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Data:
786.90 914.57 819.57 756.01 667.94 787.91 1.007.33 804.47 960.63 807.76 926.00 880.33 902.20 812.88 910.00 784.18 824.96 884.73 938.10 867.28 832.90 951.26 696.79 731.28 616.76 725.26 797.72 795.04 801.83 759.59 749.72 804.32 858.94 773.12 711.41 762.32 691.17 651.17 835.31 799.50 766.51 682.74 776.26 805.48 828.47 864.31 675.87 818.82 713.02 734.69 751.66 688.63 695.60 732.01 828.23 689.06 732.55 748.19 924.55 737.59 715.66 716.56 814.82 739.14 773.87 732.18 747.89 758.58 622.32 829.14 761.02 796.69 732.62 717.43 724.51 748.71 702.53 662.32 756.92 651.77 742.12 748.97 822.37 727.38 695.45 701.11 810.41 786.78 782.34 777.36 745.96 697.50 707.62 598.64 687.61 703.41 761.60 687.67 811.35 725.59 666.35 703.68 858.09 732.66 768.65 613.64 723.12 1.029.42 740.67 915.22 711.20 667.64 714.44 722.64 785.94 774.57 842.11 752.71 770.54 799.48 758.58 813.83 749.63 766.55 618.35 775.90 693.86 743.53 683.55 791.12 745.37 871.55 833.94 784.71 725.73 816.31 1.022.45 723.69 818.50 688.79 898.51 713.85 897.91 733.94 766.35 668.08 815.81 769.24 769.99 714.87 748.93 701.93 719.99 740.30 752.71 749.40 722.53 843.35 738.30 1.041.66 700.41 719.68 803.62 712.08 857.47 893.42 851.35 820.47 748.08 735.41 748.41 835.09 804.42 845.07 815.42 675.39 781.17 743.58 725.01 750.55 1.016.51 753.13 696.12 816.40 806.51 766.81 745.43 679.16 737.80 680.71 879.71 882.93 772.23 881.16 873.95 895.79 739.32 871.77 710.31 737.72 773.26 713.63 810.82 780.10 733.67 783.35 779.45 747.47 716.46 731.57 905.35 753.40 702.01 598.66 775.78 699.41 768.76 695.89 830.36 701.57 724.16 713.46 839.61 744.31 733.43 722.06 765.60 702.27 840.86 713.85 656.38 688.59 747.89 777.20 768.23 801.64 747.47 702.93 728.49 737.43 732.18 764.14 831.18 663.51 801.18 782.42 741.01 646.29 793.63 762.34 732.21 703.95 775.03 841.25 727.25 757.39 731.26 756.00 747.21 653.99 754.50 1.061.74 770.32 715.75 858.00 764.13 704.48 587.27 721.94 700.43 534.86 690.52 769.09 751.19 713.09 685.87 757.04 846.45 812.17 749.05 739.05 751.89 733.00 758.77 684.60 754.54 754.16 715.64 707.42 839.61 818.86 807.69 785.05 836.24 797.37 606.24 731.34 743.73 737.08 728.89 804.44 750.42 820.34 603.41 752.78 726.10 711.24 687.63 738.57 851.89 750.27 784.28 945.89 718.92 769.39 731.12 774.03 797.74 754.63 769.57 634.28 691.29 655.76 803.15 709.77 717.00 742.70 726.08 993.29 769.07 745.02 756.55 875.17 643.12 1.016.18 584.51 727.84 693.67 759.79 743.13 1.052.46 841.76 781.12 775.04 786.52 773.00 732.45 743.91 857.96 739.01 771.95 660.38 883.63 774.18 693.19 757.73 696.66 747.38 726.25 667.81 835.49 838.48 797.36 721.51 754.82 708.74 687.49 796.64 900.36 748.53 872.64 803.02 840.59 910.05 930.61 1.037.80 986.71 826.60 692.00 767.01 980.42 880.98 793.29 727.92 762.28 819.60 775.10 777.52 776.06 714.19 909.32 676.65 732.13 803.76 774.11 663.31 765.14 823.95 799.36 756.56 988.67 756.93 1.008.94 949.58 808.64 754.66 796.01 670.84 931.84 768.37 796.89 694.74 812.52 923.79 933.64 790.98 870.49 656.45 782.35 671.99 959.98 974.72 802.27 777.60 805.69 857.82 736.62 803.86 765.32 924.36 708.80 834.14 958.22 719.70 722.17 1.061.76 691.61 902.78 824.25 856.35 787.23 751.16 736.32 812.28 699.73 989.74 677.59 697.18 742.72 745.89 857.38 783.29 777.87 760.61 700.22 955.41 888.86 739.54 763.13 749.04 729.08 714.57 966.98 783.72 715.71 789.22 737.26 663.22 675.57 677.84 687.33 858.67 815.28 616.40 677.87 719.09 700.98 642.11 693.37 734.97 711.35 611.04 728.11 774.35 751.75 747.92 715.11 928.06 915.37 729.08 725.06 717.38 664.00 708.88 714.23 1.028.35 894.23 741.20 877.88 903.10 753.13 926.94 765.22 927.50 952.19 835.07 751.44 723.02 786.45 795.07 695.58 675.78 740.30 1.045.56 859.22 800.05 864.67 757.55 817.52 