature review or background introduction.
• Analysis: Details of the analysis tools you use, new variables you generate, process for analysis and the results
• Discussion/Conclusion: discuss the insights from your analysis and the limitations
• Reference: list any reference you used in the report (using Harvard style)
• Appendix (optional): anything else that you would like to include to support your analysis
Submission is via Moodle. Please only submit one single pdf file including everything. Do not include name or student number, as the marking is anonymous.
Section C: Module Learning Outcomes covered in this Assessment
This assessment contributes towards the achievement of the following stated module Learning Outcomes as highlighted below:
• Understand key concepts in statistics.
• Interpret data from descriptive statistics, measures of central tendency and measures of dispersion.
• Critically analyse datasets and sampling methods.
• Apply statistical tests to verify significance of findings.
• Identify appropriate methods to present data.
• Recognise the benefits and limitations of statistical calculations and analysis.
Reviews
There are no reviews yet.