Hello, dear friend, you can consult us at any time if you have any questions, add WeChat: daixieit

FINN1037

Quantitative Methods II

Undergraduate Programmes2023/24

SUMMATIVE ASSIGNMENT

Investigate and model the relationship between two or more quantitative variables in a dataset. You have two options for selecting your dataset:

Your Own Dataset: Select a dataset containing at least 50 observations with two quantitative variables that you believe might have a relationship.

Provided Dataset (Salary_Data.csv): Use the provided dataset, which includes the following columns: Age, Gender, Education Level, Job Title, Years of Experience, Salary. For this dataset, focus exclusively on the quantitative variables (Age, Years of Experience, Salary) as the other variables are categorical and outside the scope of the methods covered in this module.

In your work you should:

(1) For those choosing their own dataset, provide a brief description and rationale for your choice.

(2) Regardless of dataset choice, explain why you believe there might be a relationship between the two variables you plan to discuss.

(3) Conduct a descriptive statistical analysis for your chosen variables. Include measures of central tendency and dispersion. Create visualizations to represent the distribution of and relationship between your chosen variables.

(4) Calculate and interpret the correlation coefficient for your variables to assess the strength and direction of their relationship.

(5) Develop a statistical model to quantify the relationship between your chosen variables. Your model should aim to predict one variable based on the other. Provide an interpretation of the model parameters and assess the model's fit and predictive power.

(6) Discuss your findings in depth. What does the relationship between these variables suggest? How might this insight be valuable in the context of the chosen dataset? Explain the limitations of your approach.

AI-related instructions:

For this assignment, you are required to use generative AI and adhere to the following:

(1) Clearly indicate in the text where generative AI has been utilized.

(2) Include the generative AI source using this format: Author, Year (Version), Name of AI, [Description], Publisher, URL (if applicable), e.g.:

OpenAI (2023a). ChatGPT 4 (version 12/05/2023) [Large language model]. https://chat.openai.com/auth/login

(3): Include a reflective section (minimum 150 words) on usage of generative AI. This section should critically evaluate:

· Where AI was most and least effective.

· The impact of AI on the depth, coherence, and quality of your essay.

(4): Include an appendix that will serve as your "Journal of Prompts." The journal should be formatted to include:

· The source of the generative AI used (e.g., OpenAI, Year).

· Each initial and follow-up prompts posed to the generative AI.

· The corresponding outputs from the generative AI for each prompt.

Note:

The reflective section must be your original work and is subject to AI-detection techniques. The reflective section counts towards the overall word count, which remains capped at 1500 words. Journal of Prompts in Appendix does not contribute to the word count but must be included for transparency. Failure to disclose AI use may be treated as an academic offense under Section IV of the General Regulations.

Overall word limit: 1500 words

SUBMISSION INSTRUCTIONS

Your completed assignment must be uploaded to Ultra
no later than 12:00 midday on 25 April 2024

The assignment should be submitting using one of the following file types: .doc, docx or .pdf

A penalty will be applied for work uploaded after 12:00 midday as detailed in the Student Information Hub. You must leave sufficient time to fully complete the upload process before the deadline and check that you have received a receipt. At peak periods, it can take up to 30 minutes for a receipt to be generated.

Assignments should be typed, using 1.5 spacing and an easy-to-read 12-point font. Assignments and dissertations/business projects must not exceed the word count indicated in the module handbook/assessment brief.

The word count should:

§ Include all the text, including title, preface, introduction, in-text citations, quotations, footnotes and any other items not specifically excluded below.

§ Exclude diagrams, tables (including tables/lists of contents and figures), equations, executive summary/abstract, acknowledgements, declaration, bibliography/list of references and appendices. However, it is not appropriate to use diagrams or tables merely as a way of circumventing the word limit. If a student uses a table or figure as a means of presenting his/her own words, then this is included in the word count.

Examiners will stop reading once the word limit has been reached, and work beyond this point will not be assessed. Checks of word counts will be carried out on submitted work, including any assignments or dissertations/business projects that appear to be clearly over-length. Checks may take place manually and/or with the aid of the word count provided via an electronic submission. Where a student has intentionally misrepresented their word count, the School may treat this as an offence under Section IV of the General Regulations of the University. Extreme cases may be viewed as dishonest practice under Section IV, 5 (a) (x) of the General Regulations.

Very occasionally it may be appropriate to present, in an appendix, material which does not properly belong in the main body of the assessment but which some students wish to provide for the sake of completeness. Any appendices will not have a role in the assessment - examiners are under no obligation to read appendices and they do not form part of the word count. Material that students wish to be assessed should always be included in the main body of the text.

Guidance on referencing can be found on Durham University website and in the Student Information Hub.

MARKING GUIDELINES

Performance in the summative assessment for this module is judged against the following criteria:

· Relevance to question(s)

· Organisation, structure, and presentation

· Depth of understanding

· Analysis and discussion

· Use of sources and referencing

· Overall conclusions