The seasonal index for the number of meat pie sales in winter is 1.75
To correct for seasonality, the actual number of meat pie sales for winter should be reduced, to the nearest whole percentage, by
- 25%
- 43%
- 57%
- 75%
Aussie Maths & Science Teachers: Save your time with SmarterEd
The seasonal index for the number of meat pie sales in winter is 1.75
To correct for seasonality, the actual number of meat pie sales for winter should be reduced, to the nearest whole percentage, by
\(B\)
| \(\text{Seasonal index}\) | \(=\dfrac{\text {actual}}{\text {deseasonalised}}\) |
| \(\text{deseasonalised}\) | \(=\dfrac{\text{actual}}{\text{seasonal index}}=\dfrac{\text{actual}}{1.75}=\text{actual} \times 0.57\) |
\(\therefore \ \text{Reduce actual figure by 43% to adjust for seasonality.}\)
\(\Rightarrow B\)
The sales revenue, in dollars, from the sale of chocolate eggs is seasonal.
To correct the sales revenue in May for seasonality, the actual sales revenue, to the nearest percent, is decreased by 17 %.
The seasonal index for that month is closest to
\(D\)
\(\text{17% decrease}\ \Rightarrow\ \text{Multiplying factor = 0.83}\)
\(\text{Seasonal index}\ = \dfrac{1}{0.83}=1.204…\)
\(\Rightarrow D\)
The time series plot below shows the average monthly ice cream consumption recorded over three years, from January 2010 to December 2012.
The data for the graph was recorded in the Northern Hemisphere.
In this graph, month number 1 is January 2010, month number 2 is February 2010 and so on.
--- 3 WORK AREA LINES (style=lined) ---
--- 2 WORK AREA LINES (style=lined) ---
| Consumption (litres/person) | ||||||||||||
| Year | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sept | Oct | Nov | Dec |
| 2011 | 0.156 | 0.150 | 0.158 | 0.180 | 0.200 | 0.210 | 0.183 | 0.172 | 0.162 | 0.145 | 0.134 | 0.154 |
--- 5 WORK AREA LINES (style=lined) ---
a. \(\text{Maximum values are 12 months apart.}\)
b. \(\text{Deseasonalised (Apr)}\ = \dfrac{0.180}{1.05} = 0.1714… = 0.17\ \text{(2 d.p.)}\)
c. \(\text{2011 monthly mean}\ =\dfrac{2.004}{12}=0.167\)
| \(\text{Seasonal index (July)}\) | \(=\dfrac{0.183}{0.167}\) | |
| \(=1.095…\) | ||
| \(=1.10\ \text{(2 d.p.)}\) |
b. \(\text{Deseasonalised (Apr)}\ = \dfrac{0.180}{1.05} = 0.1714… = 0.17\ \text{(2 d.p.)}\)
c. \(\text{2011 monthly mean}\)
\(=(0.156+0.15+0.158+0.18+0.2+0.21+0.183+0.172+\)
\(0.162+0.145+0.134+0.154)\ ÷\ 12 \)
\(=\dfrac{2.004}{12}\)
\(=0.167\)
| \(\text{Seasonal index (July)}\) | \(=\dfrac{0.183}{0.167}\) | |
| \(=1.095…\) | ||
| \(=1.10\ \text{(2 d.p.)}\) |
The seasonal index for sales of sunscreen in summer is 1.25
To correct for seasonality, the actual sunscreen sales for summer should be
\(A\)
\(\text{Deseasonalised Sales}\)
| \(= \dfrac{\text{Actual Sales}}{\text{Seasonal Index}}\) | ||
| \(=\dfrac{\text{Actual Sales}}{1.25}\) | ||
| \(=0.8 \times \text{Actual Sales}\) |
\(\therefore \ \text{Summer sales should be reduced by 20%.}\)
\(\Rightarrow A\)
The number of visitors each month to a zoo is seasonal.
To correct the number of visitors in January for seasonality, the actual number of visitors, to the nearest percent, is increased by 35%.
The seasonal index for that month is closest to
\(D\)
| \(\text{Seasonal index}\) | \(=\ \dfrac{\text{actual}}{\text{deseasonalised}} \) | |
| \(=\ \dfrac{\text{actual}}{1.35 \times \text{actual}} \) | ||
| \(=0.741\) |
\(\Rightarrow D\)
The number of visitors to a regional animal park is seasonal.
Data is collected and deseasonalised before a least squares line is fitted.
The equation of the least squares line is
deseasonalised number of visitors = 2349 – 198.5 × month number
where month number 1 is January 2020.
