The simplest OLSMultipleLinearRegression example ever

Assuming that the reader knows what Multivariate Linear Regression by Ordinary Least Squares is, and that he would like to use it in Java by means of OLSMultipleLinearRegression, class that is part of the Apache Commons Math library, let's see the simplest example of its usage I could think of.

I have two observations, one variable, and I want to get the description of an interpolating curve:

OLSMultipleLinearRegression ols = new OLSMultipleLinearRegression();

double[] data = { 2, 1, 4, 2 }; // 1
int obs = 2;
int vars = 1; // 2
try {
    ols.newSampleData(data, obs, vars); // 3
}
catch(IllegalArgumentException e) {
    System.out.print("Can't sample data: ");
    e.printStackTrace();
    return;
}

double[] coe = null;
try {
    coe = ols.estimateRegressionParameters(); // 4
}
catch(IllegalArgumentException | InvalidMatrixException e) { // 5
    System.out.print("Can't estimate parameters: ");
    e.printStackTrace();
    return;
}

dumpEstimation(coe);

1. The input data is flattened in an array, where all the observations are stored one after the other, respecting the convention of having first the y and then the x component.
2. We should have more observations than variables, so this is the minimal case. Having just one variable, we'll get a straight line as a result.
3. The functions performs a few check on the passed data before working with them, if the number of variables is not bigger than the number of observations, or if there are less values than expected in the array, an IllegalArgumentException is thrown.
4. The resulting curve parameters are returned in a double array. In case of failure an exception is thrown.
5. Cool Java 7 feature, we can group all the exceptions that requires the same management in a single block. In previous Java version, a specific catch is required for each exception type.

The last line is a call to a testing function that gives some feedback to the user. It calls another short function that actually calculates the estimated y value given a specific x:

private void dumpEstimation(double[] coe) {
    if(coe == null)
        return;

    for(double d : coe)
        System.out.print(d + " ");
    System.out.println();

    System.out.println("Estimations:");
    System.out.println("x = 1, y = " + calculateEstimation(1, coe));
    System.out.println("x = 2, y = " + calculateEstimation(2, coe));
    System.out.println("x = 3, y = " + calculateEstimation(3, coe));
    System.out.println("x = 4, y = " + calculateEstimation(4, coe));
}

private double calculateEstimation(double x, double[] coe) {
    double result = 0;
    for(int i = 0; i < coe.length; ++i)
        result += coe[i] * Math.pow(x, i); // 1
    return result;
}

1. The most interesting line in this testing code. It shows how the coefficients are stored in the array returned from OLSMultipleLinearRegression.estimateRegressionParameters(). As we see, the coefficient 'i' is relative to the 'i'-th power of x.

The expected output is:

-0.0 2.0 
Estimations:
x = 1, y = 2.0
x = 2, y = 4.0
x = 3, y = 6.0
x = 4, y = 8.0