verbetering

*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: Fri, 27 Nov 2009 03:19:49 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317300tcxpdc48q5bfocq.htm/, Retrieved Fri, 27 Nov 2009 11:21:52 +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/2009/Nov/27/t1259317300tcxpdc48q5bfocq.htm/}, year = {2009}, } @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 = {2009}, 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 «

455626 0 454724 461251 470390 474605 516847 0 455626 454724 461251 470390 525192 0 516847 455626 454724 461251 522975 0 525192 516847 455626 454724 518585 0 522975 525192 516847 455626 509239 0 518585 522975 525192 516847 512238 0 509239 518585 522975 525192 519164 0 512238 509239 518585 522975 517009 0 519164 512238 509239 518585 509933 0 517009 519164 512238 509239 509127 0 509933 517009 519164 512238 500875 0 509127 509933 517009 519164 506971 0 500875 509127 509933 517009 569323 0 506971 500875 509127 509933 579714 0 569323 506971 500875 509127 577992 0 579714 569323 506971 500875 565644 0 577992 579714 569323 506971 547344 0 565644 577992 579714 569323 554788 0 547344 565644 577992 579714 562325 0 554788 547344 565644 577992 560854 0 562325 554788 547344 565644 555332 0 560854 562325 554788 547344 543599 0 555332 560854 562325 554788 536662 0 543599 555332 560854 562325 542722 0 536662 543599 555332 560854 593530 0 542722 536662 543599 555332 610763 0 593530 542722 536662 543599 etc...

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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Werkzoekend[t] = + 4526.95980827144 + 9592.97406000194Crisis[t] + 1.01480015176834`y-1`[t] + 0.0185295723351084`y-2`[t] + 0.0183146040141146`y-3`[t] -0.0676007411428346`y-4`[t] + 10538.1631793460M1[t] + 63094.8479288806M2[t] + 17031.705529519M3[t] -2023.71654495441M4[t] -9391.24766424685M5[t] -7193.07284145974M6[t] + 12555.8033114798M7[t] + 12663.4218611850M8[t] + 5037.51185249125M9[t] -1351.76601434561M10[t] + 2791.41971788684M11[t] -110.182403005296t + 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) 4526.95980827144 10559.927059 0.4287 0.669609 0.334805 Crisis 9592.97406000194 3599.239642 2.6653 0.00976 0.00488 `y-1` 1.01480015176834 0.123849 8.1939 0 0 `y-2` 0.0185295723351084 0.177266 0.1045 0.917081 0.458541 `y-3` 0.0183146040141146 0.176211 0.1039 0.91755 0.458775 `y-4` -0.0676007411428346 0.129679 -0.5213 0.60399 0.301995 M1 10538.1631793460 3432.53629 3.0701 0.003156 0.001578 M2 63094.8479288806 3462.555512 18.222 0 0 M3 17031.705529519 8209.162845 2.0747 0.042103 0.021052 M4 -2023.71654495441 8547.234565 -0.2368 0.813605 0.406802 M5 -9391.24766424685 8524.890209 -1.1016 0.274815 0.137407 M6 -7193.07284145974 3922.678962 -1.8337 0.071421 0.03571 M7 12555.8033114798 3632.890019 3.4561 0.000986 0.000493 M8 12663.4218611850 3799.637735 3.3328 0.001443 0.000721 M9 5037.51185249125 4067.14199 1.2386 0.220093 0.110047 M10 -1351.76601434561 3813.412033 -0.3545 0.724165 0.362083 M11 2791.41971788684 3631.4879 0.7687 0.444961 0.22248 t -110.182403005296 43.090195 -2.557 0.012978 0.006489 Multiple Linear Regression - Regression Statistics Multiple R 0.991865523199382 R-squared 0.983797216111585 Adjusted R-squared 0.97942503633217 F-TEST (value) 225.012983396471 F-TEST (DF numerator) 17 F-TEST (DF denominator) 63 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5949.54546237874 Sum Squared Residuals 2230016746.16143 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 455626 461487.065398564 -5861.06539856394 2 516847 514845.53492119 2001.46507881029 3 525192 531314.267637383 -6122.26763738304 4 522975 522209.319184599 765.680815401193 5 518585 513696.685510804 4888.31448919558 6 509239 507302.875599449 1936.12440055141 7 512238 516771.170646469 -4533.17064646858 8 519164 519708.284796769 -544.284796769401 9 517009 519181.867388152 -2172.86738815207 10 509933 511310.570633401 -1377.57063340137 11 509127 508047.029185048 1079.97081495201 12 500875 503688.712183182 -2813.71218318166 13 506971 505743.712730987 1227.28726901320 14 569323 564687.112045278 4635.88795472203 15 579714 581804.916663962 -2090.91666396214 16 577992 575008.943599728 2983.05640027234 17 565644 566706.1430737 -1062.14307370030 18 547344 552206.740934458 -4862.74093445822 19 554788 552311.81169851 2476.18830148956 20 562325 559414.588747123 2910.41125287702 21 560854 559964.555913938 889.444086062061 22 555332 553485.409482729 1846.59051727079 23 543599 551522.246626374 -7923.24662637376 24 536662 536075.226457851 586.773542149097 25 542722 539244.438556022 3477.56144397808 26 593530 597870.496222672 -4340.49622267192 27 610763 604035.537827485 6727.4621725154 28 612613 603879.367718265 8733.63228173472 29 611324 599119.222506213 12204.7774937866 30 594167 596814.374354176 -2647.37435417593 31 595454 597887.175726792 -2433.17572679247 32 590865 598724.078900576 -7859.07890057624 33 589379 586127.829846271 3251.17015372944 34 584428 579218.741154609 5209.25884539137 35 573100 578028.886116269 -4928.88611626914 36 567456 563822.892263054 3633.10773694639 37 569028 568323.217084266 704.782915733981 38 620735 622387.627798242 -1652.62779824216 39 628884 629378.116501682 -494.116501682007 40 628232 619850.556198215 8381.44380178468 41 612117 612702.915326605 -585.915326604691 42 595404 595079.136205316 324.863794684324 43 597141 596896.051399175 244.948600824753 44 593408 598095.446506598 -4687.44650659767 45 590072 587386.584962122 2685.41503787758 46 579799 578594.204146347 1204.79585365316 47 574205 571954.559958998 2250.44004100191 48 572775 563377.06754021 9397.93245978958 49 572942 572287.599817295 654.400182704756 50 619567 625469.087019636 -5902.08701963611 51 625809 626965.882394261 -1156.88239426089 52 619916 615098.329372949 4817.67062705079 53 587625 602598.689235184 -14973.6892351836 54 565724 568771.010386915 -3047.01038691458 55 557274 565056.335805029 -7782.33580502869 56 560576 555879.86879491 4696.13120509025 57 548854 553119.858987848 -4265.85898784830 58 531673 536111.861414678 -4438.86141467814 59 525919 523124.080774573 2794.91922542716 60 511038 513627.060562609 -2589.06056260908 61 498662 509324.933797379 -10662.9337973786 62 555362 549992.597001782 5369.40299821789 63 564591 561245.553859663 3345.44614033734 64 541667 553275.471823922 -11608.4718239225 65 527070 524580.553865552 2489.44613444799 66 509846 507766.800011409 2079.19998859113 67 514258 508612.368557483 5645.63144251713 68 516922 514050.290735049 2871.70926495098 69 507561 509770.695679726 -2209.69567972623 70 492622 495066.213168236 -2444.21316823582 71 490243 483516.197338738 6726.80266126182 72 469357 477572.040993094 -8215.04099309435 73 477580 467120.032615487 10459.9673845126 74 528379 528490.5449912 -111.544991200011 75 533590 533798.725115565 -208.725115564656 76 517945 532018.012102321 -14073.0121023212 77 506174 509134.790481942 -2960.79048194152 78 501866 495649.062508278 6216.93749172188 79 516441 510059.086166542 6381.9138334583 80 528222 525609.441518975 2612.55848102506 81 532638 530815.607221943 1822.39277805753 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.203762359734878 0.407524719469755 0.796237640265122 22 0.124875465768409 0.249750931536818 0.875124534231591 23 0.303055482982582 0.606110965965164 0.696944517017418 24 0.192697494175068 0.385394988350136 0.807302505824932 25 0.113102661038 0.226205322076 0.886897338962 26 0.211431208849690 0.422862417699381 0.78856879115031 27 0.159597495664339 0.319194991328677 0.840402504335661 28 0.110639472009085 0.221278944018171 0.889360527990915 29 0.140311103216044 0.280622206432088 0.859688896783956 30 0.105097013841228 0.210194027682457 0.894902986158772 31 0.0774852353648482 