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Data X:
225.18 233.09 226.83 221.81 229.61 235.41 222.29 226.04 209.75 203.66 223.57 217.38 217.38 222.84 221.72 216.14 208.2 199.41 186.55 175.42 179.87 192.57 201.11 201.77 202.76 207.37 194.98 186.47 186.91 183.51 175.73 178.13 191.34 201.46 197.8 211.58 216.85 205.25 197.78 203.64 200.69 187.41 193.83 200.44 214.49 260.26 347.84 361.09 428.75 372.07 367.2 353.67 355.45 317.71 287.61 293.13 341.72 361.89 343.13 325.73 315.33 279.72 243.02 219.12 165.59 164.07 156.44 158.75 161.91 173.46 177.52 165.46 180.18 188.8 201.5 201.22 188.89 175.51 176.26 190.68 202.76 207.32 206.26 194.81 197.78 184.05 183.2 149.53 144.15 134.05 153.25 158.35 160.21 184.91 192.41 207.03 212.32 193.7 191.66 184.29 170.76 166.29 146.2 148.12 164.59 181.34 192.64 195.76 193.03 181.69 178 171.83 169.1 176.81 193.73 184 214.74 261.53 235.31 254.33 252.93 211.29 192.6 194.81 187.02 181.49 189.79 174.55 185.25 220.2 228.6 219.95 236.19 200.35 184.74 182.04 173.49 163.71 152.35 162.12 174.29 185.85 197.07 198.63 175.28 162.51 165.44 192.87 211.22 201.56 206.41 221.06 221.01 200.79 203.64 235.12 239.49 203.56 190.48 177.75 172.86 166.67 165.19 166.61 180.02 196.49 182.2 186.3 164.45 173.63 186.5 186.31 183.96 196.7 181.78 183.82 201.2 190.79 178.32 172.36 163.56 169.04 172.09 167.97 164.39 145.5 143.37 150.37 163.39 176.91 173.62 179.66 174.42 177.03 168.66 172.37 176.12 171.24 160.17 167.97 187.38 183.51 189.37 197.74 189.36 193.57 213.18 240.29 252.76 224.81 244.77 257.76 247.53 252.51 264.69 265.05 317.37 329.13 261.48 234.06 190.3 218.15 226.2 237.75 225.25 209.8 206.75 206.5 225 225.25 231.75 236.75 222 207.25 205.6 209 239 280 278 228.68 228 234.25 253.08 249.4 252 240.64 247 261.35 262.43 253 250.82 245.43 236.09 230 224.82 212.35 195.57 191 196.09 197.74 190.9 177.26 162.09 163.57 172.91 169.9 195.19 185.64 164.09 168.5 175.43 181.86 193.43 181.09 178.48 183.73 182.39 192.25 192.19 188.5 182.09 196.95 204.48 191.95 195.41 200.48 200 205.22 202.82 198.1 191.78 177.95 186.62 186.59 190.73 206.45 240.73 201.76 192.5 201.78 236.67 258.1 241.52 190.71 200.32 223.41 201.38 211.83 224.41 211.57 194.77 201.86 225 278.9 259.74 230.45 238.26 250.14 263.81 247.22 229.81 224.27 213.23 239.57 249.7 212.5 203.27 192.05 190.04 202.05 211.91 210.39 231.25 224.3 209.64 206.05 229.7 264.67 246.29 260.91 265.14 284.52 287.48 321.9 321.59 282.39 241 228.48 261.59 270 262.86 277.41 288 287.14 337.65 328.38 374.41 344.77 361.05 374.22 321.95 317.55 317.52 314.64 271.71 261.95 259.18 315.09 337.18
Data Y:
17.3 22.75 19.63 21.25 30.94 30.8 27.7 31.77 34.74 40.55 37.81 27.78 27.78 24.09 21.81 17.83 15.08 16.38 16.34 14.76 11.65 12.04 11.97 12.98 12.9 13.08 11.26 9.58 8.11 6.84 7.8 6.77 5.77 5.93 6.52 6.31 6.03 6.43 6.2 6.71 9.24 10.74 10.53 10.56 9.43 9.69 8.33 7.69 6.97 6.63 6.43 6 5.61 5.52 4.54 4.05 4.1 4.65 4.38 3.55 3.61 3.69 3.83 3.42 2.82 2.78 3.18 4.39 5.12 5.01 5.48 5.32 4.84 5.56 7.04 8.33 7.67 6.34 5.55 5.57 4.68 5.39 5.95 5.7 6.48 7.39 7.56 6.68 6.71 6.44 6.1 5.62 5.82 6.65 7.33 8.3 9.67 8.43 8.52 8.54 8.9 10.57 14.02 11.18 10.16 10.28 10.84 11.22 9.8 10.54 11.5 12.16 11.98 12.61 14.01 14.05 14.13 14.44 15.02 13.43 14.2 14.65 15.31 15.24 14.62 12.97 12 10.93 11.02 9.39 10.06 9.74 8.8 8.51 9.14 8.51 7.59 9.2 10.32 9.59 9.3 9.13 8.63 9 8.42 7.84 8.25 9.46 9.61 10.35 10.31 9.86 9.31 8.71 8.56 8.15 8.24 8.56 10.62 11.15 11.83 10.4 9.68 9.34 9.54 10.29 10.07 10.52 10.3 10.82 11.73 11.01 11.58 12.05 11.77 12.09 12.59 12.76 13.93 14.67 14.8 14.41 14.59 13.61 13.52 14.02 13.59 12.98 11.69 11.84 11.98 12.31 12.53 12.82 12.9 11.98 11.38 12.17 12.81 12.37 11.92 11.12 10.72 10.74 10.69 10.81 11.14 11.3 11.13 11.43 11.57 11.7 11.33 11.38 12.01 12.33 11.52 10.72 9.84 9.68 9.23 8.1 8.62 8.48 7.23 7.46 8.06 8.08 8.11 6.82 6.03 5.42 5.75 6.04 5.38 5.72 6.67 6.75 6.51 5.98 5.63 5.35 5.11 6.01 6.78 8.33 9.64 10.6 9.86 10.41 9.51 9.72 10.06 9.36 8.72 8.05 8.96 8.74 8.54 7.9 7.16 6.6 7.28 7.41 7.31 5.68 5.92 5.18 5.61 5.25 5.79 5.86 6.41 7.02 7.3 7.51 7.89 8.35 7.84 7.26 7.01 6.4 6.73 6.71 6.27 6.1 6.19 6.34 6.03 5.87 6.5 6.86 6.62 7.51 8.17 7.88 8.67 8.96 8.67 8.8 8.92 9.32 8.9 8.53 8.51 9.03 9.6 9.88 10.81 11.61 11.81 13.93 16.19 18.05 17.08 17.46 16.9 15.69 15.86 12.98 12.31 11.51 11.73 11.7 10.9 10.57 10.37 9.59 9.09 9.26 9.9 9.61 9.85 9.99 9.9 10.45 11.66 13.61 12.88 12.52 10.93 12.07 13.21 13.68 14.02 11.7 11.83 11.32 12.24 13.31 12.93 13.47 15.47 16.58 17.8 21.72 23.45 23.16 22.77 24.9 28.38 25.96 19.26 16.32 14.6 15.44 17.7 18.6 22.68
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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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