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Data X:
21 22 21 21 21 21 21 23 22 25 21 23 22 21 21 25 21 21 20 24 23 21 24 23 21 22 20 18 21 22 22 21 21 25 22 22 20 21 21 21 22 21 24 22 22 21 22 19 22 23 20 20 23 20 23 21 22 21 21 19 22 21 21 21 21 21 21 22 22 18 21 23 19 19 21 21 21 21 20 19 21 19 19 19 20 19 19 19 20 19 18 19 21 18 18 19 21 20 24 22 21 21 19 19 20 18 19 19 20 21 18 19 19 22 22 22 20 19 20 22 21 21 21 21 21 21 21 22 24 21 22 20 21 24 25 22 21 21 22 23 24 20 22 25 22 21 21 21 22 22 21 22 23 21 21 21 19 21 21 19 18 19 21 22 22 19 20 19 21 19 20 21 19 21 21 21 19 25 21 20 25 19 20 22 19 20 19 19 18 19 21 19 20 20 19 19 22 21 19 19 19 23 19 20 19 22 19 25 19 19 19 20 20 21 19 21 23 19 22 20 18 21 20 21 21 21 19 21 19 21 21 22 21 22 22 22 22 21 22 23 19 22 21 19 19 20 18 21 21 20 20 21 21 19 19 21 19 19 24 19 19 20 19 19 19 19 19 19 20 20 19 21 19 19 19 21 22 19
Data Y:
12 8 11 13 11 10 7 10 15 12 12 10 10 14 6 12 14 11 8 12 15 13 11 12 7 11 7 12 12 13 9 11 12 15 12 6 5 13 11 6 12 10 6 12 11 6 12 12 8 10 11 7 12 13 14 12 6 14 10 12 11 10 7 12 7 12 12 10 10 12 12 12 8 10 5 10 10 12 11 9 12 11 10 12 10 9 11 12 7 11 12 6 9 15 10 11 12 12 12 11 9 11 12 12 14 8 10 9 10 9 10 12 11 9 11 12 12 7 12 12 12 10 15 10 15 10 15 9 15 12 13 12 12 8 9 15 12 12 15 11 12 6 14 12 12 12 11 12 12 12 12 8 8 12 12 11 10 11 12 13 12 12 10 10 11 8 12 9 12 9 11 15 8 8 11 11 11 13 7 12 8 8 4 11 10 7 12 11 9 10 8 8 11 12 10 10 12 8 11 8 10 14 9 9 10 13 12 13 8 3 8 12 11 9 12 12 12 10 13 9 12 11 14 11 9 12 8 15 12 14 12 9 9 13 13 15 11 7 10 11 14 14 13 12 8 13 9 12 13 11 11 13 12 12 10 9 10 13 13 9 11 12 8 12 12 12 9 12 12 11 12 6 7 10 12 10 12 9
Sample Range:
(leave blank to include all observations)
From:
To:
bandwidth of density plot
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Chart options
Label y-axis:
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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