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Data X:
38 36 23 30 26 26 30 27 34 28 36 42 31 30 26 16 30 23 28 45 42 50 30 45 30 24 29 30 31 26 34 41 37 33 26 48 44 29 44 37 43 31 28 26 30 27 34 47 39 37 42 27 30 17 36 39 32 25 19 29 26 31 31 31 20 40 39 28 22 31 36 28 39 44 35 33 27 33 31 39 37 24 33 28 37 32 31 29 40 29 40 15 27 32 28 41 47 42 28 32 33 22 29 26 37 39 29 33 39 31 21 36 29 32 15 24 25 28 39 31 40 25 36 23 39 31 23 31 28 47 33 25 26 24 31 39 31 30 25 35 44 42 38 36 34 45 40 29 25 30 27 44 49 31 31 26 42 35 47
Data Y:
23 15 25 18 21 19 15 22 19 20 26 26 21 18 19 19 18 19 24 28 20 29 27 18 19 24 21 22 25 19 15 34 23 19 26 15 15 17 30 19 28 23 23 21 18 19 24 15 20 24 9 20 20 10 44 20 20 20 11 21 21 19 21 17 16 14 19 21 16 19 19 16 24 29 21 20 19 23 18 19 23 19 21 26 13 23 16 17 30 19 22 14 14 21 21 33 23 30 19 21 25 18 29 25 21 16 17 23 26 18 19 28 20 29 19 18 25 15 24 12 11 19 25 12 15 25 14 19 23 19 24 20 16 13 20 30 18 22 21 25 18 25 44 12 28 17 26 18 21 24 20 24 28 20 33 19 19 25 35
Sample Range:
(leave blank to include all observations)
From:
To:
bandwidth of density plot
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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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R Server
Big Analytics Cloud Computing Center
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