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Data X:
25.94 28.66 33.95 31.01 21.00 26.19 25.41 30.47 12.88 9.78 8.25 7.44 10.81 9.12 11.03 12.74 9.98 11.62 9.40 9.27 7.76 8.78 10.65 10.95 12.36 10.85 11.84 12.14 11.65 8.86 7.63 7.38 7.25 8.03 7.75 7.16 7.18 7.51 7.07 7.11 8.98 9.53 10.54 11.31 10.36 11.44 10.45 10.69 11.28 11.96 13.52 12.89 14.03 16.27 16.17 17.25 19.38 26.20 33.53 32.20 38.45 44.86 41.67 36.06 39.76 36.81 42.65 46.89 53.61 57.59 67.82 71.89 75.51 68.49 62.72 70.39 59.77 57.27 67.96 67.85 76.98 81.08 91.66 84.84 85.73 84.61 92.91 99.80 121.19 122.04 131.76 138.48 153.47 189.95 182.22 198.08 135.36 125.02 143.50 173.95 188.75 167.44 158.95 169.53 113.66 107.59 92.67 85.35 90.13 89.31 105.12 125.83 135.81 142.43 163.39 168.21 185.35 188.50 199.91 210.73 192.06 204.62 235.00 261.09 256.88 251.53 257.25 243.10 283.75 300.98
Data Y:
3940.35 4696.69 4572.83 3860.66 3400.91 3966.11 3766.99 4206.35 3672.82 3369.63 2597.93 2470.52 2772.73 2151.83 1840.26 2116.24 2110.49 2160.54 2027.13 1805.43 1498.80 1690.20 1930.58 1950.40 1934.03 1731.49 1845.35 1688.23 1615.73 1463.21 1328.26 1314.85 1172.06 1329.75 1478.78 1335.51 1320.91 1337.52 1341.17 1464.31 1595.91 1622.80 1735.02 1810.45 1786.94 1932.21 1960.26 2003.37 2066.15 2029.82 1994.22 1920.15 1986.74 2047.79 1887.36 1838.10 1896.84 1974.99 2096.81 2175.44 2062.41 2051.72 1999.23 1921.65 2068.22 2056.96 2184.83 2152.09 2151.69 2120.30 2232.82 2205.32 2305.82 2281.39 2339.79 2322.57 2178.88 2172.09 2091.47 2183.75 2258.43 2366.71 2431.77 2415.29 2463.93 2416.15 2421.64 2525.09 2604.52 2603.23 2546.27 2596.36 2701.50 2859.12 2660.96 2652.28 2389.86 2271.48 2279.10 2412.80 2522.66 2292.98 2325.55 2367.52 2091.88 1720.95 1535.57 1577.03 1476.42 1377.84 1528.59 1717.30 1774.33 1835.04 1978.50 2009.06 2122.42 2045.11 2144.60 2269.15 2147.35 2238.26 2397.96 2461.19 2257.04 2109.24 2254.70 2114.03 2368.62 2507.41
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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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