32427 Numeracy Statistical Analysis & Financial Literacy B

Java Python Assignment Remit

Module Learning Outcomes:

This assignment is designed to assess the following module learning outcomes. Your submission will be marked using the Grading Criteria given in the section below.

LO 1. Demonstrate knowledge of descriptive statistics

LO 2. Demonstrate knowledge of hypothesis testing

LO 3. Demonstrate knowledge and understanding of the measure of association between two random variables

Assignment:

This assessment constitutes 30% of the total evaluation for the module. To gauge your current skills, you are expected to complete the following tasks. An electronic copy of these exercises should be submitted as required using Word or any other appropriate software package (converting it into PDF format is recommended). The cover sheet should include the title "Statistics Summary Exercise 2023," along with your student ID number and degree programme. Please submit only one file.

You will need the Excel worksheet titled "Stock  Returns Data Sample 2023" to successfully complete this exercise. This can be found on Canvas. Prepare a report of no more than 750 words (tables are not inc 32427 Numeracy, Statistical Analysis & Financial Literacy B luded in the word count) that summarises the investigated data. Your work should be presented clearly and concisely, utilising as many mathematical techniques as possible that have been covered this term.

Tasks

1.   Estimate the descriptive statistics of the data for both companies and the index, and provide commentary on various statistics,  including measures such as mean, standard deviation, skewness, and kurtosis. (10 marks)

2.   Using a 5% significance level, test whether a significant difference exists in the mean returns of the two stocks, GE and AAPL. You need to show the details of how you conduct the hypothesis test. (10 marks)

3.   Imagine you are an investment manager currently holding the index. Would these

two stocks, GE and AAPL, be suitable as target stocks for diversifying your portfolio's risk? Why? (10 marks)

Grading Criteria / Marking Rubric

Your submission will be graded according to the following criteria:

This is a quantitatively based assignment, and your ability to apply mathematical and statistical methods will be a key part of the assessment. The following criteria will be used to evaluate your work:

1.   10 marks for Task 1, 10 marks for Task 2, and 10 marks for Task 3 (Total 30 marks). Marks will be awarded based on how well you address the specific requirements of each task.

2.   A good report will demonstrate your  understanding  of key concepts, theories, and knowledge covered during the module. Strong responses will accurately apply statistical principles and mathematical techniques relevant to the data.

3.   Support  your discussion with relevant data and statistical evidence from the dataset provided. Clear and correct use of quantitative data is essential, and interpretations should align with the statistical findings.

4.   The report should be well-organised and clearly written, with logical flow between sections. Proper formatting of tables is expected, along with concise yet comprehensive explanations of your findings.

5.   Demonstrate critical thinking in your quantitative analysis. A strong report will go beyond basic calculations, offering thoughtful insights and original commentary, particularly in Task 3, where you evaluate portfolio diversification.

Ethical Use of Generative AI (GenAI)

You are permitted to use GenAIto support  your submission for this assessment. You may use it for the following activities:

•    Researching and refining your ideas

•    Information retrieval or background research

•    Drafting an outline to organise or summarise your thoughts

•    Refining research questions

•   Checking spelling and grammar

Applying GenAI tools should  be done with human oversight and control. You should carefully review and use the results carefully as AI can generate authoritative-sounding output that can be incorrect, incomplete, uncritical, or biased.

You may not submit any work generated by an AI tool as your own. Where you include any material generated by an AI tool, it should be properly declared just like any other reference material. Alongside your assignment you should also provide a commentary in the Cover  Sheet detailing how GenAI has been used to develop your final submission. If you have not used GenAI tools, you should clearly state so.

Plagiarism, including that which results from using GenAI, is a form of academic misconduct that will be dealt with under the University’s Code of Practice on Academic Integrity.

https://intranet.birmingham.ac.uk/as/registry/policy/conduct/plagiarism/index.aspx

University guidance on ethical use of GenAI can be found here: