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Data X:
17298 9858 8467 20510 5114 7721 23654 1271 13872 11235 12777 10681 5603 10843 6589 16859 14431 8857 15750 6978 25068 12029 3960 11590 15216 15173 12205 14522 6008 7005 27658 6268 12588 1308 20414 3963 14294 10474 11082 17263 12043 10149 13551 6083 4922 8065 6773 12649 12030 12688 12485 2694 11347 3597 5011 17745 12888 20771 23195 7483 17441 27499 7763 10506 17544 9863 11786 14626 11114 9482 9432 2970 10957 10467 11882 14093 16933 7947 9348 7854 8475 15615 8312 9400 7905 4525 16732 10784 15550 19591 5537 11778 4695 8911 15746 11288 17295 18560 15476 26914 16766 14341 5373 7409 10791 21000 23021 15492 11181 6006 14271 9346 238 13240 3879 4397 11417 338 7941 3988 13508 11025 1819 5531 1888 12343 16321 14263 19165 9157 25740 12142 4565 12694 13172 304 20477 7945 17712 6397 303 11306 16379 2805 15448 9031 15706 19234 0 2065 0 0 0 0 10853 8571 0 0 556 2089 2658 1419 0 7521
Data Y:
78973 46146 46492 60656 21898 36555 74680 22807 61282 37981 41553 45081 38557 51641 30658 52924 79256 53462 68950 53639 67819 48333 28001 51665 39019 46221 65792 39858 19574 41829 78688 36781 44314 24874 56911 37048 48426 33388 26998 46502 41507 40001 33144 29501 43059 43249 29272 49821 98341 44372 42448 5950 64839 32551 30767 62046 71930 67328 67253 35373 85544 88087 30621 50580 49670 25456 69245 43787 53638 35683 38008 18801 44324 51408 53880 55708 63858 183643 35660 41664 29883 62047 33321 46553 56622 15430 49379 58215 38253 77786 21331 55292 30105 37651 59370 46216 73122 93927 55935 93308 74344 78094 25625 43750 28995 47336 57582 60875 165877 32984 61638 36367 1168 40530 21427 15024 39088 855 80455 14116 43915 76705 40112 41821 8773 52045 51491 53470 53211 63091 131634 41745 23656 51442 54574 35708 66627 39585 50029 25266 34860 62759 62307 37238 42452 59820 75075 97567 0 6023 0 0 0 0 42420 31116 0 0 1644 6179 3926 23238 0 38818
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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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