Meaningful 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.
African population
Our first example shows the populations (millions) of all African countries in 2005 and their land areas (thousand km2).
Select Population and Area from the pop-up menu to display these variables on the African 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 |
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Area |
Select Density from the pop-up menu to display its distribution.
African calorie intake
A second African example shows the calorie intake (calories per capita per day) of African countries in 1993 and 1998.
Select 1993 cals and 1998 cals from the pop-up menu. The maps highlight are effective at highlighting the parts of Africa with worst nutrition. However these two maps do not show well the changes in nutrition between 1993 and 1998. The percentage change in calorie intake can be defined by:
%Increase = 100 × | 1998 calories − 1993 calories |
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1993 calories |
Select %Increase from the pop-up menu to see which regions of Africa had increases or decreases in their calorie intake.
Other examples
The Gross Domestic Product (GDP) of countries is often reported. It is usually more meaningful to divide by the population size to obtain the GDP per capita.
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?