Linear regression assumptions violated

[Jelisaveta]

I needed to do linear regression in jamovi for my research, but when I did the assumption checks normality, heteroskedacity and autocorellation were all violated. I tried to find a robust version that would tolerate these violations and ended up using the general linear model. Keep in mind, one of my variables didn't have a normal distribution from the very start but my professor said we can proceed because visual inspection showed that the assimetry is very miniscule. Still, I guess that variable might be causing all these assumption violations.
**I want to ask if I did the right thing with using the general linear model with robust (HC3) SE, since I am still learning about statistics and jamovi.**

[mcfanda@gmail.com]

HC3 is appropriated when you have issues with etheroschedasticity. Normality violation is better addressed with bootstrap methods. In general, what to do with assumptions violation really depends on which assumption is violated and how much the data depart from the assumption