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Tutorial Set 3 TS

The document outlines a tutorial set for Quantitative Methods II at the University of Cape Coast, focusing on revenue planning and forecasting techniques using historical data. It includes exercises on calculating moving averages, exponential smoothing, trend analysis, and predicting customer numbers and sales using various models. Additionally, it covers seasonal variations in GDP and sales forecasting for a bookshop, emphasizing practical applications of quantitative methods in business.

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derrickbosteng
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10 views3 pages

Tutorial Set 3 TS

The document outlines a tutorial set for Quantitative Methods II at the University of Cape Coast, focusing on revenue planning and forecasting techniques using historical data. It includes exercises on calculating moving averages, exponential smoothing, trend analysis, and predicting customer numbers and sales using various models. Additionally, it covers seasonal variations in GDP and sales forecasting for a bookshop, emphasizing practical applications of quantitative methods in business.

Uploaded by

derrickbosteng
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as PDF, TXT or read online on Scribd
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UNIVERSITY OF CAPE COAST

COLLEGE OF HUMANITIES AND LEGAL STUDIES


SCHOOL OF BUSINESS
DEPARTMENT OF FINANCE

QUANTITATIVE METHODS II TUTORIAL SET 3 FOR ALL GROUPS


1. The manager of Peter & Sons Company Ltd is preparing the revenue plans for the last
quarter of 2018 and the first three quarters of 2019. The data below is the revenue
streams generated in the quarters of three successive years.
Years/ Q1 Q2 Q3 Q4
Quarter
2016 49 37 58 67
2017 50 38 59 68
2018 51 40 60 55
a. Calculate the 3 quarterly moving averages for this set of data
b. Calculate the 4 quarterly moving averages for this set of data
c. Calculate the exponential smoothening for the four quarters of 2016 to 2018, if the
first forecast is 45 and the smoothening constant is 0.2.

2. Miss Afia operates a restaurant on campus. She has observed that the most demanding
time at her restaurant is lunchtime, followed by breakfast and then supper. She operates
the restaurant only on Mondays, Wednesdays and Fridays. The following data relates
to the number of customers served at the restaurant during the past week's breakfast,
lunch, and supper hours.
Day Breakfast Lunch Supper
Monday 90 125 87
Tuesday 76 131 70
Wednesday 87 112 69
Required
a. Using the additive model of trend and seasonality, predict the number of customers
she should expect for breakfast, Lunch, and Supper on Monday in the coming week.
b. Using the multiplicative model of trend and seasonality, predict the number of
customers she should expect for breakfast, Lunch, and Supper on Wednesday in the
coming week.
c. Explain two uses of Time series in business.
3. The managers of a company are preparing revenue plans for the four quarters of 2015.
The table below shows the sales of one of their main products:
Year Q1 Q2 Q3 Q4
GHS GHS GHS GHS
2010 49 37 58 67
2011 50 38 59 68
2012 51 40 60 70
2013 50 42 61 69
a. Use the method of regression to calculate the trend for this set of data.
b. Forecast the sales revenue for the four quarters of 2014 using additive and
multiplicative models of trend and seasonality.

4. The following data relates to the yearly seasonal variations of GDP (in million GHS)
in Agogo State over the past twelve years:
Years Recession Boom Normal
2010 -2012 10 37 35
2013 - 2015 20 47 45
2016 - 2018 30 57 55
a. Predict the GDP for the years 2019 – 2021 using the ratio to moving average
method.
b. Provide a forecast for the years 2022 – 2024 using the multiplicative model of trend
and seasonality.
5. The following data relates to the quarterly sales (in units) of books from Grace
Bookshop in Cape Coast. The sales show a marked seasonal pattern:
Year Q1 Q2 Q3 Q4
2001 246 216 214 280
2002 230 310 320 228
2003 290 250 302 258
2004 312 330 210 290
Required: Forecast the sales value of the four quarters of 2005 using the following
approaches:
a. The ratio to moving average method
b. The additive and multiplicative model of trend and seasonality.

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