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5.04 Time series data in tables

Interactive practice questions

The projected quarterly sale figures for a manufacturer of solar panels for three years in millions of Australian dollars are shown in the table below. Without actually calculating the figures, estimate the answer to the following questions.

  Q1 Q2 Q3 Q4
2019 $0.73$0.73 $0.85$0.85 $1.07$1.07 $1.36$1.36
2020 $0.68$0.68 $0.84$0.84 $1.25$1.25 $1.28$1.28
2021 $0.84$0.84 $0.92$0.92 $1.41$1.41 $1.53$1.53
a

The seasonal index for Q1 is:

$82.02%$82.02%

A

$70.49%$70.49%

B

$116.73%$116.73%

C

$90.08%$90.08%

D

$130.76%$130.76%

E
b

The seasonal index for Q4 is:

$82.02%$82.02%

A

$70.49%$70.49%

B

$116.73%$116.73%

C

$90.08%$90.08%

D

$130.76%$130.76%

E
c

The most appropriate moving average to smooth this data would be:

3MA

A

4CMA

B

6CMA

C

12CMA

D
d

The underlying trend in this data is that it is:

Increasing

A

Decreasing

B

Remaining stable

C
Easy
2min

Data on dam levels is collected tri-annually in January, May and September over a period of $4$4 years. The seasonal indices are calculated in order to deseasonalise the data.

Easy
2min

Data on the number of traffic accidents at a set of traffic lights is collected weekly over a period of $6$6 months. The seasonal indices are calculated in order to deseasonalise the data. Assume there are $4$4 seasons in a cycle.

Easy
2min

Data on the number of cartons of chocolate milk sold at the school canteen is collect every day over a $6$6 week time period. The seasonal indices for each day are calculated in order to deseasonalise the data.

Easy
2min
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Outcomes

ACMGM089

smooth time series data by using a simple moving average, including the use of spreadsheets to implement this process

ACMGM090

calculate seasonal indices by using the average percentage method

ACMGM091

deseasonalise a time series by using a seasonal index, including the use of spreadsheets to implement this process

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