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Residual plot -quick question

  1. Oct 15, 2015 #1
    hey guys, just wondering if the residual plot can tell one anything about the type of relationship in a model i.e is it linear or not?

    Or does it just tell one if homoscedasticity (i.e equal variances) is observed?

    thanks
     
    Last edited: Oct 15, 2015
  2. jcsd
  3. Oct 15, 2015 #2

    BvU

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    Hi there,

    If I were a computer, I'd print "not enough input" ...

    residuals can help you for what you describe, e.g.: if the residuals for some calculated variable (that was assumed to depend linearly on some independent variable) are plotted against that independent variable, you should get something looking like a parabola if the actual dependence is quadratic. Whether you can recognize it from the plot depends on the amount of scatter (noise)
     
  4. Oct 15, 2015 #3
    what if i just had residuals against fitted values, and the pattern began to fan out towards the right of the residual plot. Does this tell me anything about linearity? i.e can i assume linearity is appropriate for my model? Or should i assume non linearity instead?
     
  5. Oct 15, 2015 #4

    BvU

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    It means the scatter increases for bigger values of the fitted value. If the errors are only due to the noise, the 'trumpet' should appear to be 'horizontal'.

    If I were a computer, I'd now ask to see the plot and a short description of the model .. .:wink:
     
  6. Oct 15, 2015 #5
    the question that I'm asked is based solely on what the residual plot looks like which is in the file attached. It asks if there are any issues with the assumption of linearity and/or homoscedasticity.

    not really sure what to conclude...
     

    Attached Files:

  7. Oct 15, 2015 #6

    BvU

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    Googling heteroscedasticity and homo idem (I have a lot of experience with fitting, never needed the words, though -- so I learn from this too) I'd say you picture resembles the picture in the first link more than the one in the other.
    To me that's logical in a linear dependence relationship (*), but apparently it can be considered separately.

    (*) with a correlation coefficient > 1 Especially for residual versus fitted variable. (In this link they have a plot of residual versus prediciting variable.
     
  8. Oct 15, 2015 #7
    Ah, so i think homoscedasiticty cannot be assumed but the assumption of linearity may still be good however there will be alot of inaccuracy in the model at higher DV values?
     
  9. Oct 15, 2015 #8

    BvU

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    By now you're the expert ! Usually for a linear model you have this kind of behaviour when the slope is > 1 ( y = b x with a given b > 1 means same relative error in y as in x )
     
  10. Oct 15, 2015 #9
    Thanks BvU.
     
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