Making my linear regression model meet assumptions causes a large increase in mean squared error I was creating a linear regression model on a particle collisions dataset. I observed that my model breaks several assumptions in linear regression, and when I tried to fix them, it increased the error. Why might this be? I made use of a train-test split during my experiments and here is my methodology using multiple linear regression with no regularisation.
I altered my experiment in 3 stages and each stage increased the mean squared error in my predictions. I plotted the residuals, the standard errors in the coefficients, and the residuals on a histogram. I recorded the mean squared error in predictions on my test set.
These are the result of my original model, with a mean squared error of 388.573.

I observed that the residuals are really heavily skewed positively, so I decided to remove outliers in my data that are more than 2 standard deviations from the mean in any predictor column. The aim here was to make my residuals more normally distributed. Here is the results.

This increased the mean squared error of my predictions to 426.216 - a huge jump.
Lastly, since some of my coefficients had huge standard errors, I made attempts to remove and eliminate multicollinearity in my model by removing correlated features. This gave the following results.

This increased my mean squared error to 426.384.
This is really not what I expected.
I would have expected the predictions of my model to improve as I made alterations to meet the Gauss-Markov assumptions in linear regression.
What could explain these outcomes? What might have caused my error to increase?

I decided to remove outliers in my data that are more than 2 standard deviations from the mean in any predictor column.
I think this alone could very well cause the reduction in performance you see. Outliers should not be removed unless they are clear data errors. Otherwise, you are simply biasing the dataset - and since you are doing a train-test split, it is not surprising that the performance on the test data goes down.
I would add that even if it is not the main cause of this particular problem for you, removing outliers is a bad idea. It's especially poor practice when you are removing what seems to be a large proportion of the data and not just one or two extreme values.
Finally, 'making your linear model meet assumptions'  should mean changing your model to address the complexity of the data, NOT changing your data to meet the simplicity of your model.
