onderling effect pearson

*The author of this computation has been verified*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Sun, 26 Dec 2010 14:25:09 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/26/t1293374245sen1wgf0mp6uky3.htm/, Retrieved Sun, 26 Dec 2010 15:37:35 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/26/t1293374245sen1wgf0mp6uky3.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

332 5140 369 4749 384 3635 373 4305 378 5805 426 4260 423 3869 397 7325 422 9280 409 6222 430 3272 412 7598 470 1345 491 1900 504 1480 484 1472 474 3823 508 4454 492 3357 452 5393 457 8329 457 4152 471 4042 451 7747 493 1451 514 911 522 -406 490 1387 484 2150 506 1577 501 2642 462 4273 465 8064 454 3243 464 1112 427 2280 460 505 473 744 465 -1369 422 -531 415 1041 413 2076 420 577 363 5080 376 6584 380 3761 384 294 346 5020 389 1141 407 3805 393 2127 346 2531 348 3682 353 3263 364 2798 305 5936 307 10568 312 5296 312 1870 286 4390 324 3707 336 5201 327 3748 302 5282 299 5349 311 6249 315 5517 264 8640 278 15767 278 8850 287 5582 279 6496 324 3255 354 6189 354 6452 360 5099 363 6833 385 7046 412 7739 370 10142 389 16054 395 7721 417 6182 404 6490 456 3704 478 6235 468 4655 437 5072 432 3640 441 5147 449 5703 386 11889 396 15603 394 9589

