Nonlinear Fit for a Complicated Curve - Fitting a Function to the Data

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Bev D

Heretical Statistician
Staff member
Super Moderator
#3
Well, there isn't much data so the 'best' approach would be a simple linear fit.

For us to give better advice we'd need to know what the data are (what are the X and Y factors?)

How was the data collected? over what time period? what other factors were allowed or forced to vary?
 

Bev D

Heretical Statistician
Staff member
Super Moderator
#5
But to improve the stats get rid of point (1,14.7705035971223), its an outlier and it may affect the statistics.
I would offer different advice: outlier detection is not intended to be an automatic data censor. it is intended to detect data that may be a result of measurement, preparation or data entry error. If the data point is a result of an error it should be removed from the analysis. If the data point is a legitimate result it should stay in the analysis - it is reality and we must deal with it.

too often I see people edit 'outliers' get a model or make other decision then wonder why the model doesn't predict the actual results.:mg:

I would also add that with such a small sample size the variation represented by the single value you point out may in fact be replicated many times in a larger data set...
 
D

Darius

#6
Agree again with you Bev, but as you said the best model is a lineal ones and altho there is too much noice the point in question have a strong effect on the regresion, if the model must represent the most of the data best my advice could be still to get rid of it and of course the analist must try to understand why such meassure was like that (maybe I didn't said it before but I took it for granted).
 

Statistical Steven

Statistician
Staff member
Super Moderator
#7
I would offer different advice: outlier detection is not intended to be an automatic data censor. it is intended to detect data that may be a result of measurement, preparation or data entry error. If the data point is a result of an error it should be removed from the analysis. If the data point is a legitimate result it should stay in the analysis - it is reality and we must deal with it.

too often I see people edit 'outliers' get a model or make other decision then wonder why the model doesn't predict the actual results.:mg:

I would also add that with such a small sample size the variation represented by the single value you point out may in fact be replicated many times in a larger data set...
The lack of fit test does not give any evidence that the linear model is incorrect. Though there are some influential and potential outliers, without knowing about the data it is hard to recommend removing data from the model fit.
 
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