讲解 MSIN0154 Statistics for Business Research调试数据库编程

Assessment (non-exam) Brief

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 the extenuating circumstances procedure. Students with disabilities or ongoing, long-term conditions should explorereasonable academic adjustments. Students may use the delayed 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 their 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 to Academic 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 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 see UCL’s referencing guide for students. Failure to cite references correctly may result in your work being referred to the Academic Misconduct Panel. For guidance on how to acknowledge the use of Artificial Intelligence (AI) please see next section.

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 may be 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 may be 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 the UCL guidance on acknowledging use of AI and referencing AI.

Failure to correctly acknowledge the 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 on Engaging with AI in your education and assessment.

Content of this assessment brief

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 inlcudes sales dataset containing information about different orders and provides detailed information about each order, including customer details, product details, sales information, and shipping information. It can be used to analyse various aspects of the sales data, such as profitability, customer segments, product categories, and regional sales performance. This dataset is publicly available and is adapted from Kaggle.com.

Below is the information about the data set for this assignment, and the data set is available in Excel format on Moodle.

• Row ID: An identifier for each row in the dataset.

•    Order ID: Unique identifier for each order.

•    Order Date: The date when the order was placed.

•    Ship Date: The date when the order was shipped.

•    Ship Mode: The mode of shipping chosen for the order.

•    Customer ID: Unique identifier for each customer.

•    Customer Name: Name of the customer who placed the order.

•    Segment: The segment to which the customer belongs (e.g., consumer, corporate).

•    Country: The country where the order was placed (in this case, United States).

•    City: The city where the order was placed.

•    State: The state where the order was placed.

•    Postal Code: The postal code associated with the order's location.

•    Region: The region of the country where the order was placed.

•    Product ID: Unique identifier for each product.

•    Category: The category to which the product belongs (e.g., furniture, office supplies).

•    Sub-Category: The sub-category to which the product belongs (e.g., bookcases, chairs).

•    Product Name: The name of the product.

•    Cost: The cost of the product.

•    Price: The price at which the product was sold.

•    Profit: The profit made from the sale of the product.

•    Quantity: The quantity of the product ordered.

•    Sales: The total sales generated from the order (quantity multiplied by price).

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., cover page,  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.