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Data X:
309 210 360 737 143 81 1074 102 488 371 478 318 398 626 363 1003 434 650 302 415 408 756 258 279 750 637 640 339 366 482 553 345 493 150 759 603 626 337 212 670 959 421 385 248 791 1282 330 404 943 527 278 216 512 253 411 585 411 666 563 429 746 740 634 540 340 281 392 584 520 341 417 301 397 450 329 561 598 240 270 396 439 784 516 287 437 548 220 795 435 655 389 403 338 673 513 340 352 427 603 796 380 480 293 214 428 349 505 571 175 322 373 376 135 436 185 261 605 146 528 193 703 332 183 302 276 280 518 233 960 506 718 287 252 544 875 195 1065 407 329 456 212 970 408 716 490 290 618 479 0 85 0 0 0 0 407 489 0 0 74 259 69 187 0 312
Data Y:
78973 46146 46492 60656 21898 36555 74680 22807 61282 37981 41553 45081 38557 51641 30658 52924 79256 53462 68950 53639 67819 48333 28001 51665 39019 46221 65792 39858 19574 41829 78688 36781 44314 24874 56911 37048 48426 33388 26998 46502 41507 40001 33144 29501 43059 43249 29272 49821 98341 44372 42448 5950 64839 32551 30767 62046 71930 67328 67253 35373 85544 88087 30621 50580 49670 25456 69245 43787 53638 35683 38008 18801 44324 51408 53880 55708 63858 183643 35660 41664 29883 62047 33321 46553 56622 15430 49379 58215 38253 77786 21331 55292 30105 37651 59370 46216 73122 93927 55935 93308 74344 78094 25625 43750 28995 47336 57582 60875 165877 32984 61638 36367 1168 40530 21427 15024 39088 855 80455 14116 43915 76705 40112 41821 8773 52045 51491 53470 53211 63091 131634 41745 23656 51442 54574 35708 66627 39585 50029 25266 34860 62759 62307 37238 42452 59820 75075 97567 0 6023 0 0 0 0 42420 31116 0 0 1644 6179 3926 23238 0 38818
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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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