But in this case it also signals model misspecification. Interpretation Use the residuals versus fits plot to verify the assumption that the residuals are randomly distributed and have constant variance. I think is making a good point that a “wobble” is natural for any estimator, and the question is whether the wobble is outside of a valid confidence bound. The residuals versus fits graph plots the residuals on the y-axis and the fitted values on the x-axis. In turn, the right tail of the residuals in the misspecified model is fatter than that of the normal distribution. Poisson would assume it is multiplicative. You are modeling the conditional mean of the visitor count let’s call it $Y_$ and the outcome is linear. Also, no issues with multicollinearity were detected.īoth the cutoff in the residual plot and the bump in the QQ plot are consequences of model misspecification. Initial results indicate only 1 (out of 6) IVs to be significant, while all control variables are significant. NB: The data is complete and does not have unreasonable outliers. How do I interpret the "bump" in the top-right part of the QQ plot? What does this plot signal and, more importantly, what does it mean for my interpretation? Is multiple linear regression the correct model? However, it has this odd cutoff in the bottom left, that makes me question the homoskedasticity. fitted plot appears to be relatively flat and homoskedastic. 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)Īfter running the model, I obtained the following two graphs: Currently, I am testing the model assumptions for my multiple linear regression model. I am investigating the effects of weather on restaurant demand.
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