ASSIGNMENT
This is an individual assignment and accounts for 100% of the final module mark. The assignment consists of three sections, Section A, Section B and Section C. To pass, you will need to answer the questions in all three sections (attempt all parts), and it should obviously all make good sense!
The Intended Learning Outcomes of the Assignment are:
§ To introduce to the student the fundamental concepts which can be used to understand and improve the process of decision-making.
§ Use appropriate statistical methods to analyse a variety of financial decisions.
§ Improved literature review skills.
Section A – Multicriteria Decision Analysis
The task of this part is to model, or analyse, a decision (or decisions) using:
Multi-Criteria Decision Analysis.
The purpose of your model or analysis should be to enable the decision to be made more effectively, so that the outcome is better than it would otherwise have been.
It is important that your decision should be a realistic one. As far as possible, the data (costs, criteria, weights, etc.) you use should be genuine. Where this is not possible, you should explain how you would obtain the information.
You should discuss your proposed model with your course tutor. Your course tutor will be marking the assignment, so it will be easier and helpful if you get their approval. This is to reduce the likelihood of misdirected work.
You should submit a written account of your model or analysis. This should include (not necessarily in this order):
§ Details of the decision maker(s) and the decision(s).
§ Details of the recommendations you would make to the decision maker.
§ An explanation/description of your model or analysis. This should make it clear how the model works. You should also submit all relevant spreadsheet files.
§ The source of any information (costs, criteria, etc.) incorporated in the model or analysis.
§ An evaluation of the model or analysis. This should include a critical analysis of any assumptions, and comments on the usefulness of your analysis.
§ References to books, articles, etc. that you make use of.
To pass, you will need to submit everything listed above - and it should obviously all make good sense! In addition, other things being equal, we will give more credit to ambitious projects than to very simple ones. To get a good mark, you should:
§ Do everything listed above, and
§ Pay particular attention to the evaluation of your model, the analysis of its assumptions, and the source of the information it incorporates.
Some suggestions
These are suggestions only (we are prepared to be very flexible regarding what is acceptable, but please check with us before going ahead if you are in any doubt):
§ Potential business decisions: e.g. on your proposed dot.com or other business.
§ Business decisions: only viable if you work for a business or have good access to information from a business.
§ Personal decisions: e.g. on purchases or rental of a car or a house, on choices of courses or careers, on investments, and so on. It may be appropriate to build a model that will be helpful for any person having to make these decisions, or you may just be considering a single decision maker.
Your report for Section A must not materially exceed 1,300 words in length.
Section B – Statistics and Regressions
The task is to model, or analyse, the following case study:
A manufacturer wants to analyse the relationship between sales and the amount spent on advertising on television, in magazines, and on the radio. The table below shows the amount spent on these three types of advertising and the sales for each of ten randomly selected sales months. All figures are in millions of pounds.
|
Month |
Television Advertising Expenditure |
Magazines Advertising Expenditure |
Radio Advertising Expenditure |
Sales |
|
1 |
8.2 |
4.3 |
6.0 |
361 |
|
2 |
6.2 |
4.1 |
4.8 |
340 |
|
3 |
9.7 |
5.7 |
6.4 |
379 |
|
4 |
9.1 |
3.1 |
6.0 |
369 |
|
5 |
10.4 |
2.6 |
5.1 |
366 |
|
6 |
9.0 |
3.7 |
5.0 |
365 |
|
7 |
9.1 |
4.0 |
6.0 |
373 |
|
8 |
10.7 |
4.3 |
6.3 |
379 |
|
9 |
9.4 |
4.2 |
5.4 |
362 |
|
10 |
10.3 |
6.1 |
5.9 |
388 |
(All figures are in millions of pounds. So, for example, a value of 8.2 in the Television Advertising Expenditure column would mean an expenditure £8,200,000 whereas a value of 361 in Sales column would mean sales of £361,000,000)
You are required to analyse the data above using Microsoft Excel. For parts (c-e) below, do not use the standardised Excel functions – instead you may use Excel to apply the relevant formulas learnt in the course. Specifically, you should answer the following questions:
(a) Use Excel to construct a histogram to show the distribution of Sales (you will need to choose suitable class intervals).
(b) Discuss whether this distribution is normal.
(c) Calculate the quartiles of the Sales distribution and its inter-quartile range. Explain the meaning of the lower quartile (Q1) and the inter-quartile range in the context of the example data.
(d) Calculate the values of both Kendall’s and Pearson’s correlation coefficients between Sales and Television Advertising Expenditure. Explain the meaning of a correlation coefficient in the context of the example data.
(e) Calculate the standard deviation of Sales, Television Advertising Expenditure, Magazine Advertising Expenditure, and Radio Advertising Expenditure. Explain the meaning of the standard deviation in the context of the example data.
Now do a regression analysis using the Excel Regression Tool with Sales as the dependent variable, and Television Advertising Expenditure, Magazines Advertising Expenditure, and Radio Advertising Expenditure as the independent variables.
(f) Suppose a new advertising campaign is proposed with a budget of £11m allocated to Television Advertising, £6.5m allocated to Magazine Advertising, and £7m allocated to Radio Advertising. What will be the expected sales from this new campaign?
(g) What conclusions can be drawn from the regression analysis about the effect of Television Advertising Expenditure and Magazine Advertising Expenditure on Sales? (You may find the regression coefficients in the regression output table helpful).
(h) What does the R Square value indicate about a regression model?
(i) Explain the meaning of the numbers in the p-value column of the regression output. Which of the results are significant at the 5% significance level? Outline whether any results are significant at the 10% significance level and explain the impact of a higher significance level on your conclusions.
(j) Discuss the benefits and difficulties of using multiple regression (you can refer to the results obtained in this example if you wish).
(k) Without applying any additional analysis to the data, discuss what recommendations for further analysis you would suggest to enhance your conclusions/analysis.
Your report for Section B must not materially exceed 1,400 words in length.
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