Paper

*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: Wed, 01 Dec 2010 15:17:25 +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/01/t1291218515d4u5j0dyw3bbqe3.htm/, Retrieved Wed, 01 Dec 2010 16:48:46 +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/01/t1291218515d4u5j0dyw3bbqe3.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 «

1 162556 807 213118 6282154 1 29790 444 81767 4321023 1 87550 412 153198 4111912 0 84738 428 -26007 223193 1 54660 315 126942 1491348 1 42634 168 157214 1629616 0 40949 263 129352 1398893 1 45187 267 234817 1926517 1 37704 228 60448 983660 1 16275 129 47818 1443586 0 25830 104 245546 1073089 0 12679 122 48020 984885 1 18014 393 -1710 1405225 0 43556 190 32648 227132 1 24811 280 95350 929118 0 6575 63 151352 1071292 0 7123 102 288170 638830 1 21950 265 114337 856956 1 37597 234 37884 992426 0 17821 277 122844 444477 1 12988 73 82340 857217 1 22330 67 79801 711969 0 13326 103 165548 702380 0 16189 290 116384 358589 0 7146 83 134028 297978 0 15824 56 63838 585715 1 27664 236 74996 657954 0 11920 73 31080 209458 0 8568 34 32168 786690 0 14416 139 49857 439798 1 3369 26 87161 688779 1 11819 70 106113 574339 1 6984 40 80570 741409 1 4519 42 102129 597793 0 2220 12 301670 644190 0 18562 211 102313 377934 0 10327 74 88577 640273 1 5336 80 112477 697458 1 2365 83 191778 550608 0 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 9 seconds R Server 'George Udny Yule' @ 72.249.76.132 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation Wealth [t] = -119201.506007196 + 194798.386004110Group[t] + 19.0472970135833Costs[t] + 1974.32364909872Orders[t] + 2.42321936149088Dividends[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) -119201.506007196 114732.051052 -1.039 0.301463 0.150731 Group 194798.386004110 97817.177056 1.9915 0.049303 0.024651 Costs 19.0472970135833 4.504053 4.2289 5.4e-05 2.7e-05 Orders 1974.32364909872 800.974594 2.4649 0.015501 0.007751 Dividends 2.42321936149088 0.902329 2.6855 0.008547 0.004274 Multiple Linear Regression - Regression Statistics Multiple R 0.831262431338051 R-squared 0.690997229754048 Adjusted R-squared 0.677986586796324 F-TEST (value) 53.1101523575212 F-TEST (DF numerator) 4 F-TEST (DF denominator) 95 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 474667.31148489 Sum Squared Residuals 21404360376267.9 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 6282154 5281560.14204184 1000593.85795816 2 4321023 1717754.93576242 2603268.06423758 3 4111912 2927841.43670649 1184070.56329351 4 223193 2276818.20420978 -2053625.20420978 5 1491348 2046242.39641185 -554894.396411852 6 1629616 1600309.72262004 29306.2773799605 7 1398893 1493461.64996256 -94568.6499625583 8 1926517 2032444.60526627 -105927.605266267 9 983660 1390380.72255497 -406720.722554969 10 1443586 756152.893054489 687433.106945511 11 1073089 1173131.65669657 -100042.656696568 12 984885 479529.651756862 505355.348243138 13 1405225 1190480.37738725 214744.622612750 14 227132 1164657.32175915 -937525.32175915 15 929118 1332043.95406673 -402925.954066727 16 1071292 497175.9585507 574116.0414493 17 638830 916152.526229454 -277322.526229454 18 856956 1293944.44859101 -436988.448591012 19 992426 1345511.08199643 -353085.081996428 20 444477 1064805.98411520 -620328.984115202 21 857217 666636.682218701 190580.317781299 22 711969 826578.035066179 -114609.035066179 23 702380 739137.228709075 -36757.2287090749 24 358589 1043733.00575209 -685144.005752086 25 297978 505558.585908963 -207580.585908963 26 585715 447458.523884129 138256.476115871 27 657954 1250193.44500235 -592239.445002352 28 209458 327281.558534059 -117823.558534059 29 786690 189072.85929498 597617.14070502 30 439798 550629.762671193 -110831.762671193 31 688779 402309.861279151 286469.138720849 32 574339 696054.614943248 -121715.614943248 33 741409 482834.93225905 258574.06774095 