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 7 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation werklooshiedsstotaal[t] = + 440.696158299161 -0.0119905326726999bevolkingstotaal[t] -4.34812010455682M1[t] + 31.6196540120970M2[t] + 16.8877923226655M3[t] -2.04979044867875M4[t] + 6.87509014829945M5[t] + 28.2465203538685M6[t] + 29.5687333416249M7[t] + 22.1264012219252M8[t] + 80.8205395981514M9[t] + 17.3720507681680M10[t] -7.4052486755128M11[t] + e[t] Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STAT H0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 440.696158299161 28.40997 15.512 0 0 bevolkingstotaal -0.0119905326726999 0.00284 -4.2228 6.3e-05 3.1e-05 M1 -4.34812010455682 33.182245 -0.131 0.896071 0.448035 M2 31.6196540120970 32.426427 0.9751 0.332404 0.166202 M3 16.8877923226655 33.175093 0.5091 0.612101 0.30605 M4 -2.04979044867875 32.793382 -0.0625 0.950314 0.475157 M5 6.87509014829945 32.278128 0.213 0.831866 0.415933 M6 28.2465203538685 32.192407 0.8774 0.382849 0.191425 M7 29.5687333416249 32.284491 0.9159 0.362448 0.181224 M8 22.1264012219252 32.253659 0.686 0.494664 0.247332 M9 80.8205395981514 35.620195 2.269 0.025931 0.012966 M10 17.3720507681680 31.943891 0.5438 0.588052 0.294026 M11 -7.4052486755128 33.741821 -0.2195 0.826838 0.413419 Multiple Linear Regression - Regression Statistics Multiple R 0.495695686793552 R-squared 0.245714213905732 Adjusted R-squared 0.133968171521396 F-TEST (value) 2.19886278442532 F-TEST (DF numerator) 12 F-TEST (DF denominator) 81 p-value 0.0191132452980280 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 61.6849357804601 Sum Squared Residuals 308207.535481399 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 332 374.716700256925 -42.7167002569247 2 369 415.372772648605 -46.3727726486054 3 384 413.998364356562 -29.9983643565618 4 373 387.027124694509 -14.0271246945086 5 378 377.966206282437 0.0337937175630842 6 426 417.863009467327 8.13699053267272 7 423 423.873520730109 -0.873520730109377 8 397 374.991907693559 22.0080923064412 9 422 410.244554694657 11.7554453053434 10 409 383.463114777790 25.5368852222104 11 430 394.057886718574 35.9421132814264 12 412 349.592091051987 62.4079089480135 13 470 420.220771749822 49.7792282501777 14 491 449.533800233128 41.4661997668724 15 504 439.83796226623 64.1620377337699 16 484 420.996303756267 63.0036962437325 17 474 401.731442039728 72.2685579602719 18 508 415.536846128824 92.4631538711765 19 492 430.012673458532 61.9873265414683 20 452 398.157616817215 53.8423831827849 21 457 421.647551266394 35.3524487336058 22 457 408.283517410278 48.7164825897216 23 471 384.825176560595 86.1748234394054 24 451 347.805501683754 103.194498316246 25 493 418.949775286516 74.0502247134839 26 514 461.392437046428 52.6075629535722 27 522 462.452106886942 59.5478931130579 28 490 422.015499033447 67.9845009665531 29 484 421.791603201155 62.2083967988449 30 506 450.033608628181 55.9663913718188 31 501 438.585904319512 62.4140956804879 32 462 411.587013410639 50.412986589361 33 465 424.82504242466 40.1749575753403 34 454 419.182911609763 34.8170883902374 35 464 419.957437291605 44.0425627083946 36 427 413.357743805405 13.6422561945953 37 460 430.29281919489 29.7071808051098 38 473 463.394856002769 9.60514399723133 39 465 473.998989850752 -8.99898985075214 40 422 445.013340699685 -23.0133406996854 41 415 435.089103935179 -20.0891039351793 42 413 444.050332824504 -31.0503328245039 43 420 463.346354288637 -43.3463542886375 44 363 401.91065354377 -38.9106535437701 45 376 442.571030780256 -66.5710307802556 46 380 412.971815685304 -32.9718156853041 47 384 429.765693017874 -45.7656930178739 48 346 380.503684282207 -34.5036842822069 49 389 422.666840415053 -33.6668404150531 50 407 426.691835491634 -19.6918354916342 51 393 432.080087626993 -39.0800876269932 52 346 408.298329655878 -62.2983296558782 53 348 403.422107146579 -55.4221071465788 54 353 429.817570542009 -76.8175705420091 55 364 436.715381222571 -72.715381222571 56 305 391.646757575939 -86.646757575939 57 307 394.800748612219 -87.8007486122191 58 312 394.56634803271 -82.5663480327097 59 312 410.868613525699 -98.8686135256989 60 286 388.057719866008 -102.057719866008 61 324 391.899133576905 -67.8991335769051 62 336 409.953051880545 -73.9530518805452 63 327 412.643434164547 -85.6434341645467 64 302 375.312374273281 -73.3123742732808 65 299 383.433889181188 -84.4338891811881 66 311 394.013839981327 -83.0138399813272 67 315 404.1131228855 -89.1131228854999 68 264 359.224357228958 -95.2243572289584 69 278 332.461969246852 -54.4619692468523 70 278 351.951994913934 -73.9519949139342 71 287 366.359756244637 -79.3597562446368 72 279 362.805658057302 -83.8056580573018 73 324 397.318854344965 -73.3188543449654 74 354 398.106405599918 -44.1064055999177 75 354 380.221033817566 -26.2210338175661 76 360 377.506641752385 -17.5066417523849 77 363 365.639938694901 -2.6399386949014 78 385 384.457385441185 0.542614558814658 79 412 377.470159286761 34.5298407132393 80 370 341.214577154563 28.7854228454368 81 389 329.020686369787 59.9793136302126 82 395 365.489306301412 29.5106936985876 83 417 359.165436641017 57.8345633589832 84 404 362.877601253338 41.122398746662 85 456 391.935105174923 64.0648948250768 86 478 397.554841096973 80.4451589030266 87 468 401.768021030408 66.2319789695921 88 437 377.830386134548 59.1696138654523 89 432 403.925709518832 28.0742904811678 90 441 407.227406986643 33.7725930133575 91 449 401.882883808378 47.1171161916223 92 386 320.267116575356 65.7328834246436 93 396 334.428416605175 61.5715833948249 94 394 343.090991268809 50.909008731191 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0159462600957028 0.0318925201914055 0.984053739904297 17 0.00393798615229698 0.00787597230459395 0.996062013847703 18 