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Data X:
145766 78642 113289 127858 59528 25417 285071 32750 92936 108063 149277 112619 92159 167645 122490 218894 154534 166221 99745 132469 151199 251194 90298 98318 130114 133160 150852 133986 90545 155231 177702 106521 178549 59102 216420 153601 203589 107673 78776 174599 192038 102531 108418 62398 79864 260371 78800 139046 202021 119127 90761 24019 215627 64672 90402 149157 144731 176629 170460 131729 230652 178429 127465 157162 121578 101823 104349 132045 137862 74427 118803 63131 119127 128736 94763 178575 189257 90583 77597 107325 95780 252436 128784 110865 80953 90557 79146 190898 138401 99946 110959 133606 93456 148443 163576 107135 135420 160134 167432 204800 135069 136833 88506 43287 110313 107627 127431 166298 59464 85837 150763 134062 20764 115199 61675 75795 186239 21054 152149 31249 174869 126627 57260 55352 38214 87280 172148 110569 221936 158395 225323 114015 51227 157216 229805 46069 223607 83448 143703 102524 74792 223851 135508 173260 173505 57817 119515 161915 0 14688 98 455 0 0 118444 165367 0 203 7199 46660 17547 73567 969 91234
Data Y:
68 56 37 70 30 43 74 22 52 34 87 107 61 89 41 122 75 45 40 86 82 76 48 104 83 78 43 83 56 81 93 72 107 75 72 62 90 40 18 75 59 63 55 47 23 69 66 102 73 87 42 7 95 61 32 56 108 71 86 69 85 47 50 76 56 27 68 68 66 52 81 51 91 50 75 81 75 61 62 61 55 60 79 32 27 59 82 71 36 80 36 76 57 73 71 60 67 56 123 65 87 62 37 64 17 28 48 64 43 60 87 75 0 54 30 47 56 0 29 9 78 90 56 24 21 71 102 83 89 83 104 60 48 71 81 58 82 49 75 59 17 57 62 78 73 89 51 84 0 0 0 0 0 0 34 55 0 0 0 13 4 31 0 22
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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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