The seasonal indices for the 12 months of 2020 are shown in the table below.
The actual number of visitors predicted for February 2020 was closest to
`E`
`text{Deseasonalised number of visitors in February}`
`= 2349 – 198.5 xx 2`
`= 1952`
`text{Actual visitors predicted in February}`
`= 1952 xx 1.25`
`= 2440`
`=> E`
Table 4 below shows the monthly rainfall for 2019, in millimetres, recorded at a weather station, and the associated long-term seasonal indices for each month of the year.
Part 1
The deseasonalised rainfall for May 2019 is closest to
Part 2
The six-mean smoothed monthly rainfall with centring for August 2019 is closest to
`text(Part 1:)\ B`
`text(Part 2:)\ C`
Part 1
`text(Deseasonalised rainfall for May)`
`= 92.6/1.222`
`= 75.8\ text(mm)`
`=> B`
Part 2
`text{Six-mean smoothed average (Aug)}`
`=[(92.6 + 77.2 + 80 + 86.8 + 93.8 + 55.2) ÷6 +`
`(77.2 + 80 + 86.8 + 93.8 + 55.2 + 97.3) ÷ 6] ÷ 2`
`~~ (80.93 + 81.72) ÷ 2`
`~~ 81.3\ text(mm)`
`=> C`
Table 3 below shows the long-term mean rainfall, in millimetres, recorded at a weather station, and the associated long-term seasonal indices for each month of the year.
The long-term mean rainfall for December is missing.
Part 1
To correct the rainfall in March for seasonality, the actual rainfall should be, to the nearest per cent
Part 2
The long-term mean rainfall for December is closest to
`text(Part 1:)\ C`
`text(Part 2:)\ D`
Part 1
`text(Deseasonalised rainfall for March)`
`= 52.8/0.741`
`= 71.255`
| `:.\ text(Percentage increase)` | `= (71.255 – 52.8)/() xx 100` |
| `~~ 35%` |
`=> D`
Part 2
`text(Mean) = 51.9/0.728 = 71.3`
| `text(Actual December)` | `= 71.3 xx 1.072` |
| `~~ 76.4\ text(mm)` |
`=> D`
A small cafe is open every day of the week except Tuesday.
The table below shows the daily seasonal indices for labour costs at this cafe.
Excluding the day that the cafe is closed, the long-term average weekly labour cost is $9786.
The expected daily labour cost on a Wednesday is closest to
`E`
`text(Average daily labour cost)`
`= 9786/6`
`= 1631`
`:.\ text(Wednesday’s expected cost)`
`= 1631 xx 1.24`
`= $2022.44`
`=>\ E`
The total rainfall, in millimetres, for each of the four seasons in 2015 and 2016 is shown in Table 5 below.
Use the values in Table 5 to find the seasonal indices for summer, autumn and spring.
--- 4 WORK AREA LINES (style=lined) ---
Use the appropriate seasonal index from Table 6 to deseasonalise the total rainfall for winter in 2017.
Round your answer to the nearest whole number. (1 mark)
--- 2 WORK AREA LINES (style=lined) ---
| a. |
`text{Average rainfall (2015)}\ = (142 + 156 + 222 + 120)/4 = 160`
`text{Average rainfall (2016)}\ = (135 + 153 + 216 + 96)/4= 150`
| `text{SI (Summer)}` | `= 1/2 (142/160 + 135/150)=0.89` |
| `text{SI (Autumn)}` | `= 1/2(156/160 + 153/150)=1.00` |
| `text{SI (Spring)}` | `= 1/2 (120/160 + 96/150)=0.70` |
| b. | `text{Winter (deseasonalised)}` | `= 262/1.41` |
| `~~ 186\ text(mm)` |
The seasonal index for the sales of cold drinks in a shop in January is 1.6
To correct the January sales of cold drinks for seasonality, the actual sales should be
`A`
`text(Deseasonalised sales)`
`= text(Actual)/text(index)`
`= text(Actual) xx 1/1.6`
`=\ text(Actual × 62.5%)`
`:.\ text(Actual sales need to be reduced by 37.5%)`
`=> A`
Which one of the following statistics can never be negative?
`D`
`text(The value of a seasonal index cannot be negative.)`
`=> D`
The table below shows the long-term average of the number of meals served each day at a restaurant. Also shown is the daily seasonal index for Monday through to Friday.
Part 1
The seasonal index for Wednesday is 0.84
This tells us that, on average, the number of meals served on a Wednesday is
Part 2
Last Tuesday, 108 meals were served in the restaurant.
The deseasonalised number of meals served last Tuesday was closest to
Part 3
The seasonal index for Saturday is closest to
`text(Part 1:)\ A`
`text(Part 2:)\ E`
`text(Part 3:)\ D`
`text(Part 1)`
`1 – 0.84 = 0.16`
`:.\ text(A seasonal index of 0.84 tell us)`
`text(16% less meals are served.)`
`=> A`
`text(Part 2)`
`text{Deseasonalised number (Tues)}`
`= text(actual number)/text(seasonal index)`
`= 108/0.71`
`~~ 152`
`=> E`
`text(Part 3)`
`text(S)text(ince the same number of deseasonalised)`
`text(meals are served each day.)`
| `text{S.I. (Sat)}/190` | `= 1.10/145` |
| `text{S.I. (Sat)}` | `= (1.10 xx 190)/145` |
| `= 1.44…` |
`=> D`
--- 0 WORK AREA LINES (style=lined) ---
Using the appropriate seasonal index in Table 2, determine the deseasonalised value for the summer rainfall in 2008. Write your answer correct to the nearest millimetre. (1 mark)
--- 3 WORK AREA LINES (style=lined) ---
--- 2 WORK AREA LINES (style=lined) ---
a. `text(Seasonal index for Spring)`
`=4-(0.78 + 1.05 + 1.07)`
`= 1.10`
b. `text{Deseasonalised value (Summer)}`
`= 188/0.78`
`=241.02…`
`=241\ text{mm (nearest mm)}`
c. `text(The Autumn rainfall is 5% above the average)`
`text(for the four seasons of the year.)`
A garden supplies outlet sells water tanks. The monthly seasonal indices for the revenue from the sale of water tanks are given below.
The seasonal index for September is missing.
The revenue from the sale of water tanks in September 2009 was $104 500.
The deseasonalised revenue for September 2009 is closest to
A. `$42\ 800`
B. `$74\ 100`
C. `$104\ 500`
D. `$141\ 000`
E. `$147\ 300`
`B`
| `text(Seasonal index for September)` | `=12 -\ text(sum of others)` |
| `=12-10.59` | |
| `=1.41` |
`:.\ text(Deseasonalised Revenue for September)`
`= (text(actual))/(text(index))`
`= (104\ 500)/(1.41)`
`= $74\ 113.48`
`=> B`
The quarterly seasonal indices for tractor sales for a supplier are displayed in Table 1.
The quarterly tractor sales in 2014 for this supplier are displayed in Table 2.
The sales data in Table 2 is to be deseasonalised before a least squares regression line is fitted.
The equation of this least squares regression line is closest to
A. deseasonalised sales = 0.32 + 910 × quarter number
B. deseasonalised sales = 370 – 2300 × quarter number
C. deseasonalised sales = 910 + 0.32 × quarter number
D. deseasonalised sales = 2300 – 370 × quarter number
E. deseasonalised sales = 2300 – 0.32 × quarter number
`D`
The following information relates to Parts 1, 2 and 3.
The table shows the seasonal indices for the monthly unemployment numbers for workers in a regional town.
Part 1
The seasonal index for October is missing from the table.
The value of the missing seasonal index for October is
A. `0.93`
B. `0.95`
C. `0.96`
D. `0.98`
E. `1.03`
Part 2
The actual number of unemployed in the regional town in September is 330.
The deseasonalised number of unemployed in September is closest to
A. `310`
B. `344`
C. `351`
D. `371`
E. `640`
Part 3
A trend line that can be used to forecast the deseasonalised number of unemployed workers in the regional town for the first nine months of the year is given by
deseasonalised number of unemployed = 373.3 – 3.38 × month number
where month 1 is January, month 2 is February, and so on.
The actual number of unemployed for June is predicted to be closest to
A. `304`
B. `353`
C. `376`
D. `393`
E. `410`
`text (Part 1:)\ D`
`text (Part 2:)\ C`
`text (Part 3:)\ A`
`text (Part 1)`
| `text(Oct index)` | `=12-text(sum of other 11)` |
| `=12-11.02` | |
| `=0.98` |
`rArr D`
`text (Part 2)`
| `text(Deseasonalised number)` | `= text(Actual)/text(Index)` |
| `=330/0.94` | |
| `=351.06…` |
`rArr C`
`text (Part 3)`
`text(Deseasonalised number)`
`=373.3 – 3.38 xx 6`
`=353.02`
| `text(Actual number)` | `= text(Deseasonalised) xx text(Index)` |
| `=353.02 xx 0.86` | |
| `=303.59…` |
`rArr A`
The seasonal index for heaters in winter is 1.25.
