I guess you want quantify the association between variables $V$ and $P.$
By eight 'reactors', I suppose you mean you have eight values of $V$ and
eight matching values of $P.$
First, I would make a plot of the eight $V_i$ against their corresponding
$P_i.$ If the relationship is 'mainly' linear, then you should use the
coefficient of correlation $r$ to assess the association. (See the link.) By "linear"
I don't mean the points all need to lie exactly on a straight line; I mean
that a straight line should 'fit' the data better than some kind of simple curve.
The coefficient of correlation $r$ measures linear association.
If $r > 0,$ then $V$ increases as $P$ increases. If $r < 0$ then $V,$ decreases as $P$ increases. If $r \approx 0,$ then there is no linear association.
Always, $-1 \le r \le 1,$ where $r = \pm 1$ indicates perfect fit of the points
to a line. There is a statistical test to check whether the data are
consistent with $r = 0.$
If the relationship is steadily increasing or decreasing, but not mainly
linear, then Spearman's rank correlation may be more useful. (See the link.)
Here is a plot to illustrate these ideas. In the nearly linear plot at left
$r = .995.$ In the curved plot at right the coefficient of correlation $r = .964,$ but Spearman's rank coefficient $r_s = 1.$
Regression methods might allow you to find the best equation for expressing
$V$ in terms of $R$ (or $R$ in terms of $V$), or to predict the value of
one variable given the value of the other.
These are only vague statements for orientation. Without seeing your data and knowing what your objective is, it is very difficult to give specific advice.
If you have data, please post them with $V$ on one line, separated by commas,
and corresponding values of $R$ (same order) on another line, separated by
commas. Also say what you mean by a 'significant' finding. Then maybe I or
someone else on this site can give your further guidance.