0.051467802256983 0.102935604513966 0.948532197743017 19 0.0369035106032164 0.0738070212064327 0.963096489396784 20 0.0187548727974323 0.0375097455948645 0.981245127202568 21 0.00782799530286613 0.0156559906057323 0.992172004697134 22 0.00447902254626861 0.00895804509253722 0.995520977453731 23 0.00721469896805538 0.0144293979361108 0.992785301031945 24 0.00630828220764321 0.0126165644152864 0.993691717792357 25 0.0037833156034488 0.0075666312068976 0.996216684396551 26 0.00185255544875526 0.00370511089751051 0.998147444551245 27 0.00131164233410189 0.00262328466820378 0.998688357665898 28 0.000705941600146206 0.00141188320029241 0.999294058399854 29 0.000607980792170178 0.00121596158434036 0.99939201920783 30 0.000905106401406626 0.00181021280281325 0.999094893598593 31 0.00055701337225553 0.00111402674451106 0.999442986627744 32 0.000446658279367709 0.000893316558735418 0.999553341720632 33 0.000259269970797277 0.000518539941594555 0.999740730029203 34 0.000253424028586396 0.000506848057172791 0.999746575971414 35 0.00057070060610457 0.00114140121220914 0.999429299393895 36 0.0153990379812211 0.0307980759624423 0.98460096201878 37 0.012123756117450 0.024247512234900 0.98787624388255 38 0.00926627022409388 0.0185325404481878 0.990733729775906 39 0.0122850374098263 0.0245700748196525 0.987714962590174 40 0.0198506136129174 0.0397012272258348 0.980149386387083 41 0.0279403435991922 0.0558806871983845 0.972059656400808 42 0.0408544540712134 0.0817089081424267 0.959145545928787 43 0.0565219623120366 0.113043924624073 0.943478037687963 44 0.0663262316778637 0.132652463355727 0.933673768322136 45 0.0925522335786154 0.185104467157231 0.907447766421385 46 0.0963479345665922 0.192695869133184 0.903652065433408 47 0.131900798155504 0.263801596311007 0.868099201844496 48 0.145500230864241 0.291000461728483 0.854499769135759 49 0.133420532139892 0.266841064279784 0.866579467860108 50 0.110922349551123 0.221844699102246 0.889077650448877 51 0.101965635361836 0.203931270723672 0.898034364638164 52 0.103766834491988 0.207533668983976 0.896233165508012 53 0.101625891128340 0.203251782256680 0.89837410887166 54 0.115028284643528 0.230056569287056 0.884971715356472 55 0.113749237636423 0.227498475272847 0.886250762363576 56 0.121591316440664 0.243182632881327 0.878408683559336 57 0.115820138115194 0.231640276230388 0.884179861884806 58 0.112940262108633 0.225880524217266 0.887059737891367 59 0.126137422623634 0.252274845247269 0.873862577376366 60 0.147632303015984 0.295264606031969 0.852367696984015 61 0.142984847976496 0.285969695952992 0.857015152023504 62 0.138276543600810 0.276553087201620 0.86172345639919 63 0.140179783878509 0.280359567757017 0.859820216121491 64 0.145710465939665 0.29142093187933 0.854289534060335 65 0.161795877327603 0.323591754655207 0.838204122672397 66 0.178023062635584 0.356046125271168 0.821976937364416 67 0.214075906784432 0.428151813568865 0.785924093215568 68 0.302810442840872 0.605620885681743 0.697189557159128 69 0.355111960610548 0.710223921221095 0.644888039389452 70 0.422154611273223 0.844309222546446 0.577845388726777 71 0.544236870505093 0.911526258989814 0.455763129494907 72 0.656035658850754 0.687928682298492 0.343964341149246 73 0.833178200499166 0.333643599001668 0.166821799500834 74 0.946559819499434 0.106880361001133 0.0534401805005663 75 0.969675855733382 0.0606482885332368 0.0303241442666184 76 0.991059677837447 0.0178806443251063 0.00894032216255316 77 0.97955359224538 0.04089281550924 0.02044640775462 78 0.977242541581288 0.0455149168374241 0.0227574584187121 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 13 0.206349206349206 NOK 5% type I error level 26 0.412698412698413 NOK 10% type I error level 30 0.476190476190476 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293374245sen1wgf0mp6uky3/102nsk1293373499.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293374245sen1wgf0mp6uky3/102nsk1293373499.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293374245sen1wgf0mp6uky3/1d4v81293373499.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293374245sen1wgf0mp6uky3/1d4v81293373499.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293374245sen1wgf0mp6uky3/2d4v81293373499.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293374245sen1wgf0mp6uky3/2d4v81293373499.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293374245sen1wgf0mp6uky3/3ovct1293373499.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293374245sen1wgf0mp6uky3/3ovct1293373499.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293374245sen1wgf0mp6uky3/4ovct1293373499.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293374245sen1wgf0mp6uky3/4ovct1293373499.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293374245sen1wgf0mp6uky3/5ovct1293373499.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293374245sen1wgf0mp6uky3/5ovct1293373499.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293374245sen1wgf0mp6uky3/6gncw1293373499.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293374245sen1wgf0mp6uky3/6gncw1293373499.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293374245sen1wgf0mp6uky3/7rwth1293373499.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293374245sen1wgf0mp6uky3/7rwth1293373499.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293374245sen1wgf0mp6uky3/8rwth1293373499.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293374245sen1wgf0mp6uky3/8rwth1293373499.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293374245sen1wgf0mp6uky3/9rwth1293373499.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t1293374245sen1wgf0mp6uky3/9rwth1293373499.ps (open in new window)

Parameters (Session):

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

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