To correct for seasonality, the actual heater sales in winter should be
A. reduced by 20%.
B. increased by 20%.
C. reduced by 25%.
D. increased by 25%.
E. reduced by 75%.
`A`
`text(Deseasonalised Sales)`
`=\ text(Actual Sales)/text(Seasonal index)`
`=\ text(Actual Sales)/1.25`
`=80 text(%) xx text(Actual Sales)`
`∴\ text(Winter Sales should be reduced by 20%)`
`=> A`
The seasonal indices for the first 11 months of the year, for sales in a sporting equipment store, are shown in the table below.
Part 1
The seasonal index for December is
A. `0.89`
B. `0.97`
C. `1.02`
D. `1.23`
E. `1.29`
Part 2
In May, the store sold $213 956 worth of sporting equipment.
The deseasonalised value of these sales was closest to
A. `$165\ 857`
B. `$190\ 420`
C. `$209\ 677`
D. `$218\ 322`
E. `$240\ 400`
`text(Part 1:)\ E`
`text(Part 2:)\ E`
`text(Part 1)`
`text(Sum of seasonal indices) = 12`
`:.\ text(December’s seasonal index)`
| `=12 – text{(1.23 + 0.96 + 1.12 + 1.08 + 0.89}` |
| `text{+ 0.98 + 0.86 + 0.76 + 0.76 + 0.95 + 1.12)}` |
| `=1.29` |
`=>E`
`text(Part 2)`
| `text(May Index)` | `=\ text(Actual Sales)/text{Deseasonalised Sales (D)}` |
| `0.89` | `= (213\ 956)/(text{D})` |
| `:.\ text(D)` | `= (213\ 956)/0.89` |
| `= $240\ 400` |
`=> E`
The seasonal index for headache tablet sales in summer is 0.80.
To correct for seasonality, the headache tablet sales figures for summer should be
A. reduced by 80%
B. reduced by 25%
C. reduced by 20%
D. increased by 20%
E. increased by 25%
`E`
`text(Deseasonalised Sales)`
| `= \ \ text(Actual Sales) / text(Seasonal index)` |
| `= \ \ text(Actual Sales) / 0.8` |
| `= \ \ 1.25 xx text(Actual Sales)` |
`:.\ text(Deseasonalised Sales need Actual Sales to)`
`text(be increased by 25%)`
`=> E`
A trend line was fitted to a deseasonalised set of quarterly sales data for 2012.
The seasonal indices for quarters 1, 2 and 3 are given in the table below. The seasonal index for quarter 4 is not shown.
The equation of the trend line is
`text(deseasonalised sales) = 256 000 + 15 600 xx text(quarter number)`
Using this trend line, the actual sales for quarter 4 in 2012 are predicted to be closest to
A. `$222\ 880`
B. `$244\ 923`
C. `$318\ 400`
D. `$382\ 080`
E. `$413\ 920`
`E`
`text {Deseasonalised Sales (Q4)}`
| `= 256\ 000 + 15 600 xx 4` |
| `= 318\ 400` |
`text (Seasonal index (Q4))`
| `= 4 – (1.2 + 0.7 + 0.8)` |
| `= 1.3` |
`:.\ text (Actual Sales (Q4))`
| `=\ text(Deseasonalised Sales × seasonal index)` |
| `= 318\ 400 xx 1.3` |
| `= 413\ 920` |
`rArr E`
Use the following information to answer Parts 1 and 2.
The table below shows the long-term average rainfall (in mm) for summer, autumn, winter and spring. Also shown are the seasonal indices for summer and autumn. The seasonal indices for winter and spring are missing.
Part 1
The seasonal index for spring is closest to
A. `0.90`
B. `1.03`
C. `1.13`
D. `1.15`
E. `1.17`
Part 2
In 2011, the rainfall in autumn was 48.9 mm.
The deseasonalised rainfall (in mm) for autumn is closest to
A. `48.4`
B. `48.9`
C. `49.4`
D. `50.9`
E. `54.0`
`text (Part 1:)\ C`
`text (Part 2:)\ A`
`text (Part 1)`
`text (Average Seasonal Rainfall)`
`= (52.0 + 54.5 + 48.8 + 61.3)/4`
`=54.15`
`:.\ text {Seasonal index (Spring)}`
`= 61.3/54.15`
`= 1.132…`
`rArr C`
`text (Part 2)`
`:.\ text {Deseasonalised Rainfall (Autumn)}`
`= 48.9/1.01`
`=48.415`
`rArr A`