ws7

*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, 18 Nov 2009 11:56:17 -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/18/t1258570670fbodj79bfwsdyi6.htm/, Retrieved Wed, 18 Nov 2009 19:58:02 +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/18/t1258570670fbodj79bfwsdyi6.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:

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216234 562325 213587 560854 209465 555332 204045 543599 200237 536662 203666 542722 241476 593530 260307 610763 243324 612613 244460 611324 233575 594167 237217 595454 235243 590865 230354 589379 227184 584428 221678 573100 217142 567456 219452 569028 256446 620735 265845 628884 248624 628232 241114 612117 229245 595404 231805 597141 219277 593408 219313 590072 212610 579799 214771 574205 211142 572775 211457 572942 240048 619567 240636 625809 230580 619916 208795 587625 197922 565742 194596 557274 194581 560576 185686 548854 178106 531673 172608 525919 167302 511038 168053 498662 202300 555362 202388 564591 182516 541657 173476 527070 166444 509846 171297 514258 169701 516922 164182 507561 161914 492622 159612 490243 151001 469357 158114 477580 186530 528379 187069 533590 174330 517945 169362 506174 166827 501866 178037 516141 186412 528222 189226 532638 191563 536322 188906 536535 186005 523597 195309 536214 223532 586570 226899 596594 2141 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = -188223.253419678 + 0.702834499083682X[t] -889.536963523605M1[t] -1383.35694703016M2[t] + 793.444108625458M3[t] + 1874.13940928973M4[t] + 4422.1341500451M5[t] + 6387.43457361211M6[t] + 2806.48839924629M7[t] + 1705.05816847870M8[t] -6283.98960683445M9[t] -4151.18539806343M10[t] -1926.27254572696M11[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) -188223.253419678 14693.612347 -12.8099 0 0 X 0.702834499083682 0.025736 27.3096 0 0 M1 -889.536963523605 4514.011652 -0.1971 0.844493 0.422247 M2 -1383.35694703016 4513.588987 -0.3065 0.76037 0.380185 M3 793.444108625458 4519.910438 0.1755 0.861285 0.430643 M4 1874.13940928973 4530.978284 0.4136 0.680726 0.340363 M5 4422.1341500451 4562.466693 0.9692 0.336592 0.168296 M6 6387.43457361211 4552.796098 1.403 0.166146 0.083073 M7 2806.48839924629 4570.53159 0.614 0.541676 0.270838 M8 1705.05816847870 4614.541348 0.3695 0.713151 0.356576 M9 -6283.98960683445 4568.352169 -1.3755 0.174439 0.087219 M10 -4151.18539806343 4725.69736 -0.8784 0.383465 0.191732 M11 -1926.27254572696 4714.679495 -0.4086 0.684414 0.342207 Multiple Linear Regression - Regression Statistics Multiple R 0.97161513268981 R-squared 0.944035966071837 Adjusted R-squared 0.93204367308723 F-TEST (value) 78.7202220028835 F-TEST (DF numerator) 12 F-TEST (DF denominator) 56 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 7453.78367358868 Sum Squared Residuals 3111297898.94880 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 216234 206108.619314029 10125.3806859708 2 213587 204580.929782371 9006.0702176288 3 209465 202876.678734087 6588.32126591332 4 204045 195711.016857002 8333.98314299788 5 200237 193383.448677614 6853.55132238602 6 203666 199607.926165628 4058.0738343719 7 241476 231736.595220706 9739.404779294 8 260307 242747.111912647 17559.8880873525 9 243324 236058.307960639 7265.69203936084 10 244460 237285.158500091 7174.8414999087 11 233575 227451.539851649 6123.46014835096 12 237217 230282.360397697 6934.63960230329 13 235243 226167.515917878 9075.48408212191 14 230354 224629.283868733 5724.71613126682 15 227184 223326.351319425 3857.64868057451 16 221678 216445.337414470 5232.66258553019 17 217142 215026.534242397 2115.46575760312 18 219452 218096.690498523 1355.30950147656 19 256446 250857.207768278 5588.79223172244 20 265845 255483.175870543 10361.8241294571 21 248624 247035.880001827 1588.11999817282 22 241114 237842.506257865 3271.49374213534 23 229245 228320.946127016 924.053872984434 24 231805 231468.042197651 336.957802349125 25 219277 227954.824049048 -8677.8240490479 26 219313 225116.348176598 -5803.34817659817 27 212610 220072.930423167 -7462.93042316712 28 214771 217221.969535957 -2450.96953595728 29 211142 218764.910943023 -7622.91094302298 30 211457 220847.584727937 -9390.58472793697 31 240048 250036.297073348 -9988.29707334782 32 240636 253321.959785861 -12685.9597858606 33 230580 241191.108307447 -10611.1083074473 34 208795 220628.683706307 -11833.6837063071 35 197922 207473.469215195 -9551.4692151954 36 194596 203448.139222682 -8852.13922268173 37 194581 204879.361775132 -10298.3617751324 38 185686 196146.915793367 -10460.9157933670 39 178106 186248.317320266 -8142.31732026584 40 172608 183284.902913203 -10676.9029132026 41 167302 175374.017473094 -8072.01747309371 42 168053 168641.038136001 -588.038136001073 43 202300 204910.80805968 -2610.80805968002 44 202388 210295.837420956 -7907.83742095573 45 182516 186187.983243657 -3671.98324365742 46 173476 178068.540614295 -4592.54061429476 47 166444 168187.832054414 -1743.83205441390 48 171297 173215.010410098 -1918.01041009807 49 169701 174197.824552133 -4496.82455213339 50 164182 167124.770822705 -2942.77082270449 51 161914 158801.927296549 3112.07270345102 52 159612 158210.579323893 1401.42067610682 53 151001 146079.172716787 4921.82728321324 54 158114 