No Collinearity: Multiple regression assumes that one predictor shouldn't be highly correlated with the other predictor. In other words, it can explain that each predictor should provide unique information. In the output, we looked at the table of Collinearity Diagnostics and checked the Condition Index and Variance Proportions. There could be issues of collinearity in the smallest eigenvalues or high condition indices if the variance proportions of any predictor are high (near 1) across them