Question:
how do i draw a scatter graph when i am investigating the correlation between two grouped variables?
anonymous
2008-04-14 03:40:27 UTC
i am trying to find a correlation between IQ and BMI

i have split the data into groups...is this what i should have done? and bmi is continuos while iq is discrete
Three answers:
anonymous
2008-04-14 03:55:36 UTC
You'd be better off drawing the graph with your raw (ungrouped) data. Grouping reduces detail, and therefore makes any correlation you find less reliable. If you've got lots of data pairs, you'll find software that does it for you easily enough. Chances are you've summarised your data into a small number of groups (<10?) in which case a scatter graph is of no value.



Correlation between discreet and continuous data isn't a problem, so long as you mention that your codomain for your regression formula has to be the integer set.



IQ & BMI eh?? So your hypothesis is that fat people are thick?? Here's hoping you find a low correlation coefficient, speaking personally!



Re 100 pairs in your data. The whole point of a scatter graph is to look for a trend. The more points you use, the more valid your observation (your regression line) will be. 100 points is ideal - enough to see a trend, not too many to be unmanageable. It takes time, but lengthy repetitive operations and calculations are an occupational hazard in statistics, which is why computers are used so widely.



PS Don't be scared to report the observation "no correlation". You may be looking for correlation, but if it isn't there, then reporting its absence is a result itself and a starting point for discussion.
D B
2008-04-14 03:47:54 UTC
your x axis could be labelled with the IQ data and the y axis with the BMI data



for your first participant you find the point on the graph where the IQ meets the BMI and put a cross

then do the same with the second person's data and so on

remeber that one cross indicates both pieces of data and you do not join the crosses up - though you can calculate and draw in a line of best fit.
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2016-11-07 12:36:36 UTC
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