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MSIN0154 Statistics for Business Research

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 explore a Summary of Reasonable Adjustments.

Return and status of marked assessments: Students should expect to receive feedback within one calendar month 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, obtaining help from/sharing work with others be they individuals and/or organisations or any other form of cheating. 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 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 see UCL’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 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 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 on Engaging 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

07/12/2023

Submission time

10am

Assessment is marked out of:

100

% weighting of this assessment within total module mark

30%

Maximum word count/page length/duration

15 pages

Footnotes, appendices, tables, figures, diagrams, charts included in/excluded from word count/page length?

Including everything, i.e., figures, tables, references and appendix

Bibliographies, reference lists included in/excluded from word count/page length?

including everything, i.e., figures, tables, references and appendix

Penalty for exceeding word count/page length

Penalty for exceeding word count will be a deduction of 10 percentage points, capped at 40% for Levels 4,5, 6, and 50% for Level 7) Refer to Academic Manual Section 3: Module Assessment - 3.13 Word Counts.

Penalty for late submission

Standard UCL penalties apply. Students should refer to https://www.ucl.ac.uk/academic-manual/chapters/chapter-4-assessment-framework-taught-programmes/section-3-module-assessment#3.12

Artificial Intelligence (AI) category

Not permitted

Submitting your assessment

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.

Anonymity of identity. Normally, all submissions are anonymous unless the nature of the submission is such that anonymity is not appropriate, illustratively as in presentations or where minutes of group meetings are required as part of a group work

submission The nature of this assessment is such that anonymity is required.

Section B: Assessment Brief and Requirements

MSIN0154 Individual Assignment

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 and regression. The data is comprised of the physicochemical tests and the grades of the red and white wine samples. This dataset is publicly available for research. More details can be found in below publication.

P. Cortez, A. Cerdeira, F. Almeida, T. Matos, and J. Reis. Modeling wine preferences by data mining from physicochemical properties. Decision Support System (2009).

Below is the information about the data set for this assignment, and the data set is available in Excel format on Moodle. The original data set in CSV format can be found at:

https://archive.ics.uci.edu/dataset/186/wine+quality

Number of records: 6497

Number of variables: 13.

List of variables:

1 - fixed acidity (continuous)

2 - volatile acidity (continuous)

3 - citric acid (continuous)

4 - residual sugar (continuous)

5 - chlorides (continuous)

6 - free sulfur dioxide (continuous)

7 - total sulfur dioxide (continuous)

8 - density (continuous)

9 - pH (continuous)

10 - sulphates (continuous)

11 – alcohol (continuous)

12 – quality (integers, 0 to 10, based on sensory data)

13 – wine type (white or red, categorical)

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 different research questions with a word limit of 15 pages (around 2000~2500 words), including everything, i.e., figures, tables, references and appendix.

A typical report should include:

• Introduction: Briefly introduce the questions you are studying in the report with justifications.

• 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

Below is a list of sample questions for your reference, please feel free to explore other questions that you find interesting:

• The physicochemical factors affecting how the participant perceive the wine (quality).

• The physicochemical factors contributing the difference between the two kinds of wine.

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.

Section D: Groupwork Instructions (where relevant/appropriate)

N/A

Section E: How your work is assessed

Within each section of this assessment you may be assessed on the following aspects, as applicable and appropriate to this assessment, and should thus consider these aspects when fulfilling the requirements of each section:

• The accuracy of any calculations required.

• The strengths and quality of your overall analysis and evaluation;

• Appropriate use of relevant theoretical models, concepts and frameworks;

• The rationale and evidence that you provide in support of your arguments;

• The credibility and viability of the evidenced conclusions/recommendations/plans of action you put forward;

• Structure and coherence of your considerations and reports;

• Appropriate and relevant use of, as and where relevant and appropriate, real world examples, academic materials and referenced sources. Any references should use either the Harvard OR Vancouver referencing system (see References, Citations and Avoiding Plagiarism)

• Academic judgement regarding the blend of scope, thrust and communication of ideas, contentions, evidence, knowledge, arguments, conclusions.

• Each assessment requirement(s) has allocated marks/weightings.

Student submissions are reviewed/scrutinised by an internal assessor and are available to an External Examiner for further review/scrutiny before consideration by the relevant Examination Board.

It is not uncommon for some students to feel that their submissions deserve higher marks (irrespective of whether they actually deserve higher marks). To help you assess the relative strengths and weaknesses of your submission please refer to SOM Assessment Criteria Guidelines, located on the Assessment tab of the SOM Student Information Centre Moodle site.

The above is an important link as it specifies the criteria for attaining the pass/fail bandings shown below: At UG Levels 4, 5 and 6:

80% to 100%: Outstanding Pass - 1st; 70% to 79%: Excellent Pass - 1st; 60%-69%: Very Good Pass - 2.1; 50% to 59%: Good Pass - 2.2; 40% to 49%: Satisfactory Pass - 3rd; 20% to 39%: Insufficient to Pass - Fail; 0% to 19%: Poor and Insufficient to Pass - Fail.

At PG Level 7:

86% to 100%: Outstanding Pass - Distinction; 70% to 85%: Excellent Pass - Distinction; 60%-69%: Good Pass - Merit; 50% to 59%: Satisfactory - Pass; 40% to 49%: Insufficient to Pass - Fail; 0% to 39%: Poor and Insufficient to Pass - Fail.

You are strongly advised to review these criteria before you start your work and during your work, and before you submit.

You are strongly advised to not compare your mark with marks of other submissions from your student colleagues. Each submission has its own range of characteristics which differ from others in terms of breadth, scope, depth, insights, and subtleties and nuances. On the surface one submission may appear to be similar to another but invariably, digging beneath the surface reveals a range of differing characteristics.

Students who wish to request a review of a decision made by the Board of Examiners should refer to the UCL Academic Appeals Procedure, taking note of the acceptable grounds for such appeals.

Note that the purpose of this procedure is not to dispute academic judgement – it is to ensure correct application of UCL’s regulations and procedures. The appeals process is evidence-based and circumstances must be supported by independent evidence.