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Data X:
31/01/2007 28/02/2007 31/03/2007 30/04/2007 31/05/2007 30/06/2007 31/07/2007 31/08/2007 30/09/2007 31/10/2007 30/11/2007 31/12/2007 31/01/2008 29/02/2008 31/03/2008 30/04/2008 31/05/2008 30/06/2008 31/07/2008 31/08/2008 30/09/2008 31/10/2008 30/11/2008 31/12/2008 31/01/2009 28/02/2009 31/03/2009 30/04/2009 31/05/2009 30/06/2009 31/07/2009 31/08/2009 30/09/2009 31/10/2009 30/11/2009 31/12/2009 31/01/2010 28/02/2010 31/03/2010 30/04/2010 31/05/2010 30/06/2010 31/07/2010 31/08/2010 30/09/2010 31/10/2010 30/11/2010 31/12/2010 31/01/2011 28/02/2011 31/03/2011 30/04/2011 31/05/2011 30/06/2011 31/07/2011 31/08/2011 30/09/2011 31/10/2011 30/11/2011 31/12/2011 31/01/2012 29/02/2012 31/03/2012 30/04/2012 31/05/2012 30/06/2012 31/07/2012 31/08/2012 30/09/2012 31/10/2012
Data Y:
1.2999 1.3074 1.3242 1.3516 1.3511 1.3419 1.3716 1.3622 1.3896 1.4227 1.4684 1.457 1.4718 1.4748 1.5527 1.575 1.5557 1.5553 1.577 1.4975 1.4369 1.3322 1.2732 1.3449 1.3239 1.2785 1.305 1.319 1.365 1.4016 1.4088 1.4268 1.4562 1.4816 1.4914 1.4614 1.4272 1.3686 1.3569 1.3406 1.2565 1.2208 1.277 1.2894 1.3067 1.3898 1.3661 1.322 1.336 1.3649 1.3999 1.4442 1.4349 1.4388 1.4264 1.4343 1.377 1.3706 1.3556 1.3179 1.2905 1.3224 1.3201 1.3162 1.2789 1.2526 1.2288 1.24 1.2856 1.2974
Sample Range:
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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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