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Data X:
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 54 222 522 1084 2023 3229 5198 8080 11132 14877 18448 21418 24525 26708 30336 33613 36727 39660 42131 43769 45025 46760 49.385 51.382 53.800 54.017 53.313 53.577 53.629 54.090 53.725 52.995 52.453 52.220 51.870 51.122 51.296 49.265 50.020 50.058 50.799 50.216 50.571 42.873 42.535 40.069 35.293 31.552 34.590 36.992 37.279 35.618 33.745 32.570 32.227 31.114 30.698 29.453 27.620 24.178 22.031 19.962 17.784 16.259 14.147 11.990 9.988 8.383 6.661 4.111 2.154 1.601 1.295 1.137 876 515 342 206 126 52 33 17
Data Y:
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 3 67 637 1720 3076 4702 6480 9072 12391 16535 20599 23840 27333 29952 31943 33544 36653 39181 42473 44948 46268 47412 48094 49200 51365 53532 55528 54924 54457 54857 54043 54848 53640 42706 52.409 52.040 50.823 49.928 49.283 47.862 47.903 47.677 48.316 47.549 47.517 40.279 39.549 37.094 32.129 29.162 32.144 34.227 34.167 31.682 30.270 28.807 28.102 26.555 26.164 24.248 22.424 19.052 16.928 14.574 12.866 11.177 9.350 7.796 6.125 4.927 3.684 1.963 1.023 713 512 446 333 237 111 56 42 11 5 2
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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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