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Data X:
65.03 61.94 62.68 69.8 70.81 70.9 74.52 73.18 64.33 59.44 59.36 62.16 54.37 59.11 60.55 64.04 63.75 67.29 73.73 72.39 78.86 85.16 94.71 91.29 92.56 94.84 105.12 111.67 125.45 133.69 134.08 117 103.88 77.6 57.55 42.78 41.43 39.15 47.48 49.7 58.79 69.59 64.27 71 69.27 75.47 77.97 74.61 79.55 77.3 81.1 84.65 74.22 75.4 76.03 76.67 75.01 81.96 84.25 89.25 91.55 88.92 90.3 88.8 88.03 89.13 91.11 91.94 91.45 91.54 91.02 91.38 91.06 88.86 89.11 88.15 86.19 87.35 86 89.34 92 89.68 90.77 91.08 90.67 89.03 87.69 86.94 86.75 86.9 85.58 85.4 84.32 85.13 86.03 86.2 91.42 93.57 98.9 99.48 97.88 97.94 90.08 99.63 102.2 101.28 104.42 105.15 105.02 104.76 103.33 101.16 101.19 97.18 97.98 101.42 101.07 102.33 102.33 104 105.6 105.4 104.77 104.79 104.31 106.46 102.86 107.94 108.1 108.35 108.47 108.83 112.79 110.46 106.25 106.53 107.95 109.66 107.16 108.15 111.16 112.19 112.35 112.2 111.9 112.76 112.53 113.5 109.96 113.89 111.05 109.16 101 97.18 102.19 103.88 99.35 99.77 99.65 97.69 96.91 100.93 99.06 99.49 97.83 99.59 101.14 100.53 100.59 100.59 102.8 101.56 100.29 100.51 100.22 100.22 99.01 99.09 100.74 99.29 96.9 97.3 99.37 94.81 93.01 93.26 93.26 94.16 95.41 91.16 91.16 90.61 92.89 94.77 96.25 94.94 94.94 94.94 96.89 96.65 96.2 96.2 95.15 97.43 98.05 97.24 97.24 95.93 97.5 98.4 99.87 99.28 99.2 99.59 97.4 95.2 97.06 95.7 94.89 93.79 86.68 86.88 86.88 79.3 80.66 83.75 85.38 87.27 86.65 87.74 87.58 82.26 82.26 85.44 85.9 85.08 85.37 85.37 88.9 88.84 86.63 89.08 86.45 86.45 86.02 89.53 89.19 87.24 87.91 90.21 89 89.28 87.96 85.28 86.89 86.39 85.92 79.85 80.58 84.45 81.67 82.82 79.2 85.97 77.61 75.67 79.16 79.68 82.98 85.6 86.41 85.57 84.77 86.8 86.48 88.34 88.21 86.11 87.4 87.4 91.59 93.17 90.2 93.96 93.2 86.21
Data Y:
55.57 56.5 53.83 52.29 50.97 51.18 51.5 53.8 50.7 49.05 48.44 53.77 54.67 53.96 54.03 50.52 48.83 53.93 62.2 58.23 60.93 63.06 61.77 64.48 69.38 71 77.89 71.43 69.15 69.88 69.64 67.18 61.18 50.14 42.2 44.8 49.04 45.56 42.63 49.2 57.43 54.11 58.65 58.6 60.15 65.36 68.83 73.61 72 74.46 81.22 81.41 80.62 81.43 80.84 82.4 91.32 103.89 132.32 142.13 142.2 143.78 145.2 141.22 140.6 143.25 147.25 147.97 144.06 141.44 141.44 145.44 148.94 152.94 156.94 161.94 161.83 166.83 169.39 164.75 168.44 151.23 172.22 176.22 171.86 167.86 174.51 175.29 180.58 187.58 189.97 186.05 190.02 197.02 204.02 197.02 197.02 187.94 186.56 181.28 191.34 196.52 185.54 195.87 204.45 208.2 215.15 214.5 207.04 205.99 200.98 204.94 197.94 190.94 185.12 192.12 199.12 198.96 205.96 201.87 208.82 204.49 197.99 194.88 193.67 200.23 201.27 195.55 195.55 201.06 208.06 208.06 202.97 204.58 199.73 197.35 196.04 195.52 196.45 189.82 183.17 186.67 186.67 188 181.84 174.89 172.82 178.78 192.55 175.91 179.21 173.19 165.36 153.8 145.4 151.4 150.3 144.3 145.15 151.15 155.04 159.86 155.65 155.61 153.89 153.88 156.03 151.03 151.03 153.5 158.67 156.33 160.97 164.24 161.63 155.63 148.63 145.05 151.05 150.03 150.95 155.55 151.96 145.96 145.18 148.73 154.73 161.22 164.55 165.22 162 160.91 162.14 159.79 155.73 159.79 161.5 159 155.4 136.2 116.58 111.58 106.59 110.35 106.35 101.46 97.95 101.95 101.29 99.33 99.14 97.09 101.09 103.85 102.81 102.08 115.78 105.59 107.17 104.42 105.01 101.48 99.01 97.26 99.16 97.79 100.72 105.03 104.62 108.43 107.55 106.76 106.61 105.14 104.62 102.59 103.92 104.77 104.91 105.88 103.85 106.08 106.08 106.3 106.24 110.02 112.45 110.3 110.72 111.56 113.39 110.62 109.08 104.08 103.81 101.27 97.64 99.99 98.43 99.39 98.33 100.97 99.31 105.28 97.75 100.92 102.75 102.25 101.48 103.34 103.47 100.51 101.56 101.94 100.36 100.18 99.72 96.86 97.1 97.94 99.68 100.32 104.32 104.37 102.29 100.91
