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Data X:
79.14 79.45 80.18 80.42 80.23 79.89 80.11 80.01 80.74 80.93 80.84 80.76 80.88 81.04 81.65 81.73 81.91 81.51 81.8 82.02 82.76 82.84 82.88 82.63 82.36 82.82 83.51 83.71 83.63 83.28 83.46 83.46 84.16 84.35 84.34 84.1 84.16 84.58 85.31 85.49 85.52 85.19 85.13 85.61 86.01 86.08 86.19 85.91 85.77 86.24 86.92 87.07 87.33 87.27 87.11 87.46 88.43 88.59 88.6 88.14 88.96 89.7 90.47 90.97 91.27 90.94 90.88 91.19 92.23 92.14 92.04 91.8 92.54 93.01 93.75 94.18 94.21 93.91 93.87 94.14 95.1 94.87 94.6 94.32 94.72 95.28 96.11 96.19 96.13 95.72 95.86 96.14 97.02 96.75 96.5 95.92 96.01 96.39 97.16 97.46 97.6 97.02 96.95 97.23 98 98.04 97.76 96.99 97.44 98 98.84 98.98 98.92 98.63 98.52 98.97 99.74 99.68 99.45 98.97 98.68 99.06 99.84 100.3 100.38 100.02 99.83 100.36 100.74 100.49 100.33 99.96 100.08 100.54 101.63 102.12 102.19 101.77 101.29 101.47 102.07 102.11 102.26 101.83 102.11 102.8 103.82 104.2 104.57 104.38 104.54 104.74 105.19 104.95 104.57 103.81 104.08 104.81 105.86 106.1 106.24 105.87 104.74 105.03 105.59 105.69 105.58 104.96 104.93 105.68 106.93 107.29 107.25 106.74 106.44 106.6 107.26 107.35
Data Y:
1744799 1659093 2099821 2135736 2427894 2468882 2703217 2766841 2655236 2550373 2052097 1998055 1920748 1876694 2380930 2467402 2770771 2781340 3143926 3172235 2952540 2920877 2384552 2248987 2208616 2178756 2632870 2706905 3029745 3015402 3391414 3507805 3177852 3142961 2545815 2414007 2372578 2332664 2825328 2901478 3263955 3226738 3610786 3709274 3467185 3449646 2802951 2462530 2490645 2561520 3067554 3226951 3546493 3492787 3952263 3932072 3720284 3651555 2914972 2713514 2703997 2591373 3163748 3355137 3613702 3686773 4098716 4063517 3551489 3226663 2656842 2597484 2572399 2596631 3165225 3303145 3698247 3668631 4130433 4131400 3864358 3721110 2892532 2843451 2747502 2668775 3018602 3013392 3393657 3544233 4075832 4032923 3734509 3761285 2970090 2847849 2741680 2830639 3257673 3480085 3843271 3796961 4337767 4243630 3927202 3915296 3087396 2963792 2955792 2829925 3281195 3548011 4059648 3941175 4528594 4433151 4145737 4077132 3198519 3078660 3028202 2858642 3398954 3808883 4175961 4227542 4744616 4608012 4295049 4201144 3353276 3286851 3169889 3051720 3695426 3905501 4296458 4246247 4921849 4821446 4425064 4379099 3472889 3359160 3200944 3153170 3741498 3918719 4403449 4400407 4847473 4716136 4297440 4272253 3271834 3168388 2911748 2720999 3199918 3672623 3892013 3850845 4532467 4484739 4014972 3983758 3158459 3100569 2935404 2855719 3465611 3006985 4095110 4104793 4730788 4642726 4246919 4308117
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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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