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What is the best way to represent the following data graphically? Can I use a histogram

Year    Output per Person       Capital Employed
2010    16.3 units/annum        £40,000 p.p
2011    15.1 units/annum        £38,000 p.p
2012    14.4 units /annum       £35,000 p.p
2013    11.7 units per annum    £33,000 p.p
2014    10.8 units per annum    £30,000 p.p
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You have three variables (year, output, capital). Are you interested in the relationship between output and capital, or is it their individual behaviour over time you care about -- or something else? What aspects of the information matter for you? (Is this for some subject or is it a real issue you face?) –  Glen_b Aug 28 at 5:01
    
@Glen_b I'm interested in the relationship between output and the capital? I want to show that when capital employed is decreased, output also decreased –  chamzzey Aug 28 at 5:36

2 Answers 2

up vote 6 down vote accepted

I see two main alternatives, the scatterplot (see Stephan's post, or a slightly different version below which is sometimes worth trying with time series), and superimposed time series (see the second plot below), though there are a number of other possibilities.

However, beware of interpreting the appearance of correlation as meaningful. Specifically, since both your quantities are both "per person, per annum", you have a classical situation where you expect spurious correlation in the original sense of the term.

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Probably best to do a simple scatterplot. Using R:

foo <- data.frame(year=2010:2014,output=c(16.3,15.1,14.4,11.7,10.8),
  capital=c(40,38,35,33,30)*1000)
with(foo,plot(capital,output,xlab="Capital Employed",ylab="Output per Person",pch=19))

scatterplot

Or also plot the years:

with(foo,plot(capital,output,xlab="Capital Employed",ylab="Output per Person",pch=NA))
with(foo,text(capital,output,year))

scatterplot with years

However, note that both capital and output decline over time. So either output is driven by capital... or both are independently driven by some time-dependent trend (or something in between). Be careful when interpreting correlations, especially over time.

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