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12 FURTHER MATHEMATICS Investigating and modelling time series Investigating and modelling time series Time series data are a special kind of bivariate

data, where the explanatory variable is time. An example of time series data is the following table of Australian annual birth rates (average births per female) between 1931 and 2002. This dataset is rather complex, and it is hard to see any patterns just by looking at the data. However, we can start to make sense of the data by displaying it graphically. The graph we

use for this purpose is called a time series plot. A time series plot is a line graph with time plotted on the horizontal axis. The variable under investigation, the response variable, is plotted on the vertical axis. Investigating and modelling time

series Investigating and modelling time series Investigating and modelling time series

Investigating and modelling time series Each of these trends can be explained by changing socioeconomic circumstances. Trend 1: Between 1940 and 1961 the birth rate in Australia grew quite dramatically. Those in the armed services came home from the Second World War, and the economy grew quickly. This

rapid increase in the Australian birth rate during this period is known as the Baby Boom. Trend 2: From about 1962 until 1980 the birth rate declined very rapidly. Birth control methods became more effective, and women started to think more about careers. This period is sometimes referred to as the Baby Bust. Trend 3: During the 1980s, and up until the early

2000s, the birth rate continued to decline slowly for a complex range of social and economic reasons. Investigating and modelling time series Investigating and modelling time

series The upward sloping trend line signals the presence of a general increasing trend. This tells us that, even though demand for accommodation has fluctuated from month to month, demand for hotel and motel accommodation has increased over time. Investigating and modelling time

series The plot reveals an abrupt change in power usage in June to July. During this period, monthly power use suddenly decreases from around 300 kWh per month from January to June to around 175k Wh for the rest of the year. This is an example of structural change that can probably be explained by a change in tenants, from a

family with two children to a person living alone. Structural change is also displayed in the birth rate time series plot we saw earlier. This revealed three quite distinct trends during the period 19002010. These reflect significant external events (like a war) or changes in social and economic circumstances. One consequence of structural change is that we can no

longer use a single mathematical model to describe the key features of a time series plot. Investigating and modelling time series For this household, daily electricity use follows a regular pattern that, although

fluctuating, averages about 10 kWh per day. In terms of daily power use, day 4 is a clear outlier, with less than 2 kWh of electricity used. A follow-up investigation found that, on this day, the house was without power for 18 hours due

to a storm, so much less power was used than normal. Investigating and modelling time series Investigating and modelling time

series Investigating and modelling time series Investigating and modelling time series

Investigating and modelling time series WORK TO BE COMPLETED Exercise 6A Odd Numbered Questions

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