puts the files in the right format x y xerror yerror

GNUPlot.java

import java.util.Scanner;

class GNUPlot {

    private double gradient;
    private double xVariance;
    private double  yVariance;
    private double offset;
    private double xxVariance;
    private double yyVariance;
    private double covariance;
    private double sumx2;
    private int n;
    private final double[] x;
    private double[] residual;
    private double gradientError;
    private final double[] y;
    private double linearCorrelationCoefficient;
    private double rss;

    private final int max = 15;


    public GNUPlot(){

        gradient = 0.0;
        n = 0;
        x= new double[max];
        y= new double[max];
        residual = new double[max];
        offset=0.0;
        gradientError=0.0;
        xxVariance=0.0; yyVariance=0.0;
        covariance =0.0;
        sumx2 = 0.0;
        linearCorrelationCoefficient =0;

    }
    public void printGradient(Scanner xScan, Scanner yScan){

      
        double sumx = 0.0, sumy = 0.0;
           //raead in x file and y file 
        while(n< max){

            x[n] = IOUtil.skipToDouble(xScan);
            y[n] = IOUtil.skipToDouble(yScan);

            sumx  += x[n];
            sumx2 += x[n] * x[n];
            sumy  += y[n];
            n++;
        }

        xVariance = sumx / n;
        yVariance = sumy / n;

        //works out the difference of least squares fit
        for (int i = 0; i < n; i++) {
            xxVariance += (x[i] - xVariance) * (x[i] - xVariance);
            yyVariance += (y[i] - yVariance) * (y[i] - yVariance);
            covariance += (x[i] - xVariance) * (y[i] - yVariance);
        }
        gradient = covariance / xxVariance;//linear correlation coefficient
        offset = yVariance - gradient * xVariance;
    }

    public double[] computeResidual(){

        //degrees of freedom=number of points minus constrains
        int degreesFreedom = n - 2;
        rss = 0.0;  //standard error in mean i.e. residual sum of squares
        double ssr = 0.0;  // regression sum of squares
        for (int i = 0; i < n; i++){
            double fit = gradient*x[i] + offset;
            rss += (fit - y[i]) * (fit - y[i]);
            ssr += (fit - yVariance) * (fit - yVariance);
            residual[i] =y[i]- fit;
        }
        linearCorrelationCoefficient    = ssr / yyVariance;
        double svar  = rss / degreesFreedom;
        double svar1 = svar / xxVariance;
        double svar0 = svar/n + xVariance*xVariance*svar1;
        gradientError=Math.sqrt(svar1);

        System.out.printf(" R absolute = %g", linearCorrelationCoefficient);
        System.out.println();

        System.out.printf(" std fit of the gradient = %g ", svar0);
        System.out.println();

        // print results
        System.out.printf(" The gradient is  %g ",this.gradient);
        System.out.println();

        System.out.printf(" error of the gradient = %g ", gradientError);
        System.out.println();

        System.out.printf(" The the offset is  %g ",offset);
        System.out.printf(" std error of the offset = %g ",Math.sqrt(svar0));
        System.out.println();

        //TODO put into a method
        svar0 = svar * sumx2 / (n * xxVariance);

        System.out.printf(" SSTO = %g ", yyVariance);
        System.out.println();

        System.out.printf(" Sum of squares of Residuals: %g ", rss);     
        System.out.println();
        System.out.printf(" Regression sum of squares is  %g " , ssr);
        System.out.println();

        return residual;
    }
    public double getGradient(){

        return gradient;
    }
    public double getGradientError(){

        return gradientError;
    }
    public double[] getX(){
        return x;
    }
    public double[] getY(){
        return y;
    }

    public double rss(){
        return rss;
    }

}