Line graphs

Line graphs display data where both variables are continuous.

The table shows some data for alcohol-related deaths per 100 000 of the female population in England and Scotland between 2006 and 2013.

YearEnglandScotland
20069.319.6
20079.516.9
20089.416.7
20099.116.7
20108.915.2
20118.916.0
20128.812.5
20138.713.0

The graph below has been plotted with the results from England and Scotland, so that the data can be compared.

A graph showing the number of alcohol related deaths per 1000,000 of the population.

With line graphs, you have a choice between connecting the plots point-to-point, or using a line of best fit.

Joining dots point to point is essential to show fluctuations in data – for instance, with the death rates above, or a person's body temperature. We can also connect data point to point when we are confident of their quality.

A line of best fit looks at how variables behave in relation to each other – it is used to establish or display a correlation or a trend. Relationships may not be obvious from a table of data or could be obscured by experimental errors. Lines of best fit are also used to make predictions.

Question

The population of which country shows most fluctuation in the graph shown above?

Scotland.

Question

If the data for 2014 is:

England: alcohol-related death rate = 9.1 per 100 000 of population

Scotland: alcohol-related death rate = 13.3 per 100 000 of population

What has happened to the number of deaths for each country between 2013 and 2014?

Both have risen.

Instead of simply describing what has happened qualitatively, you could make your analysis quantitative, to give more information.

In England, deaths rose by 40 000.

That's 9.1 - 8.7 × 100 000 = 0.4 × 100 000 = 40 000.

In Scotland, deaths rose by 30 000.

That's 13.3 - 13.0 × 100 000 = 0.3 × 100 000 = 30 000

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