Sample Range:
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bandwidth of density plot
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R Code
par1 <- as.numeric(par1) library(lattice) z <- as.data.frame(cbind(x,y)) m <- lm(y~x) summary(m) bitmap(file='test1.png') plot(z,main='Scatterplot, lowess, and regression line') lines(lowess(z),col='red') abline(m) grid() dev.off() bitmap(file='test2.png') m2 <- lm(m$fitted.values ~ x) summary(m2) z2 <- as.data.frame(cbind(x,m$fitted.values)) names(z2) <- list('x','Fitted') plot(z2,main='Scatterplot, lowess, and regression line') lines(lowess(z2),col='red') abline(m2) grid() dev.off() bitmap(file='test3.png') m3 <- lm(m$residuals ~ x) summary(m3) z3 <- as.data.frame(cbind(x,m$residuals)) names(z3) <- list('x','Residuals') plot(z3,main='Scatterplot, lowess, and regression line') lines(lowess(z3),col='red') abline(m3) grid() dev.off() bitmap(file='test4.png') m4 <- lm(m$fitted.values ~ m$residuals) summary(m4) z4 <- as.data.frame(cbind(m$residuals,m$fitted.values)) names(z4) <- list('Residuals','Fitted') plot(z4,main='Scatterplot, lowess, and regression line') lines(lowess(z4),col='red') abline(m4) grid() dev.off() bitmap(file='test5.png') myr <- as.ts(m$residuals) z5 <- as.data.frame(cbind(lag(myr,1),myr)) names(z5) <- list('Lagged Residuals','Residuals') plot(z5,main='Lag plot') m5 <- lm(z5) summary(m5) abline(m5) grid() dev.off() bitmap(file='test6.png') hist(m$residuals,main='Residual Histogram',xlab='Residuals') dev.off() bitmap(file='test7.png') if (par1 > 0) { densityplot(~m$residuals,col='black',main=paste('Density Plot bw = ',par1),bw=par1) } else { densityplot(~m$residuals,col='black',main='Density Plot') } dev.off() bitmap(file='test8.png') acf(m$residuals,main='Residual Autocorrelation Function') dev.off() bitmap(file='test9.png') qqnorm(x) qqline(x) grid() dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Simple Linear Regression',5,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Statistics',1,TRUE) a<-table.element(a,'Estimate',1,TRUE) a<-table.element(a,'S.D.',1,TRUE) a<-table.element(a,'T-STAT (H0: coeff=0)',1,TRUE) a<-table.element(a,'P-value (two-sided)',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'constant term',header=TRUE) a<-table.element(a,m$coefficients[[1]]) sd <- sqrt(vcov(m)[1,1]) a<-table.element(a,sd) tstat <- m$coefficients[[1]]/sd a<-table.element(a,tstat) pval <- 2*(1-pt(abs(tstat),length(x)-2)) a<-table.element(a,pval) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'slope',header=TRUE) a<-table.element(a,m$coefficients[[2]]) sd <- sqrt(vcov(m)[2,2]) a<-table.element(a,sd) tstat <- m$coefficients[[2]]/sd a<-table.element(a,tstat) pval <- 2*(1-pt(abs(tstat),length(x)-2)) a<-table.element(a,pval) 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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