[R] about "normalized y" via covariance analysis

[XinMeng]

Hello sir: 
Here's a question on covariance analysis which needs your help. 
There're 3 experiments,and x refers to control while y refers to experimental result. 
The purpose is to compare the "y" values across the 3 experiments. 
 
 experiment_1: 
 x:0.1 0.2 0.3 0.4 0.5 
 y:0.5 0.6 0.6 0.7 0.9 
 
 experiment_2: 
 x:1 2 3 4 5 
 y:3 4 6.5 7.5 11 
 
 experiment_3: 
 x:10 20 30 40 50 
 y:18 35 75 90 98 
 
Apparently,the control("x") isn't at the similar level so that we can't compare the "y" directly through ANOVA. 
We must normalize "y" via "x" in order to eliminate the influence of different level of "x". 
The method of normalize I can get is "covariance analysis",since "x" is the covariant of y. 


After this normalization,we can get the according "normalized y" of every "original y". 

All in all,the "normalized y" of every "original y" is what I want indeed. 



My question is: 
How to perform "covariance analysis" by using R in order to get "normalized y"? 

Some suggestion is:lm()
I wanna know wheter my performance is right:

fitted.values(lm(y~factor(experiment)+ x))




Thanks a lot!  
My best regards!