I am investigating the effects of weather on restaurant demand. Currently, I am testing the model assumptions for my multiple linear regression model.
My model specification (simplified) is as follows:
lm(Visitor ~ Temperature + Temperature_Squared + Pressure + Clouds + Sun + Rain + Day_Fri + Day_Sat + Day_Sun + Day_Mon + Day_Tue + Day_Wed + Hour_00 + Hour_01 + Hour_02 + Hour_13 + Hour_14 + Hour_15 + Hour_16 + Hour_17 + Hour_18 + Hour_19 + Hour_20 + Hour_21 + Hour_22 + Hour_23 + Holiday, data=dat)
After running the model, I obtained the following two graphs:
The residuals vs. fitted plot appears to be relatively flat and homoskedastic. However, it has this odd cutoff in the bottom left, that makes me question the homoskedasticity. What does this plot signal and, more importantly, what does it mean for my interpretation? Is multiple linear regression the correct model?
How do I interpret the "bump" in the top-right part of the QQ plot?
NB: The data is complete and does not have unreasonable outliers. Initial results indicate only 1 (out of 6) IVs to be significant, while all control variables are significant. Also, no issues with multicollinearity were detected.