MATLAB vs. Excel: Matlab seems to give better answer to exponential decay, anyone know why?
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A co-worker of mine plotted the data (pHi1,alf) below in Excel and used the "add trendline" to fit the data. Excel returns "y = 2814.2e-3.5613x" and the fit is not very good.
I used the following Matlab code to fit the data and of course, the fit is much better.
As I am trying to explain to my co-workers the advantages of Matlab, does anyone know why Excel does such a poor job with this?
Although better can be subjective, is there something obvious that I am doing "wrong" in excel or is the answer simply "You get what you pay for"?
pHi1 = [0.063 0.113 0.22 0.286 0.373 0.437 0.53 0.547 0.709 0.938 1.72];
alf = [4620 3390 2440 1460 840 410 220 190 90 40 20];
%set up dissolution model
aldis = @(dis,xx)(dis(1)*exp(-dis(2)*xx));
dis = [alf(1) 2];
%use nlinfit for least squares to determine dis(1) and dis(2) in disfit
disfit = nlinfit(pHi1,alf,aldis,dis)
pHfit = min(pHi1):0.1:max(pHi1);
alfit = aldis(disfit,pHfit);
ylabel('Dissolved Alumina by ICP (ppm)')
Matt Tearle on 16 Mar 2011
Excel is apparently doing a log of the y data then fitting a line. This approach is very sensitive to outliers. Change your plot commands to semilogy and you'll see why with your data. That last point is an outlier in semilog space, even though it isn't so much in the original variables.
To see what Excel did (in MATLAB, if that makes sense):
Look familiar? :)
More Answers (2)
the cyclist on 16 Mar 2011
The trend command in Excel just does a linear fit, right? Did you try to fit a linear function to exponential data? (If so, that's why the Excel fit was poor.) Or did you take the log of the exponential data before fitting in Excel?
Richard Crozier on 17 Mar 2011
You should never use Excel for anything more complicated than accounting, as this only requires a precision of 2 decimal places, and can be checked on a calculator. Anything else, and you basically can't trust the result.
Anyone attempting to present statistics done in Excel should be particularly embarrassed.