Creating new variables
When given a data set to analyse, you should always ask yourself whether the variables are the most useful ones to analyse. Sometimes a simple transformation provides a variable whose values are more meaningful or highlight a different aspect of the data. This is most easily illustrated with some examples.
GDP in Europe
Our first example shows the Gross Domestic Product (GDP in US$billion ) of all European countries in 2012 and their populations.
Select GDP and Population from the pop-up menu to display these variables on the European map. Although the map of GDP does show the relative importance of countries, the maps of the two variables mainly highlight the large and small countries. It is more useful to display the GDP per capita, defined by:
GDP per capita = | Country's total GDP |
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Population |
Select GDP per capita from the pop-up menu to see which parts of Europe have the greatest GDP per person.
European population
Another example shows the populations (millions) of all European countries in 2012 and their land areas (thousand km2).
Select Population and Area from the pop-up menu to display these variables on the European map with different colours. These colours represent different aspects of the 'sizes' of the countries and add little to the map. It is more interesting to examine population density, defined by:
Density = | Population | × 1000 |
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Area |
Select Density from the pop-up menu to display its distribution.
Other examples
When data are available in different years, it is often useful to examine which individuals have increased or decreased over the period. For example, if calorie intake is recorded in developing countries in 1993 and 1998, it is helpful to analyse the percentage increase in calories over this period,
%Increase = 100 × | 1998 calories − 1993 calories |
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1993 calories |
When the values of imports or exports are reported over a series of years, it is usual to take account of inflation by dividing by a price index (e.g. the consumer price index).
Always think carefully about the variables you have been given. Are they accurate? Are there other variables that would be more informative?