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Data X:
165 135 121 148 73 49 185 5 125 93 154 98 70 148 100 150 197 114 169 200 148 140 74 128 140 116 147 132 70 144 155 165 161 31 199 78 121 112 41 158 123 104 94 73 52 71 21 155 174 136 128 7 165 21 35 137 174 257 207 103 171 279 83 130 131 126 158 138 200 104 111 26 115 127 140 121 183 68 112 103 63 166 38 163 59 27 108 88 92 170 98 205 96 107 150 123 176 213 208 307 125 208 73 49 82 206 112 139 60 70 112 142 11 130 31 132 219 4 102 39 125 121 42 111 16 70 162 173 171 172 254 90 50 113 187 16 175 90 140 145 141 125 241 16 175 132 154 198 0 5 0 0 0 0 125 174 0 0 6 13 3 35 0 80
Data Y:
140824 110459 105079 112098 43929 76173 187326 22807 144408 66485 79089 81625 68788 103297 69446 114948 167949 125081 125818 136588 112431 103037 82317 118906 83515 104581 103129 83243 37110 113344 139165 86652 112302 69652 119442 69867 101629 70168 31081 103925 92622 79011 93487 64520 93473 114360 33032 96125 151911 89256 95671 5950 149695 32551 31701 100087 169707 150491 120192 95893 151715 176225 59900 104767 114799 72128 143592 89626 131072 126817 81351 22618 88977 92059 81897 108146 126372 249771 71154 71571 55918 160141 38692 102812 56622 15986 123534 108535 93879 144551 56750 127654 65594 59938 146975 143372 168553 183500 165986 184923 140358 149959 57224 43750 48029 104978 100046 101047 197426 160902 147172 109432 1168 83248 25162 45724 110529 855 101382 14116 89506 135356 116066 144244 8773 102153 117440 104128 134238 134047 279488 79756 66089 102070 146760 154771 165933 64593 92280 67150 128692 124089 125386 37238 140015 150047 154451 156349 0 6023 0 0 0 0 84601 68946 0 0 1644 6179 3926 52789 0 100350
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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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