It would be concluded that multicollinearity was not a problem if:

Question

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Tolerance measures the amount of variance in a predictor variable that is not explained by the other predictor variables in the model. A tolerance of 0.90 or higher indicates that there is not a significant amount of redundancy among the predictor variables and thus multicollinearity is not a problem. A tolerance of 0.001 or lower would indicate that there is perfect multicollinearity, which is a severe problem. R² is not a commonly used metric for detecting multicollinearity, and singularity occurs when a variable is a linear combination of other variables and is not necessarily a direct indication of multicollinearity.

It Would Be Concluded That Multicollinearity Was Not a Problem