Wessa.net

Send output to:


Data X:
278 201 331 650 126 74 962 96 463 371 438 299 352 603 350 944 390 583 288 373 389 689 218 265 675 611 625 293 361 466 514 284 430 131 605 552 616 323 209 595 848 409 363 226 791 1203 310 381 915 525 267 216 460 253 390 585 398 619 561 358 690 696 607 489 296 238 366 541 492 284 414 299 392 411 299 518 542 240 260 339 394 735 497 232 437 548 201 721 405 584 351 356 310 625 465 320 301 419 518 744 334 475 284 206 425 346 490 547 169 291 329 335 135 395 185 252 563 146 517 181 657 277 173 289 276 275 476 215 853 460 668 287 252 520 822 191 970 405 309 396 177 890 366 716 442 290 596 436 0 85 0 0 0 0 389 443 0 0 74 259 69 187 0 295

Data Y:
93809 75589 61071 64672 39014 32213 131696 6853 43253 57555 85473 44501 42453 67285 57056 94982 89324 60461 64491 60717 83022 91708 28378 59360 54436 70629 95409 68631 42319 91440 100462 41411 94894 16617 114686 64881 106679 54866 45988 82618 79199 46657 71070 29970 42948 61186 47824 62913 101444 61145 58388 15049 81451 25109 44688 77378 80180 96279 115202 31648 119420 121683 46568 57903 71075 63394 58118 62099 63739 35768 53318 22330 57871 52090 50767 68496 101760 58441 38972 43530 38996 98432 49867 56434 55792 25155 43461 65474 82234 49606 47992 63723 39066 54501 69924 58204 80248 106887 82830 125642 62525 93166 30028 19630 56584 63525 68879 80800 56474 25551 77587 60521 5841 54108 24587 23872 116211 6622 83478 13155 87473 43580 10439 33067 13983 52276 81079 63507 106262 92878 120184 63110 29996 55746 105611 6783 69866 39663 93382 42310 1472 83141 70434 10901 66220 25867 71760 94809 0 7953 0 0 0 0 63404 89657 0 0 4245 21509 7670 10641 0 31359


Sample Range: (leave blank to include all observations)
From:
To:

bandwidth of density plot (?)


Chart options


Label y-axis:
Label x-axis:

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')

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 seconds
R Server	Big Analytics Cloud Computing Center

Simple Linear Regression
Statistics	Estimate	S.D.	T-STAT (H0: coeff=0)	P-value (two-sided)
constant term	17068.5286977466	3675.03201337086	4.64445714639931	7.015268857824e-06
slope	99.5724648162386	8.04039502295478	12.3840264728245	0