Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
13768040.14 17487530.67 16198106.13 17535166.38 16571771.60 16198892.67 16554237.93 19554176.37 15903762.33 18003781.65 18329610.38 16260733.42 14851949.20 18174068.44 18406552.23 18466459.42 16016524.60 17428458.32 17167191.42 19629987.60 17183629.01 18344657.85 19301440.71 18147463.68 16192909.22 18374420.60 20515191.95 18957217.20 16471529.53 18746813.27 19009453.59 19211178.55 20547653.75 19325754.03 20605542.58 20056915.06 16141449.72 20359793.22 19711553.27 15638580.70 14384486.00 13855616.12 14308336.46 15290621.44 14423755.53 13779681.49 15686348.94 14733828.17 12522497.94 16189383.57 16059123.25 16007123.26 15806842.33 15159951.13 15692144.17 18908869.11 16969881.42 16997477.78 19858875.65 17681170.13
Data Y:
14731798.37 16471559.62 15213975.95 17637387.4 17972385.83 16896235.55 16697955.94 19691579.52 15930700.75 17444615.98 17699369.88 15189796.81 15672722.75 17180794.3 17664893.45 17862884.98 16162288.88 17463628.82 16772112.17 19106861.48 16721314.25 18161267.85 18509941.2 17802737.97 16409869.75 17967742.04 20286602.27 19537280.81 18021889.62 20194317.23 19049596.62 20244720.94 21473302.24 19673603.19 21053177.29 20159479.84 18203628.31 21289464.94 20432335.71 17180395.07 15816786.32 15071819.75 14521120.61 15668789.39 14346884.11 13881008.13 15465943.69 14238232.92 13557713.21 16127590.29 16793894.2 16014007.43 16867867.15 16014583.21 15878594.85 18664899.14 17962530.06 17332692.2 19542066.35 17203555.19
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')
Compute
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
Click here to blog (archive) this computation