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Data X:
637.12 633.12 639.38 646.62 655.88 662.88 652.88 654.88 651.88 643.5 651 645.88 655.12 655.38 651.12 639.38 648.38 649.62 650.25 649.38 649.62 635.12 646.38 637.12 641.12 639.62 638.12 641.25 649.38 607.38 603.38 602.38 603.12 606.88 595.88 588.12 586.25 592.38 635.5 625.75 646.25 653.25 644.88 640.38 652.25 680.12 687.25 697 690.75 695.62 703.88 721.62 725.25 747.38 736.5 736.62 736.25 750.5 757.12 749.62 760.62 744.75 765.88 771.5 764.62 758.88 754.12 731.88 730.25 728.62 724.75 712.5 711.38 698 709.5 713.62 706 708.62 701.62 706.62 678.75 686.5 669.75 686.38 693.12 687.5 701.75 711.25 682.5 668.62 666.75 683.75 687.12 688 677.12 679.25 690.38 682.38 681.75 705.25 703.62 687.75 703.62 689 684.88 664.88 642.75 643.62 642.88 627.38 617.12 626.38 620.38 606.5
Data Y:
616.38 614.62 618 621.38 642.62 658.12 639 635.38 634.62 624.38 626.12 629.25 647.62 646.12 642.62 621.38 635.38 642.38 633.75 630.62 630.88 617 629.62 624.88 621.38 623.88 621.75 623.88 657.5 614.62 612.12 608.88 615.12 626.38 611.12 615.88 612.38 606 658.5 632.75 652.38 654.5 646.12 642.62 637.62 662.12 673.12 676.62 688.5 693.38 697.75 704 703.88 731.62 728 729.12 738.12 753.5 763.62 765.62 775.88 765.12 788.12 785.88 789.75 770.62 762 754.38 746.62 755 737.62 732.12 731.75 706.12 725.5 725.62 710.38 711.12 703.62 703.12 684.5 682.88 651.88 673.62 677.75 673 704.88 715.38 681 674.88 672.88 697.38 699.88 692.12 687.62 683.62 693.38 680.75 687.38 700.62 693.62 681.38 695.38 707.88 716 667.5 638.12 647.38 650 635.25 624.12 635.25 625.62 611.62
Sample Range:
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bandwidth of density plot
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Chart options
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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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