What does a Q-Q plot of residuals tell us?
Residual plots and Q-Q plots are used to visually check that your data meets the homoscedasticity and normality assumptions of linear regression. Homoscedasticity means that the residuals, the difference between the observed value and the predicted value, are equal across all values of your predictor variable.
What assumption about a linear model does the normal QQ plot evaluate?
The normality assumption is evaluated based on the residuals and can be evaluated using a QQ-plot (plot 2) by comparing the residuals to “ideal” normal observations. Observations lie well along the 45-degree line in the QQ-plot, so we may assume that normality holds here.
What pattern should a normal quantile plot of the residuals follow?
A Quantile-Quantile plot (QQ-plot) shows the “match” of an observed distribution with a theoretical distribution, almost always the normal distribution. If the observed distribution of the residuals matches the shape of the normal distribution, then the plotted points should follow a 1-1 relationship.
Is the density plot and QQ plot normal?
Essentially following another Stack answer, I am looking at the density plot and the QQ plot with: I do not have a strong experience in Statistics, but they look like examples of normal distributions I have seen. If I interpret it correctly, it tells me it is safe to reject the null hypothesis, which is that the distribution is normal.
Where are the negative residuals on the QQ plot?
These are for the negative residuals (left tail) and there are many residuals at around the same value a little smaller than -1. If the distribution had followed the normal here, the points would be on the 1-1 line and would actually be even smaller.
Should I stick with the QQ plot and assume my distribution is normal?
Should I stick with the QQ plot and assume my distribution is normal? You do not have a problem here. Your data my be slightly non-normal, but it is normal enough that it shouldn’t pose any problems. Many researchers do statistical tests assuming normality with far less normal data than those that you have.
Is the tails of a QQ plot normal?
My conclusion still stands, though: Your data are not so non-normal that it should pose problems. Your distribution is not normal. Look at the tails (or lack thereof). Below is what you would expect from a normal QQ plot. Refer to this post on how to interpret various QQ plots.