A linear pattern occurs on my residual plot: what can I do? I'm a bit stuck with a problem here and any kind of help would help a lot :)
Just to give a clue about my data. I have 6 independant variables (IV) which are:

*

*$X_1$ = Population -within a block-

*$X_2$ = Households -within a block-

*$X_3$ = Total Rooms -aggregated-

*$X_4$ = Total Bedrood -aggregated-

*$X_5$ = Median Income

*$X_6$ = Ocean Proximity [Categorical]

and my dependant variable (DV) is $Y$ = Median House Price.
I ran a regression including all IVs but there is a violation for almost all OLS assumptions in addition to huge multicollinearity. Here are the residual plot and normality plot before any adjustments.

What I did then is transform all my IVs and my DV using the Box-Tidwell method which I think it is not the correct way of solving the issue so this is my first question what can I do to solve the normality issue?
The other problem is that even after transforming all variables I still have an issue in my residual plot which is the main problem here. I have a linear pattern on the graph that I don't know how to solve. I run my DV against each IV separately and still have the same issue. Here is the graph for the transformed model.


Residuals following a linear boundary are typically the result of a ceiling effect or a floor effect. If your sample is large enough, the results are biased only slightly (note that linear models are quite robust to normality violations if you are working with a large sample, unlike, for example, homoskedasticity violations ).
Basically, you have two options. You can use a different model (e.g. lognormal regression, Poisson regression, depending on the specific data), or you can ignore the problem and rely on the fact that the results are only slightly biased since you have a large data set.
