= Statistics =

''Part of'' TutorialIntermediate

== Description ==

Demonstrates calculating linear regression statistics.

== Example ==

{{{
#!d
import std.stdio;

double average(double[] r)
{
    double t = 0;

    for(int i=0; i < r.length; i++)
     t += r[i];

    return (t / (cast(double) r.length));
}


double beta1(double[] x, double[] y, double x_bar, double y_bar)
{
    int l;

    if (x.length != y.length) 
    {
        writefln("x and y really should be the same length!");

        // Set length as the shorter series and move on...

        if(x.length < y.length) l = x.length;
        else l = y.length;
    }
    else l = x.length;


    double top_sum, btm_sum;

    top_sum = 0;

    for(int i = 0; i < l; i++)
     top_sum += (x[i] - x_bar) * (y[i] - y_bar);

    btm_sum = 0;

    for(int i = 0; i < l; i++)
     btm_sum += ((x[i] - x_bar) * (x[i] - x_bar));

    return top_sum / btm_sum;
}


double beta0(double x_bar, double y_bar, double b1)
{
    return y_bar - (b1 * x_bar);
}



int main()
{
    double[] x;

    x.length = 10;

    x[0] = 1;
    x[1] = 2;
    x[2] = 3;
    x[3] = 4;
    x[4] = 5;
    x[5] = 6.5;
    x[6] = 7;
    x[7] = 7.5;
    x[8] = 8;
    x[9] = 8.5 ;


    double[] y;

    y.length = 10;

    y[0] = 1;
    y[1] = 2;
    y[2] = 3;
    y[3] = 3.2;
    y[4] = 3.4;
    y[5] = 4;
    y[6] = 4.2;
    y[7] = 4.3;
    y[8] = 4.35;
    y[9] = 4.37;


    double x_bar, y_bar;

    x_bar = average(x);
    y_bar = average(y);


    double b1, b0;
    b1 = beta1(x, y, x_bar, y_bar);    
    b0 = beta0(x_bar, y_bar, b1);    

    writefln("b1: %f", b1);
    writefln("b0: %f", b0);


    return 0;
}
}}}

== Source ==

Based on [http://jcc_7.tripod.com/d/tutor/stat.html stat.html] by jcc7.