34 597793 492074.178633146 105718.821366854 35 644190 677787.961933097 -33597.9619330973 36 377934 898863.553650982 -520929.553650982 37 640273 438241.381668161 202031.618331839 38 697458 607735.592911703 89722.4070882974 39 550608 749232.763017231 -198624.763017231 40 207393 410320.941497423 -202927.941497423 41 301607 756963.156532259 -455356.156532259 42 345783 486366.09975174 -140583.09975174 43 501749 370026.949269874 131722.050730126 44 379983 470151.597662202 -90168.5976622022 45 387475 213535.434707864 173939.565292136 46 377305 441884.795152372 -64579.7951523716 47 370837 775751.5230066 -404914.5230066 48 430866 844387.670694154 -413521.670694154 49 469107 384098.972857069 85008.0271429313 50 194493 190391.018860862 4101.9811391377 51 530670 575410.940639718 -44740.9406397179 52 518365 749701.796815861 -231336.796815861 53 491303 863546.543166312 -372243.543166312 54 527021 575892.346863288 -48871.3468632883 55 233773 768887.623247804 -535114.623247804 56 405972 226889.832442529 179082.167557471 57 652925 86042.7999453715 566882.200054629 58 446211 288459.540150379 157751.459849621 59 341340 247048.447814658 94291.5521853418 60 387699 632903.942778169 -245204.942778169 61 493408 495133.867642223 -1725.86764222317 62 146494 119264.251802016 27229.748197984 63 414462 413826.809453132 635.190546868282 64 364304 571370.269568106 -207066.269568106 65 355178 181374.627612576 173803.372387424 66 357760 848886.148622456 -491126.148622456 67 261216 122949.285535447 138266.714464553 68 397144 444918.179265144 -47774.1792651437 69 374943 369770.30112559 5172.69887440978 70 424898 563263.063074457 -138365.063074457 71 202055 296564.343358659 -94509.3433586588 72 378525 147925.966724330 230599.033275670 73 310768 236162.305190649 74605.6948093514 74 325738 101244.556456021 224493.443543979 75 394510 273618.650577384 120891.349422616 76 247060 336799.388704870 -89739.3887048705 77 368078 235437.784073997 132640.215926003 78 236761 97298.9457546389 139462.054245361 79 312378 176172.796869287 136205.203130713 80 339836 399571.030165814 -59735.0301658135 81 347385 112111.912797848 235273.087202152 82 426280 513393.633240494 -87113.6332404941 83 352850 299393.824482709 53456.1755172911 84 301881 21937.9156974166 279943.084302583 85 377516 169128.868303416 208387.131696584 86 357312 473933.291299756 -116621.291299756 87 458343 366325.740633035 92017.2593669652 88 354228 168668.126639013 185559.873360987 89 308636 267965.173981661 40670.826018339 90 386212 169666.447400162 216545.552599838 91 393343 221528.819894352 171814.180105648 92 378509 411632.459579599 -33123.4595795993 93 452469 212065.851158408 240403.148841592 94 364839 564819.026377326 -199980.026377326 95 358649 92610.9079413748 266038.092058625 96 376641 339007.778718114 37633.2212818855 97 429112 177559.943374048 251552.056625952 98 330546 316036.105921169 14509.8940788306 99 403560 282505.680572930 121054.319427070 100 317892 297138.749476313 20753.2505236870 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.999999999998406 3.18829107649308e-12 1.59414553824654e-12 9 0.999999999992773 1.44531628744812e-11 7.22658143724062e-12 10 0.999999999999993 1.42280010290397e-14 7.11400051451986e-15 11 0.999999999999998 4.73600824587067e-15 2.36800412293533e-15 12 1 4.46591172946131e-18 2.23295586473066e-18 13 1 4.75278995391256e-24 2.37639497695628e-24 14 1 1.07046487876317e-26 5.35232439381584e-27 15 1 1.13561589046310e-28 5.67807945231552e-29 16 1 4.88833491812956e-32 2.44416745906478e-32 17 1 1.15931893957694e-32 5.79659469788472e-33 18 1 4.43186281324715e-34 2.21593140662358e-34 19 1 1.77715569973115e-33 8.88577849865577e-34 20 1 1.90818990315379e-33 9.54094951576893e-34 21 1 7.04953882844098e-34 3.52476941422049e-34 22 1 5.05774018357252e-33 2.52887009178626e-33 23 1 1.50671116021554e-32 7.53355580107772e-33 24 1 2.86569141579555e-32 