Bristol Business School
Academic Year: 09/10
Assessment Period: August
Assessment Type: Referral Coursework
Module Leader:
Narges Dailami (UWE), Ng Foon Lee (Taylors)
Module Number:
UFQEEP-20-1
Module Name:
Business Statistics and Decision Making for Accounting
Word Limit:
2,500
Coursework Submission Date and Time:
Assignments are to be submitted by 2pm Monday 16 August 2010 at the Programmes
Office. Please be aware that there is NO 24hr or 10 day window this year.
Deadline:
Monday 16 August 2010
14:00
Assignment Instruction:
Referral Coursework Assignment for 2010
The objective of this assignment is to assess your ability to analyse a business problem by
selecting appropriate quantitative techniques for analysis; carrying out a technically correct
analysis; effectively communicating the results of your analysis.
Your Assignment task:
You have recently graduated from UWE and are now employed as a quantitative analyst
with Cape Virgo, a consultancy firm that carries out specialist assignments for corporate
clients. Your boss, an Account Manager, recently received the e-mails below from two
clients asking Cape Virgo for assistance. She has asked you to draft a paper reply to the emails using appropriate software.
Think carefully about the approach and model you might use to analyse the problems.
Consider the nature of the reader and use your own good judgement in drafting appropriate
replies which, as you will see from the marking scheme, count for 40% of the assignment's
marks. This task assesses your ability to effectively communicate the results of an analysis,
and not just your technical competence in modelling and interpreting results. You may use
Excel.
The breakdown of marks is:
Correct choice of method 10%
Correct analysis including suitable use of a spreadsheet 50%
Content, clarity and appropriateness of your written reply 40%
Marking criteria:
The following criteria will be used to award marks for your report:



Appropriateness and quality of the data analysis for the chosen model.
Appropriateness and quality of the charts used to present the results of the analysis.
The quality of the communication in the report.
E-mail 1
To: Angela Davis, Project Manager, Ascom.
From: John Horne, Sales Director, Health Foods plc.
Subject: Sales Forecast for Babolious
We’ve been having a lot of trouble in recent years with forecasting sales for Babolious our
established bedtime drink product. As you know, it is one of our best-selling products and a
key contributor to our profitability.
Last year, we severely underestimated our winter sales of Babolious and lost sales to
competitors, albeit temporarily. Then, trying to correct this situation, we produced far more
than we could sell in the summer, and were left with a lot of unsold stock on our hands that
we had to sell at a loss. In other words, our sales forecasting is in a mess, and we need
some help for the coming year. If we don’t get our forecasts right, we risk losing market
share permanently.
Our quarterly sales figure for the last five years were:
Year Quarter Sales (£'000s)
Year
Quarter
2005
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
2006
2007
2008
2009
2010
Sales
(£’000s)
51,838
64,273
102,854
74,380
67,373
91,297
132,412
97,588
91,205
123,778
149,691
117,926
103,467
144,944
186,974
136,014
128,901
174,575
235,741
170,806
Above all, I need:
a) a presentation quality graph showing all the sales data over the last five years, that I could
show at our sales meeting next week.
b) a complete trend and seasonality analysis of the sales data, in straightforward language.
c) sales forecasts for each of the next four quarters, starting with Q3 of 2010, with a simple
explanation of how you arrived at the forecasts.
John Horne.
E-mail 2
The price of a car usually depends on its age. You are to use an Excel spreadsheet to
investigate whether a linear regression model could be used to predict the price of a
particular second-hand model given its age.
Choose a common make and model of car and obtain a set of prices for second-hand cars
of this make & model, together with the age (in years) of each one. These should be real
prices of real cars as advertised in something like the local free paper or the Internet. Do not
use average prices from the price-guide sections of car magazines or web sites. Your
sample should consist of 20 cars ranging in age from 1 year to 5 years. You must keep the
adverts you use and paste them into an appendix to your assignment (including print-outs of
any web-pages).
Your investigation should form the basis of a short word-processed report (maximum 500
words plus figures, tables, etc.). In this report you should include a scatter plot of the data
(identifying any unusual points), and any further information you judge from your study in this
module to be pertinent and useful. Use your regression equation to predict the cost of your
chosen model of car aged 2, 7 and 10 years old. Comment on the practical significance of
your analysis in an intelligent and thoughtful manner using language that can be readily
understood by someone not familiar with technical terminology of regression analysis.
Discuss any reservations you have regarding using this technique for your investigation.
Attach an appendix for a table of the collected data (containing just the age and price of
each car), your adverts and a printout of your worksheet.
-o0o