770.72 769.48 401.01 758.88 848.41 738.57 895.11 776.61 728.31 964.13 910.22 793.71 928.17 696.16 807.23 816.89 824.40 758.38 888.64 850.68 799.68 854.80 869.47 918.94 810.25 829.50 756.80 800.86 904.84 723.95 673.51 849.11 775.86 755.49 607.16 744.71 696.48 765.83 678.24 810.56 836.93 756.65 733.67 827.77 812.56 750.40 812.27 781.85 806.38 701.41 731.95 636.54 746.93 729.32 693.94 734.99 802.74 781.99 645.62 785.30 846.54 737.91 804.19 812.75 795.57 749.20 739.40 716.49 738.72 731.07 730.75 658.57 718.77 849.55 649.65 690.56 678.98 968.69 703.28 736.34 717.80 842.98 760.11 685.98 709.05 880.08 699.06 842.69 743.81 865.96 724.51 716.35 741.90 666.01 719.75 834.17 756.81 750.25 747.03 741.47 837.25 696.93 874.05 732.25 770.27 727.80 604.75 724.92 706.33 753.35 727.52 692.36 804.08 694.74 855.66 911.69 725.69 681.82 696.46 729.01 661.43 663.75 801.65 633.72 669.07 874.02 655.61 646.00 656.80 809.49 652.54 661.05
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R Code
x <-sort(x[!is.na(x)]) q1 <- function(data,n,p,i,f) { np <- n*p; i <<- floor(np) f <<- np - i qvalue <- (1-f)*data[i] + f*data[i+1] } q2 <- function(data,n,p,i,f) { np <- (n+1)*p i <<- floor(np) f <<- np - i qvalue <- (1-f)*data[i] + f*data[i+1] } q3 <- function(data,n,p,i,f) { np <- n*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i] } else { qvalue <- data[i+1] } } q4 <- function(data,n,p,i,f) { np <- n*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- (data[i]+data[i+1])/2 } else { qvalue <- data[i+1] } } q5 <- function(data,n,p,i,f) { np <- (n-1)*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i+1] } else { qvalue <- data[i+1] + f*(data[i+2]-data[i+1]) } } q6 <- function(data,n,p,i,f) { np <- n*p+0.5 i <<- floor(np) f <<- np - i qvalue <- data[i] } q7 <- function(data,n,p,i,f) { np <- (n+1)*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i] } else { qvalue <- f*data[i] + (1-f)*data[i+1] } } q8 <- function(data,n,p,i,f) { np <- (n+1)*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i] } else { if (f == 0.5) { qvalue <- (data[i]+data[i+1])/2 } else { if (f < 0.5) { qvalue <- data[i] } else { qvalue <- data[i+1] } } } } lx <- length(x) qval <- array(NA,dim=c(99,8)) mystep <- 25 mystart <- 25 if (lx>10){ mystep=10 mystart=10 } if (lx>20){ mystep=5 mystart=5 } if (lx>50){ mystep=2 mystart=2 } if (lx>=100){ mystep=1 mystart=1 } for (perc in seq(mystart,99,mystep)) { qval[perc,1] <- q1(x,lx,perc/100,i,f) qval[perc,2] <- q2(x,lx,perc/100,i,f) qval[perc,3] <- q3(x,lx,perc/100,i,f) qval[perc,4] <- q4(x,lx,perc/100,i,f) qval[perc,5] <- q5(x,lx,perc/100,i,f) qval[perc,6] <- q6(x,lx,perc/100,i,f) qval[perc,7] <- q7(x,lx,perc/100,i,f) qval[perc,8] <- q8(x,lx,perc/100,i,f) } bitmap(file='test1.png') myqqnorm <- qqnorm(x,col=2) qqline(x) grid() dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Percentiles - Ungrouped Data',9,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p',1,TRUE) a<-table.element(a, 'Weighted Average at Xnp',1,TRUE) a<-table.element(a, 'Weighted Average at X(n+1)p',1,TRUE) a<-table.element(a, 'Empirical Distribution Function',1,TRUE) a<-table.element(a, 'Empirical Distribution Function - Averaging',1,TRUE) a<-table.element(a, 'Empirical Distribution Function - Interpolation',1,TRUE) a<-table.element(a, 'Closest Observation',1,TRUE) a<-table.element(a, 'True Basic - Statistics Graphics Toolkit',1,TRUE) a<-table.element(a, 'MS Excel (old versions)',1,TRUE) a<-table.row.end(a) for (perc in seq(mystart,99,mystep)) { a<-table.row.start(a) a<-table.element(a,round(perc/100,2),1,TRUE) for (j in 1:8) { a<-table.element(a,signif(qval[perc,j],6)) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab')
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