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Data X:
9492 8767 1423 15387 11936 22386 7703 7694 14513 12552 10893 14584 5469 8334 8651 13657 22452 6023 10602 12476 783 16597 5506 5852 6630 4065 12579 10432 50116 8628 3482 7195 11938 15129 5050 0 7537 8665 3710 11128 7980 6851 15966 22214 7713 6154 2928 10805 17412 9243 6656 3585 11016 14667 11708 7619 11853 9540 10081 2574 3772 3886 8984 5956 10878 9852 13736 8033 9776 1536 6948 5846 7318 4898 1358 0 9580 17612 2941 6924 6387 3690 7153 8765 5761 8809 10686 8055 8352 6030 14070 17349 5645 4120 13983 3853 19150 3895 4408 7498 2325 11429 17265 6265 3087 3979 8931 3721 0 6185 14254 7500 5890 0 0 0 10411 28040 103 14377 2781 8759 2229 2423 13656 2790 10255 4845 5264 0 0 0 0 0 0 0 0 7711 797 0 0 4010 5959 2338
Data Y:
45943 59385 7176 63907 112746 218511 51741 60400 63412 125468 50224 81908 35821 71437 59999 64114 49519 14903 56100 11805 33983 77726 47624 44572 61932 47968 64711 59855 130532 71572 55974 73726 44795 24608 65081 0 45624 60122 31241 73314 65398 41061 30085 44881 35236 24791 24886 87233 55304 42244 33820 37861 77191 76157 81220 46992 58593 22923 48977 17919 28651 27666 22528 33493 41603 44299 49670 58803 31404 12185 83343 25915 29352 35557 23272 17647 51193 35126 15563 21057 22050 66490 27190 48592 21764 47480 74948 17111 28728 45984 45664 51855 46737 46743 14189 102297 58066 19349 60378 47671 12992 17528 34532 17934 22361 20460 47949 16231 0 23392 20284 44393 35245 34131 3058 0 71090 24715 50785 33277 43051 61450 24215 25685 32306 15802 27437 34800 47050 11747 6046 41621 6836 11056 510 6669 0 28068 8916 7131 4194 14160 11970 26174
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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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