 # 5.06 Further applications

Lesson

Further applications can require us to apply our knowledge across the different topics in this chapter and recognise the appropriate techniques to apply. Applications may entail use of the following skills:

• Recognising a cyclical pattern in a time series graph and stating the appropriate moving average.
• Recognising an outlier or fluctuation in a time series graph.
• Plotting data points for a time series graph.
• Smoothing data using an odd or even moving average.
• Using a moving average formula to find an unknown data value.
• Using the average percentage method to calculate seasonal indices.
• Smoothing data by using seasonal indices to deseasonalise data.
• Using formula for cycle mean, percentage of cycle mean and seasonal index to find a missing value in a table of data.
• Using a calculator to determine the equation of a least-squares regression line for time series data that has been smoothed either using the moving average data OR deseasonalised data.
• Predicting a future value by using the least-squares regression line formula and then multiplying the result by the seasonal index.
• Commenting on the reliability of the prediction by observing how close the time period is to the existing time series data (within one cycle is considered reliable).

#### Practice questions

##### question 1

The table below shows some time series data where $t$t represents time.

 $t$t $y$y $1$1 $2$2 $3$3 $4$4 $5$5 $6$6 $12$12 $14$14 $22$22 $11$11 $16$16 $25$25
1. Calculate the $5$5 point moving average at $t=3$t=3.

2. Calculate the $4$4 point centred moving average at $t=3$t=3.

##### question 2

A cat boarding kennel records its number of boarders every $4$4 months (tri-annually) ending at January, May and September. The data of the cat numbers, together with some calculations are shown in the table below.

Year Data number ($d$d) Trimester Number of boarders Yearly mean Percent of yearly mean Deseasonalised figure
2017 $1$1 Jan $64$64 $50.33$50.33 $127.2%$127.2% $48$48
$2$2 May $52$52 $103.3%$103.3% $54$54
$3$3 Sept $35$35 $C$C $48$48
2018 $4$4 Jan $A$A $50$50 $144.0%$144.0% $55$55
$5$5 May $45$45 $90.0%$90.0% $D$D
$6$6 Sept $33$33 $66.0%$66.0% $46$46
2019 $7$7 Jan $78$78 $B$B $125.1%$125.1% $59$59
$8$8 May $58$58 $93.1%$93.1% $61$61
$9$9 Sept $51$51 $81.8%$81.8% $70$70
1. Determine the value $A$A.

2. Determine the value $B$B.

3. Determine the value $C$C.

4. If the seasonal index for May is $0.9545$0.9545, determine the value $D$D.

##### question 3

A cat boarding kennel records its number of boarders every $4$4 months (tri-annually) ending at January, May and September.

1. The seasonal indices are shown in the table below. Complete the table by finding the seasonal index for January.

 Trimester Seasonal indices Jan May Sept $\editable{}$ $0.9545$0.9545 $0.7245$0.7245

##### question 4

A cat boarding kennel records its number of boarders every $4$4 months (tri-annually) ending at January, May and September. The data of the cat numbers, together with some calculations are shown in the table below.

Year Data number ($d$d) Trimester Number of boarders Yearly mean Percent of yearly mean Deseasonalised figure
2017 $1$1 Jan $64$64 $50.33$50.33 $127.2%$127.2% $48$48
$2$2 May $52$52 $103.3%$103.3% $54$54
$3$3 Sept $35$35 $69.5%$69.5% $48$48
2018 $4$4 Jan $72$72 $50$50 $144.0%$144.0% $55$55
$5$5 May $45$45 $90.0%$90.0% $47$47
$6$6 Sept $33$33 $66.0%$66.0% $46$46
2019 $7$7 Jan $78$78 $62.33$62.33 $125.1%$125.1% $59$59
$8$8 May $58$58 $93.1%$93.1% $61$61
$9$9 Sept $51$51 $81.8%$81.8% $70$70
1. The equation of the least-squares line for the deseasonalised figures against data number is determined to be $y=2.0333d+44.0556$y=2.0333d+44.0556

Predict the number of cat boarders for September 2020. Round your answer to the nearest whole number.

2. Comment on the reliability of your prediction.

It's reliable because the prediction was made within one cycle of the data.

A

It's unreliable because the prediction was made beyond one cycle of the data.

B

It's reliable because the prediction was made within one cycle of the data.

A

It's unreliable because the prediction was made beyond one cycle of the data.

B

### Outcomes

#### ACMGM088

describe time series plots by identifying features such as trend (long term direction), seasonality (systematic, calendar-related movements), and irregular fluctuations (unsystematic, short term fluctuations), and recognise when there are outliers; for example, one-off unanticipated events

#### 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

#### ACMGM092

fit a least-squares line to model long-term trends in time series data