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Data X:
85.45454545 61.81818182 47.27272727 78.18181818 70.90909091 76.36363636 67.27272727 74.54545455 70.90909091 76.36363636 74.54545455 72.72727273 69.09090909 87.27272727 78.18181818 58.18181818 76.36363636 83.63636364 78.18181818 61.81818182 67.27272727 69.09090909 69.09090909 83.63636364 90.90909091 74.54545455 74.54545455 65.45454545 87.27272727 74.54545455 81.81818182 80 72.72727273 72.72727273 81.81818182 74.54545455 69.09090909 69.09090909 100 67.27272727 76.36363636 76.36363636 58.18181818 78.18181818 70.90909091 61.81818182 72.72727273 70.90909091 80 74.54545455 67.27272727 81.81818182 74.54545455 72.72727273 83.63636364 80 83.63636364 78.18181818 67.27272727 89.09090909 80 61.81818182 69.09090909 78.18181818 72.72727273 76.36363636 72.72727273 70.90909091 78.18181818 61.81818182 81.81818182 72.72727273 83.63636364 74.54545455 70.90909091 72.72727273 65.45454545 65.45454545 70.90909091 69.09090909 80 76.36363636 67.27272727 70.90909091 78.18181818 78.18181818 78.18181818 74.54545455 78.18181818 69.09090909 61.81818182 80 76.36363636 76.36363636 74.54545455 81.81818182 78.18181818 60 76.36363636 83.63636364 76.36363636 70.90909091 76.36363636 65.45454545 74.54545455 67.27272727 80 61.81818182 60 74.54545455 78.18181818 63.63636364 69.09090909 85.45454545 63.63636364 65.45454545 63.63636364 61.81818182 65.45454545 78.18181818 61.81818182 72.72727273 60 74.54545455 61.81818182 69.09090909 69.09090909 70.90909091 74.54545455 70.90909091 70.90909091 61.81818182 61.81818182 69.09090909 63.63636364 76.36363636 61.81818182 78.18181818 69.09090909 70.90909091 81.81818182 65.45454545 76.36363636 69.09090909 63.63636364 70.90909091 69.09090909 83.63636364 87.27272727 70.90909091 74.54545455 70.90909091 80 83.63636364 76.36363636 70.90909091 67.27272727 70.90909091 72.72727273 63.63636364
Data Y:
37 2 3 6 17 14 3 4 18 40 0 35 12 22 50 3 3 16 12 2 4 16 6 0 21 21 2 35 10 4 36 7 17 10 14 7 12 14 45 15 20 16 9 26 15 12 12 11 30 14 24 16 10 2 14 11 16 4 15 0 12 0 13 18 11 13 24 20 12 14 21 21 0 46 2 0 3 3 25 13 18 24 20 24 23 15 7 3 0 35 14 8 0 13 12 21 12 18 6 39 8 25 26 19 4 18 14 18 13 21 4 15 0 1 0 0 10 24 19 12 0 2 8 24 23 42 6 24 18 3 14 0 32 10 4 23 5 18 24 36 40 20 40 33 17 14 40 27 24 4 15 8 43 14 24 3 1 31 12 13
Sample Range:
(leave blank to include all observations)
From:
To:
bandwidth of density plot
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Label x-axis:
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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