Percentage of contribution of multiple factors to a single dependent variable I have a set of data (underway observation along cruise track) which includes one dependent variable A and three independent variables B, C, and D. It's known that A is related with each of the three factors, so I want to find a numerical relationship that allows me to express A by means of B, C, and D, and would like to be able to evaluate the relative contribution of each independent variable to A. 
I'm aware PCA is the tool to use in this situation, and I've tried it in Excel, only to get a biplot where all four variables are treated equally as the same type of dependent variable, and variable A is used to explain those principal components. What I really want is to see how A behaves under the joint influence of B, C, and D, and I don't care how B, C, and D themselves relate to each other. 
(I'm working in MATLAB.) 
 A: As amoeba wrote in the comments, this is a multiple regression problem and has nothing to do with PCA (see Linear regression on Wikipedia). I here use the standard assumptions of a linear model with additive noise:
$$
A = b ~ B + c ~ C + d ~ D + k + E.
$$
Here $b$, $c$, and $d$ are the scalar regression coefficients for your three explanatory variables, $k$ is a constant offset, and $E$ captures everything that can't be fit into the other terms (the error).
In Matlab, to compute $A$ if given all the values on the left-hand side, one would write in matrix notation:
A = [B C D ones(n, 1)] * [b c d k]' + E

assuming that all variables are represented as column vectors, and that $n$ is the number of data points.
To solve this equation for the coefficients $b$, $c$, $d$, and $k$ for given $A$, you can use the backslash operator:
coeff = [B C D ones(n, 1)] \ A

This operator gives a least-squares solution, meaning $A$ is determined such that the sum of squared errors ($\sum_{i = 1}^n E_i^2$ or sum(E .^ 2) in Matlab) is as small as possible. You can then extract the single coefficients:
b = coeff(1)
c = coeff(2)
d = coeff(3)

These coefficients are estimates of how strongly the variables $B$, $C$, and $D$ contribute to $A$.

Your title says that you are interested in a "percentage of contribution" of the independent variables, and I assume that you mean percentages of explained variance. The problem is that if the variables $B$, $C$, and $D$ are correlated (share variance among themselves), there is no simple answer to that question, because the variance of their contributions to $A$, which are $b ~ B$, $c ~ C$, and $d ~ D$, do not combine additively. Assuming that there are no correlations, you can compute those contributions like this:
var([B C D] * diag([b c d])) / var(A) * 100

which gives you a three-element vector with estimates of the relative contribution in percent of the three independent variables.
