Images as Matrices Explained: The Math Behind Computer Vision

If "investment in newspaper ads" doesn’t contribute much to sales, while "radio" has an average contribution and "tv" has a high contribution, you might see the following in terms of multicollinearity:

1. **Low Contribution Variables**: If "newspaper" ads don't affect sales much, and its correlation with "radio" or "tv" is low, it might not significantly impact multicollinearity. However, if it's highly correlated with "radio" or "tv," it could still introduce some multicollinearity.

2. **High Contribution Variables**: For "radio" and "tv," if they have a strong impact on sales and are highly correlated with each other, multicollinearity could be a concern. This means it might be hard to separate their individual effects on sales.

**In Practice**:

- **Check Correlations**: Look at how "newspaper," "radio," and "tv" are correlated with each other. High correlations suggest multicollinearity.

- **Assess Impact**: Even if "newspaper" doesn’t contribute much to sales, if it's highly correlated with the other predictors, it might still affect the model's performance.

- **Consider Adjustments**: If multicollinearity is present, you might need to adjust your model by removing one of the correlated predictors or combining them in some way.

In summary, even if some variables don't seem to contribute much, their relationships with other predictors can still affect the analysis due to multicollinearity.