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Data X:
35.18 35.24 35.00 35.50 36.32 36.04 36.71 36.70 36.17 36.50 36.94 36.42 36.40 36.68 36.54 36.36 36.23 36.39 36.44 36.43 36.47 36.42 36.44 36.44 36.51 36.51 36.26 36.41 36.19 36.45 36.38 36.70 36.69 37.00 37.20 37.28 37.18 37.13 36.87 36.88 36.85 36.87 37.40 37.21 37.40 36.94 36.66 36.59 37.15 37.00 36.47 36.51 36.17 36.62 35.97 36.48 36.34 37.05 37.11 36.92 36.89 36.94 37.19 36.78 36.25 36.67 36.84 36.54 37.09 37.02 37.04 37.47 37.36 37.38 37.18 37.19 37.35 37.33 37.98 37.72 37.75 37.94 37.82 38.07 38.00 38.00 38.00 37.94 38.24 38.52 38.82 38.73 38.58 37.67 37.79 37.65 38.23 38.10 38.46 38.18 38.38 38.78 38.99 38.90 38.88 38.82 38.84 39.16 39.34 39.92 39.54 38.64 38.47 38.01 37.81 38.20 38.31 38.41 38.46 38.59 38.71 38.90 38.65 38.95 38.84 38.97 39.20 39.01 38.78 38.80 39.60 39.42 39.32 39.28 39.49 39.12 39.08 39.45 39.73 39.44 39.34 39.40 39.52 39.60 39.42 39.30 39.43 39.75 39.23 39.39 39.63 39.56 39.31 39.48 39.65 39.27 38.71 38.66 38.78 38.83 38.82 38.62 38.39 38.57 38.60 38.11 38.16 38.40 39.18 39.03 39.12 39.22 39.39 39.77 39.65 39.81 39.79 40.32 40.33 40.48 41.12 41.24 40.82 41.06 40.29 40.18 39.91 39.81 40.25 39.93 39.90 39.95
Data Y:
2518.41 2527.73 2521.10 2502.89 2514.94 2480.65 2514.87 2543.41 2558.77 2592.56 2582.96 2611.03 2600.83 2606.31 2634.16 2613.52 2625.28 2608.80 2631.57 2665.62 2654.85 2639.28 2634.45 2649.34 2649.70 2672.10 2656.93 2665.40 2656.12 2648.46 2680.56 2696.66 2696.96 2668.62 2702.87 2714.81 2708.98 2716.70 2715.16 2687.36 2667.96 2681.07 2660.86 2630.94 2644.14 2656.28 2567.68 2506.19 2561.69 2560.99 2572.46 2504.25 2446.22 2400.78 2296.81 2511.98 2483.53 2540.36 2534.85 2447.17 2439.07 2485.24 2432.85 2386.49 2395.65 2402.45 2329.79 2386.45 2469.56 2460.54 2453.37 2466.00 2464.25 2484.27 2429.67 2412.05 2390.89 2425.68 2463.32 2463.85 2498.24 2509.31 2513.80 2523.36 2528.93 2549.84 2536.22 2494.34 2455.80 2441.65 2461.96 2382.48 2386.53 2336.12 2337.29 2329.60 2389.28 2415.81 2455.99 2462.75 2470.27 2516.26 2522.12 2495.05 2442.75 2431.56 2417.34 2451.93 2501.41 2506.07 2531.75 2557.43 2545.08 2537.90 2517.30 2579.35 2588.92 2587.82 2586.09 2549.82 2571.02 2561.71 2495.93 2505.90 2485.70 2469.46 2518.34 2512.61 2474.08 2452.32 2469.08 2435.96 2424.00 2446.63 2467.55 2460.93 2457.46 2531.76 2537.76 2549.96 2560.24 2538.91 2556.00 2577.46 2569.72 2610.81 2608.85 2603.51 2585.29 2569.22 2601.85 2604.33 2575.16 2570.39 2601.61 2597.73 2603.50 2602.69 2589.73 2579.39 2558.82 2595.93 2613.44 2604.03 2622.78 2635.10 2620.82 2670.38 2662.91 2658.78 2672.38 2666.15 2669.27 2691.78 2691.53 2698.64 2692.74 2672.71 2677.40 2679.07 2688.39 2696.69 2663.84 2710.41 2686.09 2683.42 2702.84 2672.25 2631.64 2624.97 2654.43 2608.06
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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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