1.43284570789778e-32 25 1 9.71127629275803e-32 4.85563814637901e-32 26 1 1.10794916353421e-31 5.53974581767107e-32 27 1 1.86499998453973e-31 9.32499992269863e-32 28 1 1.10219374879210e-31 5.51096874396051e-32 29 1 5.24054633751549e-34 2.62027316875775e-34 30 1 3.52396343131097e-33 1.76198171565549e-33 31 1 1.60792332344183e-33 8.03961661720916e-34 32 1 8.84243565808334e-33 4.42121782904167e-33 33 1 1.01690004274575e-33 5.08450021372874e-34 34 1 1.57270145863342e-33 7.8635072931671e-34 35 1 7.8664852563727e-33 3.93324262818635e-33 36 1 3.23813939561939e-32 1.61906969780970e-32 37 1 1.22886568270673e-32 6.14432841353364e-33 38 1 8.36090404001015e-34 4.18045202000507e-34 39 1 1.84208215700095e-33 9.21041078500475e-34 40 1 3.68835128190077e-33 1.84417564095038e-33 41 1 9.32589778354368e-33 4.66294889177184e-33 42 1 4.38853391827711e-32 2.19426695913856e-32 43 1 1.18274732226267e-31 5.91373661131333e-32 44 1 8.48169497479217e-31 4.24084748739608e-31 45 1 4.83121260843323e-30 2.41560630421661e-30 46 1 3.51285425746296e-29 1.75642712873148e-29 47 1 8.41041548812615e-29 4.20520774406307e-29 48 1 3.98887252464113e-28 1.99443626232057e-28 49 1 2.18476482234208e-27 1.09238241117104e-27 50 1 2.99714072711949e-27 1.49857036355974e-27 51 1 4.40048823674942e-27 2.20024411837471e-27 52 1 5.56611720948336e-27 2.78305860474168e-27 53 1 2.92882427012348e-26 1.46441213506174e-26 54 1 1.16603787556264e-26 5.83018937781318e-27 55 1 2.09424360969568e-26 1.04712180484784e-26 56 1 8.77088089707743e-26 4.38544044853872e-26 57 1 1.23458063407922e-28 6.17290317039612e-29 58 1 5.90685703280281e-28 2.95342851640141e-28 59 1 5.42462777028401e-27 2.71231388514201e-27 60 1 4.84999680777e-26 2.424998403885e-26 61 1 8.77362197767456e-26 4.38681098883728e-26 62 1 8.07108600144844e-27 4.03554300072422e-27 63 1 4.90433627602499e-26 2.45216813801250e-26 64 1 5.17198941052967e-25 2.58599470526483e-25 65 1 4.95125313348991e-24 2.47562656674495e-24 66 1 1.39470932140279e-24 6.97354660701397e-25 67 1 5.91570800418672e-24 2.95785400209336e-24 68 1 4.65776116717963e-23 2.32888058358981e-23 69 1 3.14225547199888e-22 1.57112773599944e-22 70 1 3.30844597423774e-21 1.65422298711887e-21 71 1 2.15631696843964e-21 1.07815848421982e-21 72 1 2.19457596550924e-20 1.09728798275462e-20 73 1 9.68169476294826e-20 4.84084738147413e-20 74 1 1.12144845385625e-18 5.60724226928127e-19 75 1 1.28950710990591e-17 6.44753554952953e-18 76 1 2.91054221552848e-17 1.45527110776424e-17 77 1 3.43368413217738e-16 1.71684206608869e-16 78 1 3.28279801628071e-16 1.64139900814035e-16 79 1 8.65389475597113e-16 4.32694737798556e-16 80 0.999999999999994 1.09763365538639e-14 5.48816827693194e-15 81 0.99999999999993 1.39590094605682e-13 6.9795047302841e-14 82 0.999999999999314 1.37186440135981e-12 6.85932200679905e-13 83 0.999999999996697 6.60545958676148e-12 3.30272979338074e-12 84 0.999999999971262 5.74762844046524e-11 2.87381422023262e-11 85 0.99999999962085 7.58300593938408e-10 3.79150296969204e-10 86 0.999999995732633 8.53473329433435e-09 4.26736664716717e-09 87 0.999999951622732 9.67545350931405e-08 4.83772675465702e-08 88 0.999999514542048 9.70915904511296e-07 4.85457952255648e-07 89 0.999999924540824 1.50918351609489e-07 7.54591758047445e-08 90 0.99999850956829 2.98086342167626e-06 1.49043171083813e-06 91 0.999979737613482 4.05247730350019e-05 2.02623865175010e-05 92 0.999623619102223 0.000752761795553108 0.000376380897776554 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 85 1 NOK 5% type I error level 85 1 NOK 10% type I error level 85 1 NOK

Charts produced by software:

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Parameters (Session):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 5 ; par2 = Do not include Seasonal 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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