What do the residual and Q-Q plots show?

Question

I'm running a regression in Stats iQ and I don't understand my diagnostic plots.  What do the residual and Q-Q plots show?

john_shearer · Accepted Answer

Hi Goldie.  I don't have a lot of experience yet in StatsiQ, but do have some understanding of regression. Looking at the outputs it looks like you get:Predicted v Actual - this just shows how well the regression equation actually predicts the values you have in your data.  here you're ideally looking for your data points to be closely clustered around a straight line (sloping up or down depending on the relationship). Residuals - this shows the predicted value (based on the regression equation) on the X axis, and the residuals on the Y axis.  The residuals are essentially the difference between the predicted value and the actual value (i.e. the 'error' in your predicted value) .  Essentially when you look at this plot, you want the points to be in a pretty 'random' cloud pattern - ideally clustered close to the zero line (i.e. smaller residuals).  What you don't want is any clear pattern where the residuals either increase/decrease in line with your predicted value.  Or any pattern where the residuals appear non-linear (a U or upside down U shape).Also watch for outliers - points that are far from the general pattern of data points - as these can be influential in impacting the regression equation.Normal Q-Q Plot: This is used to assess if your residuals are normally distributed.  basically what you are looking for here is the data points closely following the straight line at a 45% angle upwards (left to right).  Again what to watch here is any patterns that deviate from this - particularly anything that looks curvilinear (bending at either end) or s shaped.This page on the Qualtrics support is actually pretty good:https://www.qualtrics.com/support/stats-iq/analyses/regression-guides/interpreting-residual-plots-improve-regression/Hope this helps!

Goldie · Answer

@john_shearer That's super helpful. Thank you!

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