Wessa.net

Send output to:


Data X:
14 11 6 12 8 10 10 11 16 11 13 12 8 12 11 4 9 8 8 14 15 16 9 14 11 8 9 9 9 9 10 16 11 8 9 16 11 16 12 12 14 9 10 9 10 12 14 14 10 14 16 9 10 6 8 13 10 8 7 15 9 10 12 13 10 11 8 9 13 11 8 9 9 15 9 10 14 12 12 11 14 6 12 8 14 11 10 14 12 10 14 5 11 10 9 10 16 13 9 10 10 7 9 8 14 14 8 9 14 14 8 8 8 7 6 8 6 11 14 11 11 11 14 8 20 11 8 11 10 14 11 9 9 8 10 13 13 12 8 13 14 12 14 15 13 16 9 9 9 8 7 16 11 9 11 9 14 13 16

Data Y:
11 7 17 10 12 12 11 11 12 13 14 16 11 10 11 15 9 11 17 17 11 18 14 10 11 15 15 13 16 13 9 18 18 12 17 9 9 12 18 12 18 14 15 16 10 11 14 9 12 17 5 12 12 6 24 12 12 14 7 13 12 13 14 8 11 9 11 13 10 11 12 9 15 18 15 12 13 14 10 13 13 11 13 16 8 16 11 9 16 12 14 8 9 15 11 21 14 18 12 13 15 12 19 15 11 11 10 13 15 12 12 16 9 18 8 13 17 9 15 8 7 12 14 6 8 17 10 11 14 11 13 12 11 9 12 20 12 13 12 12 9 15 24 7 17 11 17 11 12 14 11 16 21 14 20 13 11 15 19


Sample Range: (leave blank to include all observations)
From:
To:

bandwidth of density plot (?)


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')

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	0 seconds
R Server	Big Analytics Cloud Computing Center