# 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:

• Sorry for a noob question :) What do you mean by Y dataset is more influenced by the X dataset?
– Dawny33
Dec 2 '15 at 8:55
• 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? Dec 2 '15 at 8:56
• Thanks for explaining. I think ANCOVA is the technique you're looking for. Pl have a look at the answer and let me know if it answered your problem :)
– Dawny33
Dec 2 '15 at 9:02

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.