Example2: Linear Regression

import com.imsl.stat.*;
import com.imsl.math.*;

public class LinearRegressionEx2 {
    public static void main(String args[]) {
        // 
        LinearRegression r = new LinearRegression(2, true);
        double y[] = {3, 4, 5, 7, 7, 8, 9};
        double x[][] = {{1, 1},{1, 2},{1, 3},{1,4},{1,5},{0,6},{1,7}};
        double [][] results = new double[7][5];
        double [] confint = new double[2];
        r.update(x, y);
        for (int k=0; k<7; k++){
           LinearRegression.CaseStatistics cs = r.getCaseStatistics(x[k],y[k]);
           cs.setEffects(-2);
           results[k][0] = cs.getJackknifeResidual();
           results[k][1] = cs.getCooksDistance();
           results[k][2] = cs.getDFFITS();
           confint = cs.getConfidenceInterval();
           results[k][3] = confint[0];
           results[k][4] = confint[1];
        }
        PrintMatrix p = new PrintMatrix("Selected Case Statistics");
        PrintMatrixFormat mf = new PrintMatrixFormat();
        String labels[] = {"Jackknife Residual.","Cook's D","DFFITS", "[Conf. Interval", "on the Mean]"};
        mf.setColumnLabels(labels);
        p.print(mf, results);
    }
}

Output

                        Selected Case Statistics
   Jackknife Residual.  Cook's D  DFFITS  [Conf. Interval  on the Mean]  
0        -0.343          0.045    -0.324       2.261           3.996     
1        -0.327          0.018    -0.207       3.467           4.818     
2        -0.338          0.011    -0.161       4.613           5.702     
3         ?              0.276     ?           5.648           6.695     
4        -0.418          0.024    -0.237       6.563           7.808     
5         ?              ?         ?           6.736           9.264     
6        -0.742          0.372    -0.996       8.201          10.227