Lineaarinen regressio

double Statistic::LineFit(const double *const x, const double *const y, const size_t n, double *m, double *b)
{
    int error = 0;
    double sumx = 0, sumy = 0, sumx2 = 0, sumxy = 0;
    double dn = (double) n;
    size_t i;
    if (n <= 1)
    {
        *m = 0;
        *b = 0;
        error = 1;
    }
    else
    {
        double divisor;
        error = 0;
        for (i = 0; i < n; i++)
        {
            sumx += x[i];
            sumy += y[i];
            sumx2 += (x[i] * x[i]);
            sumxy += (x[i] * y[i]);
        }
        divisor = (sumx2 - ((sumx * sumx) / dn));
        if (divisor != 0)
        {
            *m = (sumxy - ((sumx * sumy) / dn)) / divisor;
            *b = (sumy - ((*m) * sumx)) / dn;
        }
        else
        {
            *m = 0;
            *b = 0;
            error = 2;
        }
    }
    return *m;
};

 int main()
{
// exact line:
double          x0[] =
{-1, 0, 1, 2, 3};
double          y0[] =
{-1, 0, 1, 2, 3};

// noisy line:
double          x1[] =
{-1.1, 0.01, .9999, 1.99998, 3.01};
double          y1[] =
{-1.02, -.00001, 1.002, 1.99872, 2.999973};
    size_t          length = sizeof x0 / sizeof x0[0];
    double          m;
    double          b;
    linfit(x0, y0, length, &m, &b);
    printf("Slope is %f, intercept is %f\n", m, b);
    linfit(x1, y1, length, &m, &b);
    printf("Slope is %f, intercept is %f\n", m, b);
    cin.get();
    return 0;
}