nag_corr_cov (g02bxc) Example Program Results Case 1 --- Using weights Input data 0.9 0.1 0.9 9.1 0.0 0.0 0.1 3.7 0.1 1.3 0.4 4.5 Sample means. 0.5 0.4 0.6 Standard deviation. 0.4 0.6 0.3 Correlation matrix. 1.0000 -0.4932 0.9839 -0.4932 1.0000 -0.3298 0.9839 -0.3298 1.0000 Variance matrix. 0.197 -0.123 0.149 -0.123 0.316 -0.063 0.149 -0.063 0.117 Sum of weights 17.3 Case 2 --- Using weights Input data 9.1 3.7 4.5 0.1 0.9 0.1 0.9 1.3 0.0 0.0 0.1 0.4 Sample means. 1.3 0.3 1.0 Standard deviation. 3.3 1.4 1.5 Correlation matrix. 1.0000 0.9908 0.9903 0.9908 1.0000 0.9624 0.9903 0.9624 1.0000 Variance matrix. 10.851 4.582 5.044 4.582 1.971 2.089 5.044 2.089 2.391 Sum of weights 1.8 Case 3 --- Not using weights Input data 1.1 0.1 9.7 0.7 11.1 23.5 11.1 9.4 0.9 9.0 8.7 0.0 Sample means. 4.4 10.9 9.8 Standard deviation. 5.8 11.8 1.2 Correlation matrix. 1.0000 0.9193 0.9200 0.9193 1.0000 0.6915 0.9200 0.6915 1.0000 Variance matrix. 33.951 63.208 6.485 63.208 139.250 9.871 6.485 9.871 1.464 Sum of weights 3.0 Case 4 --- Using weights Input data 1.1 0.1 9.7 0.7 11.1 23.5 11.1 19.4 0.9 9.0 78.7 0.0 Sample means. 10.7 22.7 11.1 Standard deviation. 1.9 4.5 1.8 Correlation matrix. 1.0000 0.9985 0.0173 0.9985 1.0000 0.0716 0.0173 0.0716 1.0000 Variance matrix. 3.672 8.538 0.059 8.538 19.909 0.570 0.059 0.570 3.185 Sum of weights 20.1 Case 5 --- Not using weights Input data 1.1 0.1 9.7 0.7 11.1 3.5 13.1 19.4 0.9 0.0 78.7 0.0 Sample means. 4.4 1.2 33.8 Standard deviation. 5.8 2.0 38.9 Correlation matrix. 1.0000 0.9999 -0.4781 0.9999 1.0000 -0.4881 -0.4781 -0.4881 1.0000 Variance matrix. 33.951 11.567 -108.343 11.567 3.941 -37.687 -108.343 -37.687 1512.750 Sum of weights 3.0 Case 6 --- Using weights Input data 1.1 0.1 9.7 0.7 17.1 93.5 13.1 19.4 30.9 0.0 78.7 0.9 Sample means. 17.2 86.3 15.9 Standard deviation. 4.2 25.6 13.7 Correlation matrix. 1.0000 -0.0461 0.7426 -0.0461 1.0000 -0.7033 0.7426 -0.7033 1.0000 Variance matrix. 17.846 -4.989 43.123 -4.989 656.407 -247.692 43.123 -247.692 188.970 Sum of weights 21.0