system
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human
Intuitive explaination on why colinearity blows up the standard errors of the regression coefficients

In Linear Regression, why does colinearity (or multicolinearity) increase the standard error of the coefficients' estimators ? Mathematically it is because of the Variance Inflation Factor, I know this, but intuitively, without the maths, why ?
gpt
The job of regression is to identify the independent contributions of each predictor on the outcome variable. Correlation among predictors makes it hard to distinguish each contribution. That difficulty leads to the inflated variance of the coefficient estimates.