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Data X:
197632 238592 716800 348160 471040 307200 302080 295936 195584 155648 141312 266240 323584 276480 194560 179200 168960 134144 121856 286720 276480 174080 163840 143360 136192 122880 120832 306176 289792 219136 204800 185344 168960 161792 143360 121856 110592 81920 72704 92160 61440 46080 40960 33792 27648 30720 16384 54272 36864 12288 9216 7168 4096 2048 972.8 870.4 901.12 829.44 1013.76 1290.24 944.128 905.216 685.056 774.144 702.464 302.08 269.312 265.216 211.968 177.152 185.344 151.552 144.384 156.672 124.928 120.832 120.832 106.496 112.64 106.496 104.448 103.424 99.9424 87.4496 81.92 103.424 101.1712 99.4304 97.5872 95.3344 97.4848 95.8464 88.3712 85.9136 80.384 80.0768 87.7568 87.6544 76.0832 62.5664 79.0528 78.1312 68.096 67.8912 65.3312 77.2096 74.9568 67.6864 64.4096 60.7232 83.5584 70.3488 67.6864 60.3136 61.952 62.464 54.784 56.4224 60.5184 57.0368 54.0672 53.5552 53.5552 55.9104 47.5136 53.1456 51.5072 53.248 48.5376 49.0496 43.6224 44.1344 38.6048 37.376 31.4368 35.4304 34.5088 32.9728 32.8704 32.256 32.1536 30.3104 28.9792 33.0752 28.2624 27.7504 25.088 29.4912 28.0576 26.9312 18.944 21.6064 21.0944 20.3776 22.9376 21.6064 21.0944 21.0944 18.944 22.8352 22.528 21.8112 21.8112 18.8416 17.7152 17.3056 16.896 16.6912 15.36 19.73 16.86 16.83 16.76 16.73 16.45 15.82 15.8 15.44 14.29 11.95 15.65 15.06 14.89 14.72 14.58 14.33 14.29 13.88 13.8 13.63 13.47 12.95 12.74 12.54 12.48 11.81 15.22 12.27 11.5 14.57 12.42 11.24 10.06 10.91 10.82 10.41 9.25 8.02 11.5 10.06 9.58 9.14 8.94 7.45 7.27 7.27 7.14 7.88 7.25 6.9 7.31 7.26 6.84 7.48 6.48 5.72 6.82 6.56 6.49 5.87 6.33 6.33 5.75 4.41 2.99 4.57 4.31 3.71 2.65 2.88 3.74 2.59 2.07 2.59 2.88 2.59 2.68 2.58 1.51 1.93 1.78 1.61 1.51 1.42 1.39 1.94 1.94 1.7 1.57 1.41 1.38 1.24 1.22 1.15 1.24 1.21 1.2 0.671 0.598 0.719 0.719 0.719 0.575 0.598 0.426 0.411 0.377 0.371 0.367 0.35 0.333 0.306 0.302 0.287 0.164 0.134 0.0909 0.0688 0.115 0.113 0.0821
Data Y:
1980 1980 1981 1981 1981 1981 1981 1981 1981 1981 1981 1982 1983 1983 1983 1983 1983 1983 1983 1984 1984 1984 1984 1984 1984 1984 1984 1984 1984 1984 1984 1984 1984 1984 1984 1984 1984 1984 1985 1987 1987 1987 1988 1988 1988 1988 1988 1989 1989 1989 1990 1991 1992 1993 1994 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 1996 1996 1996 1996 1996 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1998 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2003 2003 2003 2003 2003 2003 2003 2003 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2005 2005 2005 2005 2005 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2010 2010 2010 2010 2010 2010 2010
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R Code
n <- length(x) c <- array(NA,dim=c(401)) l <- array(NA,dim=c(401)) mx <- 0 mxli <- -999 for (i in 1:401) { l[i] <- (i-201)/100 if (l[i] != 0) { x1 <- (x^l[i] - 1) / l[i] } else { x1 <- log(x) } c[i] <- cor(x1,y) if (mx < abs(c[i])) { mx <- abs(c[i]) mxli <- l[i] } } c mx mxli if (mxli != 0) { x1 <- (x^mxli - 1) / mxli } else { x1 <- log(x) } r<-lm(y~x) se <- sqrt(var(r$residuals)) r1 <- lm(y~x1) se1 <- sqrt(var(r1$residuals)) bitmap(file='test1.png') plot(l,c,main='Box-Cox Linearity Plot',xlab='Lambda',ylab='correlation') grid() dev.off() bitmap(file='test2.png') plot(x,y,main='Linear Fit of Original Data',xlab='x',ylab='y') abline(r) grid() mtext(paste('Residual Standard Deviation = ',se)) dev.off() bitmap(file='test3.png') plot(x1,y,main='Linear Fit of Transformed Data',xlab='x',ylab='y') abline(r1) grid() mtext(paste('Residual Standard Deviation = ',se1)) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Box-Cox Linearity Plot',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'# observations x',header=TRUE) a<-table.element(a,n) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'maximum correlation',header=TRUE) a<-table.element(a,mx) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'optimal lambda(x)',header=TRUE) a<-table.element(a,mxli) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Residual SD (orginial)',header=TRUE) a<-table.element(a,se) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Residual SD (transformed)',header=TRUE) a<-table.element(a,se1) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab')
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Big Analytics Cloud Computing Center
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