DFI5006 Assessment Brief

Task summary:

You are required to carry out a research study in which you develop a Multiple Linear Regression (MLR) model and write a report about it using the data provided for New York Stock Exchange
(see excel file titled “NYSE Fundamentals”)

Assignment instructions:

For this report, you need to choose 2 companies (2 Ticker Symbols) from the NYSE data set and then choose:

• One dependent variable.
• and, at least 6 independent variables.

All the statistical analyses must be conducted using Excel or optionally SPSS.

Once you have selected the companies and dependent and independent variables, you are required to perform the following statistical procedure:

1. Generate and present your data set in STATA spreadsheet. Include a screenshot (i.e. the “print-screen” exhibit) of your data in your assignment submission. (15 marks)
2. Examine whether there are violations of the model assumptions for Multiple Linear Regression (any 3 model assumptions). (15 marks)
3. Perform a multiple linear regression between the dependent variable and the independent variables using STATA. (20 marks)
4. Conduct a hypothesis test for the coefficients of regression. (20 marks)
5. Interpret the model summary results. (20 marks)
6. Determine whether the regression model is adequate/significant. (10 marks)

You do not need to include all your outputs but select the outputs that will support your result interpretations. All the statistical results/outputs need to be labelled and numbered for easy reference.

You should provide a Reference list and an Appendix. The latter may include the outputs of Excel/ SPSS.

(2,500 Words)
(100 Marks)
(LOs: 2, 3 and 4)

Essential assignment resources

Data should be accessed via the module iLearn page.

Learning outcomes (LO)

After completing the module, you should be able to:

1. Critically appraise the validity and reliability of quantitative and qualitative approaches within a research project
2. Identify and critically appraise quantitative methods and their application, and select those appropriate to specified project scenarios
3. Use a range of quantitative analysis methods to summarise and analyse quantitative data
4. Demonstrate an ability to define a data requirement, collect, manage and prepare quantitative data
   All learning outcomes must be met to pass the module.

You will be graded based on how well you meet these learning outcomes. Your marker will use a rubric/ marking matrix to grade your work, and you can access this by clicking on the submission portal.

Guidelines and policies

You can find links to more useful information about the assignment and university policies below.

Word/time limit policy
Referencing guidelines
Please follow the referencing guidelines that are appropriate for your degree programme. If you are unsure which you should be using, please contact your module team.

Academic integrity and misconduct policy

Statement on use of artificial intelligence on assessment

Support information