ာ
ော Kyu Kyu Hlaing
Daw
Institute of Accounting and Finance
ACCA - F5 - PM ( Question Kit )
Daw Aye Sakawar Kyaw ( F5 (3 Months) Class )
F5 - PM Kaplan Kits Selection
Section A
1.
Z plc has found that it can estimate future sales using time series analysis and regression
techniques. The following trend equation has been derived:
y = 25,000 + 6,500x
where ‘y’ is the total sales units per quarter and ‘x’ is the time period reference number.
Z has also derived the following set of seasonal variation index values for each quarter using
a multiplicative (proportional) model:
Quarter 1
70%
Quarter 2
90%
Quarter 3
150%
Quarter 4
90%
Assuming that the first quarter of year 1 is time period reference number 1, what would
be the forecast sales units for the third quarter of year 7?
A 174,500 units
B 200,500 units
C 261,750 units
D 300,750 units
2.
Hi-Tech plc is a mobile phone repair shop. The director is interested in the relationship between
the amount that is spent on advertising and the sales revenue that is achieved.
They have established that the regression equation y = 150 + 7x can be used, where y is the
total sales revenue and x is the advertising spend (in $000s).
Which ONE of the following statements is a correct interpretation of the relationship?
A If $7,000 is spent on advertising, then sales revenue would be $150,000
B If no money is spent on advertising, then sales revenue would be $7,000
C If $150,000 is spent on advertising, then $7,000 sales revenue per month will be generated
D If no money is spent on advertising, then sales revenue would be $150,000
3.
Production overhead costs at company MZ Ltd are assumed to vary in line with the number of
labour hours worked. The following data has been collected:
Year
Total production
Number of
Cost index
overheads
labour hours
$
20X4
205,000
4,200
189
20X5
226,180
4,700
205
If the line of best fit, based on current (20X5) prices is calculated as y = 127,000 + 18.6x, calculate
the total expected overhead costs in 20X6 if expected production activity is 4,600 labour hours
and the expected cost index is 225.
A $193,666
B $212,560
C $233,298
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Daw Kyu Kyu Hlaing
Institute of Accounting and Finance
ACCA - F5 - PM ( Question Kit )
Daw Aye Sakawar Kyaw ( F5 (3 Months) Class )
D $253,048
4.
The overhead costs of RP have been found to be accurately represented by the formula:
y = $10,000 + $0.25x
where y is the monthly cost and x represents the activity level measured as the number of
orders.
Monthly activity levels of orders may be estimated using a combined regression analysis
and time series model:
a = 100,000 + 30b
where a represents the de-seasonalised monthly activity level and b represents the month
number.
In month 240, the seasonal index value is 108.
The overhead cost for RP for month 240 is $_________ (round to the nearest $1,000)
5.
Monthly sales of product R follow a linear trend of y = 9.72 + 5.816x, where y is the number of
units sold and x is the number of the month. Monthly deviations from the trend follow an
additive model.
The forecast number of units of product R to be sold in month 23, which has a seasonal
factor of +6.5 is, to the nearest whole unit:
A 134
B 137
C 143
D 150
6.
A company has calculated that the coefficient of determination between output and production
costs over a number of months is 89%.
Which TWO of the following comments are correct?
Correct?
89% of the variation in production costs from one month to the next can be
explained by corresponding variation in output.
Costs increase as output increases.
The linear relationship between output and costs is very strong.
An increase of 100% in output is associated with an increase of 89% in costs.
An increase of 89% in output is associated with an increase of 100% in costs.
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ာ
ော Kyu Kyu Hlaing
Daw
Institute of Accounting and Finance
ACCA - F5 - PM ( Question Kit )
Daw Aye Sakawar Kyaw ( F5 (3 Months) Class )
Section B
(1) .BELLAMY CO
Bellamy Co is negotiating with marketing companies with regard to its marketing campaign
on its ‘Muse’ product. Bellamy Co is uncertain as to what the total marketing costs will be
each year. However, the following information is available, as the sales manager has provided
the data for past marketing expenditure and sales on the ‘Muse’ product.
The sales manager has plotted both of these variables on a scatter plot with the marketing
expenditure from the last 12 quarters on the x-axis, and the associated sales (in $’000) on the
y-axis:
1. Which ONE of the following statements about the above graph is true?
A The graph shows a positive correlation, which means that low values of sales are associated
with low values of marketing expenditure, and high values of sales are associated with high
values of marketing expenditure.
B The graph shows a positive correlation, which means that low values of sales are associated
with the high values of marketing expenditure, and high values of sales are associated with low
values of marketing expenditure.
C The graph shows a negative correlation, which means that low values of sales are associated
with the low values of marketing expenditure, and high values of sales are associated with high
values of marketing expenditure.
D The graph shows a negative correlation, which means that low values of sales are associated
with the high values of marketing expenditure, and high values of sales are associated with low
values of marketing expenditure.
2. The linear relationship between marketing spend in thousands of dollars and sales in tens of
thousands of dollars was correctly established by the sales manager at Y = 5 + 2X.
Which TWO of the following statements are true?
(1) For every $1,000 spent on marketing, sales revenue increases by $50,000 on average.
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Daw Kyu Kyu Hlaing
Institute of Accounting and Finance
ACCA - F5 - PM ( Question Kit )
Daw Aye Sakawar Kyaw ( F5 (3 Months) Class )
(2) When nothing is spent on marketing, the average sales level is $50,000.
(3) For every $1,000 spent on marketing, sales revenue increases by $20,000 on average.
(4) When nothing is spent on marketing, the average sales level is $20,000.
A (1) and (2)
B (1) and (4)
C (2) and (3)
D (3) and (4)
3. The correlation coefficient between marketing expenditure and sales revenue is calculated to
be 0.85
Which of the following statements are true?
A There is a weak relationship between marketing expenditure and sales revenue.
B 85% of the variation in sales revenue can be explained by the corresponding variation in
marketing expenditure.
C 72% of the variation in sales revenue can be explained by the corresponding variation in
marketing expenditure.
D Sales revenue will increase by 85% more than marketing expenditure will increase.
You have been asked to use regression analysis to forecast the impact of future marketing
expenditure on sales.
4. Which TWO of the following are underlying assumptions of forecasts made using regression
analysis?
(1) A curvilinear relationship exists between the two variables.
(2) The value of one variable can be predicted or estimated from the value of one other variable.
(3) A perfect linear relationship between the two variables.
(4) What has happened in the past will provide a reliable guide to the future.
A (1) and (2)
B (1) and (4)
C (2) and (4)
D (3) and (4)
5. Which of the following would affect the reliability of a forecast using linear regression?
(1) The amount of data on which the regression line is based.
(2) The assumption that the trend line applies outside the range of X values used to establish
the line in the first place.
(3) The assumption that there is a linear relationship between the two variables.
(4) The coefficient of correlation.
A (1) and (2)
B (3) and (4)
C (1), (2), (3)
D (1), (2), (3) and (4)
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