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Data X:
77 72 70 68 63 63 62 62 62 62 60 60 58 57 57 56 53 53 52 52 50 50 50 49 49 48 48 48 48 48 47 47 47 46 46 46 45 45 44 43 43 43 43 43 42 41 41 41 41 41 41 40 40 40 39 38 38 38 38 38 38 37 37 37 37 36 36 36 36 35 35 35 35 34 34 34 34 33 33 33 32 32 31 31 31 31 31 30 30 30 30 30 30 30 30 30 30 30 29 28 28 28 28 28 27 27 26 26 26 25 25 25 25 25 25 25 25 24 24 24 23 22 22 22 22 22 22 22 21 21
Data Y:
2266 3213 1715 2219 2513 2194 1909 2305 2052 2378 1774 2706 1542 1697 1914 2609 2039 2024 1479 1387 1269 1272 1963 1384 1978 1736 2035 2209 1499 1342 1431 1497 1859 1479 1595 1465 1197 1345 1317 1509 1197 1604 1296 1199 1309 1808 1577 1133 1395 1391 1275 1068 1439 1232 1397 1330 1195 873 1169 1359 1169 1430 1172 1228 1108 1752 1109 967 1912 1212 1120 1463 1632 996 1601 843 1077 1484 958 1150 1134 693 1248 996 1053 1154 692 976 888 1107 1015 1196 1038 989 990 1000 1266 984 1157 1773 1020 939 783 1230 807 1179 1120 1051 962 1239 910 1019 756 872 880 803 1015 924 1022 841 1077 670 1577 918 822 870 620 724 866 883
Sample Range:
(leave blank to include all observations)
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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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R Server
Big Analytics Cloud Computing Center
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