153823.881226319 4290.1187736811 55 186530 185946.224770905 583.775229094972 56 187069 188507.265114863 -1438.26511486250 57 174330 169522.371601385 4807.62839861485 58 169362 163382.110921442 5979.88907855786 59 166827 162579.212751726 4247.78724827388 60 178037 174538.447771873 3498.55222812736 61 186412 182139.854391779 4272.145608221 62 189226 184749.751556226 4476.24844377402 63 191563 189515.794906506 2047.20509349412 64 188906 190746.193955475 -1840.19395547498 65 186005 184200.915947086 1804.08405291433 66 195309 195033.879245591 275.120754408500 67 223532 226844.867107084 -3312.86710708357 68 226899 232788.649895131 -5889.6498951308 69 214126 213504.348885044 621.651114956197 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0022077002780474 0.0044154005560948 0.997792299721953 17 0.000904080283198839 0.00180816056639768 0.999095919716801 18 0.000113576117158841 0.000227152234317682 0.999886423882841 19 3.23289262365322e-05 6.46578524730644e-05 0.999967671073763 20 0.00146458856717621 0.00292917713435241 0.998535411432824 21 0.00168190781829740 0.00336381563659479 0.998318092181703 22 0.00272869729774950 0.00545739459549899 0.99727130270225 23 0.00629450953856387 0.0125890190771277 0.993705490461436 24 0.0212199249866048 0.0424398499732096 0.978780075013395 25 0.502131911768906 0.995736176462188 0.497868088231094 26 0.64406378686134 0.711872426277319 0.355936213138660 27 0.73114080480528 0.537718390389441 0.268859195194721 28 0.762875603919422 0.474248792161157 0.237124396080578 29 0.748928725958211 0.502142548083577 0.251071274041789 30 0.73701876326017 0.525962473479661 0.262981236739831 31 0.83687970878837 0.326240582423261 0.163120291211630 32 0.976049208584006 0.0479015828319884 0.0239507914159942 33 0.981720612382442 0.0365587752351154 0.0182793876175577 34 0.99469068873595 0.0106186225280996 0.00530931126404982 35 0.996446311436093 0.00710737712781328 0.00355368856390664 36 0.997199726714863 0.00560054657027479 0.00280027328513740 37 0.997681633158842 0.00463673368231664 0.00231836684115832 38 0.998476019474392 0.00304796105121493 0.00152398052560747 39 0.998903209114509 0.00219358177098222 0.00109679088549111 40 0.9994237137851 0.00115257242980138 0.00057628621490069 41 0.99977572904771 0.000448541904578146 0.000224270952289073 42 0.99953358715606 0.000932825687877991 0.000466412843938996 43 0.998841859758712 0.00231628048257534 0.00115814024128767 44 0.998096634961617 0.00380673007676577 0.00190336503838288 45 0.997907357962517 0.00418528407496662 0.00209264203748331 46 0.998646532695376 0.00270693460924806 0.00135346730462403 47 0.997904461540508 0.00419107691898341 0.00209553845949170 48 0.99670898501243 0.0065820299751401 0.00329101498757005 49 0.999037749769624 0.00192450046075221 0.000962250230376107 50 0.999999652432962 6.9513407585292e-07 3.4756703792646e-07 51 0.999999982965756 3.40684874931498e-08 1.70342437465749e-08 52 0.999999298894418 1.40221116420588e-06 7.01105582102939e-07 53 0.999998885087775 2.22982445076590e-06 1.11491222538295e-06 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 26 0.68421052631579 NOK 5% type I error level 31 0.81578947368421 NOK 10% type I error level 31 0.81578947368421 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570670fbodj79bfwsdyi6/1038gm1258570572.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570670fbodj79bfwsdyi6/1038gm1258570572.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570670fbodj79bfwsdyi6/1vz381258570572.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570670fbodj79bfwsdyi6/1vz381258570572.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570670fbodj79bfwsdyi6/2ptc71258570572.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570670fbodj79bfwsdyi6/2ptc71258570572.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570670fbodj79bfwsdyi6/3ck7i1258570572.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570670fbodj79bfwsdyi6/3ck7i1258570572.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570670fbodj79bfwsdyi6/4oc271258570572.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570670fbodj79bfwsdyi6/4oc271258570572.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570670fbodj79bfwsdyi6/5yy1a1258570572.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570670fbodj79bfwsdyi6/5yy1a1258570572.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570670fbodj79bfwsdyi6/6xuzy1258570572.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570670fbodj79bfwsdyi6/6xuzy1258570572.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570670fbodj79bfwsdyi6/7blg01258570572.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570670fbodj79bfwsdyi6/7blg01258570572.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570670fbodj79bfwsdyi6/89nop1258570572.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570670fbodj79bfwsdyi6/89nop1258570572.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570670fbodj79bfwsdyi6/9f2th1258570572.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258570670fbodj79bfwsdyi6/9f2th1258570572.ps (open in new window)

Parameters (Session):

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

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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