Regression: how to interpret different linear relations? I have three datasets, let's call them X and Y1 and Y2. A scatterplot is produced out of them, with Y1 and Y2 sharing them same X dataset (or support).

My question: if the two regression lines are different in both slope and intercept, is there a way to evaluate if the X dataset has more influence on Y1 or Y2? 

Based on the image below, this is to say - which Y dataset is more influenced by the X dataset?


Blue slope (Y1): -112
Red slope (Y2): -90


EDIT

It is visible that an increase in X produces a decrease in both Y1 and Y2. My question could be interpreted as follows: which Y dataset decreases the most, given the increase in X? Is the slope everything I need?

Image:
What you are looking for is the Analysis of Covariance (ANCOVA) analysis, which is used to compare two or more regression lines by testing the effect of a categorical factor on a dependent variable (y-var) while controlling for the effect of a continuous co-variable (x-var).

Here is an example for carrying out the ANCOVA analysis using R.