Questions in implementing Considering Canonical Correlation Analysis in R

I have a dataset of temperature readings of different burner parts which determine the final temperature of the burner and time to set off burner

So my dataset looks like this:

Knob_reading_of_Coil_temperature(P1) Knob_reading_of_barrel_temperature(P2) Knob_reading_of_collar_temperature(P3) Knob_reading_of_air_holes_temperature(P4) Knob_reading_of_jet_temperature(P5) Knob_reading_of_base_temperature(P6) Final_temperature_of_output_burner(I1) Time_to_set_off_the_burner(I2)
11.23 89.12 65.32 97.12 96.4 56.7 67.7 10.2
41.12 86.7 76.76 78.65 91.2 78.24 55.42 21.67
75.65 83.79 82.65 82.43 90.6 87.45 81.13 5.89
78.13 82.57 59.34 92.56 89.8 67.72 75.08 6.65

I used CCA( Canonical Correlation Analysis ) to find which of the parameters are highly correlated.

But will CCA help me to find which of these parameters similarly affect the Final temperature of output burner or Time to set off the burner

Let me explain this better: What I want to find out is whether either of parameters (P1,P2,P3) affect the Final temperature of output burner similary. If yes, I could conclude that P1 P2 P3 affect the final temperature so instead of using all three parameters,i could use only one of them

Similarly , if P4 P5 P6 affect the time to setoff of burner similarly i could conclude that instead of using all three i could use either one as they affect the time to set of burner similarly.

Will CCA help me to find such an correlation ?

Or are there any algorithms that help me to find out how the parameters (P1-P5) affect the other set (I1-I2) ? ie to find if there a similarity in way (P1-P5) affect/ correlate the other set (I1-I2)?