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Data X:
22 16 22 21 14 13 18 18 15 16 25 32 20 28 23 35 20 22 15 23 23 22 14 26 24 18 15 23 17 22 25 28 30 20 20 20 26 16 8 20 20 25 16 18 17 22 26 24 24 20 12 4 26 18 11 16 29 20 23 20 27 18 27 22 18 10 15 23 20 15 20 16 20 20 22 24 20 19 18 21 18 25 21 23 8 20 22 19 22 23 20 21 19 20 20 16 20 19 33 19 27 21 20 19 12 17 18 20 16 20 23 26 1 20 11 22 19 0 16 8 23 26 18 13 8 19 29 28 26 27 30 17 16 19 24 16 22 18 21 22 9 21 21 21 25 25 16 25 0 0 0 0 0 0 16 27 0 0 0 5 1 23 0 12
Data Y:
68 56 30 70 29 43 66 22 52 34 87 107 57 89 41 122 75 45 40 86 68 76 48 104 83 63 40 83 56 71 93 72 107 75 72 62 90 40 18 75 55 63 55 47 23 69 66 84 73 70 42 7 95 61 32 56 108 71 78 69 85 47 50 76 56 21 56 68 66 52 65 51 72 50 75 81 75 61 62 61 55 60 79 32 27 59 82 71 32 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 49 0 29 9 78 90 50 24 21 71 102 83 89 83 104 60 48 71 81 50 82 45 67 59 12 57 62 78 73 89 51 84 0 0 0 0 0 0 34 55 0 0 0 13 4 31 0 19
Sample Range:
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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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