0.154970470729696 0.922514764635152 32 0.148347768418880 0.296695536837761 0.85165223158112 33 0.101123257072117 0.202246514144235 0.898876742927883 34 0.0696409101079357 0.139281820215871 0.930359089892064 35 0.0925484335516372 0.185096867103274 0.907451566448363 36 0.0604007570410404 0.120801514082081 0.93959924295896 37 0.042872336501332 0.085744673002664 0.957127663498668 38 0.0412969419690689 0.0825938839381377 0.958703058030931 39 0.0315645576489867 0.0631291152979735 0.968435442351013 40 0.0315752332020197 0.0631504664040394 0.96842476679798 41 0.0383906121790403 0.0767812243580807 0.96160938782096 42 0.0236931924605004 0.0473863849210007 0.9763068075395 43 0.0142563375203053 0.0285126750406105 0.985743662479695 44 0.0136146659872936 0.0272293319745873 0.986385334012706 45 0.00801891601436202 0.0160378320287240 0.991981083985638 46 0.00490754065428393 0.00981508130856786 0.995092459345716 47 0.00340828291830954 0.00681656583661907 0.99659171708169 48 0.0108341207943744 0.0216682415887488 0.989165879205626 49 0.00875035322047394 0.0175007064409479 0.991249646779526 50 0.00699900230303164 0.0139980046060633 0.993000997696968 51 0.00376735291961379 0.00753470583922758 0.996232647080386 52 0.178466513265474 0.356933026530948 0.821533486734526 53 0.349268066285675 0.69853613257135 0.650731933714325 54 0.282021932429079 0.564043864858158 0.717978067570921 55 0.330957692839798 0.661915385679595 0.669042307160202 56 0.277517417493085 0.55503483498617 0.722482582506915 57 0.199539550598797 0.399079101197593 0.800460449401203 58 0.127966920576221 0.255933841152441 0.87203307942378 59 0.0941438262111821 0.188287652422364 0.905856173788818 60 0.0657328746030623 0.131465749206125 0.934267125396938 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 3 0.075 NOK 5% type I error level 10 0.25 NOK 10% type I error level 15 0.375 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317300tcxpdc48q5bfocq/10trka1259317184.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317300tcxpdc48q5bfocq/10trka1259317184.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317300tcxpdc48q5bfocq/11dn81259317184.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317300tcxpdc48q5bfocq/11dn81259317184.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317300tcxpdc48q5bfocq/2mh2d1259317184.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317300tcxpdc48q5bfocq/2mh2d1259317184.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317300tcxpdc48q5bfocq/3fu741259317184.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317300tcxpdc48q5bfocq/3fu741259317184.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317300tcxpdc48q5bfocq/4k5j01259317184.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317300tcxpdc48q5bfocq/4k5j01259317184.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317300tcxpdc48q5bfocq/5xf6w1259317184.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317300tcxpdc48q5bfocq/5xf6w1259317184.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317300tcxpdc48q5bfocq/6fhs11259317184.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317300tcxpdc48q5bfocq/6fhs11259317184.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317300tcxpdc48q5bfocq/7ty6p1259317184.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317300tcxpdc48q5bfocq/7ty6p1259317184.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317300tcxpdc48q5bfocq/8f2lj1259317184.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317300tcxpdc48q5bfocq/8f2lj1259317184.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317300tcxpdc48q5bfocq/9v28e1259317184.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317300tcxpdc48q5bfocq/9v28e1259317184.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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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