multiple regression incl lineaire trend

*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, 17 Dec 2010 16:58:50 +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/17/t1292605035gvsrc82er52kyn4.htm/, Retrieved Fri, 17 Dec 2010 17:57:15 +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/17/t1292605035gvsrc82er52kyn4.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:

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14544.5 94.6 -3.0 14097.8 15116.3 95.9 -3.7 14776.8 17413.2 104.7 -4.7 16833.3 16181.5 102.8 -6.4 15385.5 15607.4 98.1 -7.5 15172.6 17160.9 113.9 -7.8 16858.9 14915.8 80.9 -7.7 14143.5 13768 95.7 -6.6 14731.8 17487.5 113.2 -4.2 16471.6 16198.1 105.9 -2.0 15214 17535.2 108.8 -0.7 17637.4 16571.8 102.3 0.1 17972.4 16198.9 99 0.9 16896.2 16554.2 100.7 2.1 16698 19554.2 115.5 3.5 19691.6 15903.8 100.7 4.9 15930.7 18003.8 109.9 5.7 17444.6 18329.6 114.6 6.2 17699.4 16260.7 85.4 6.5 15189.8 14851.9 100.5 6.5 15672.7 18174.1 114.8 6.3 17180.8 18406.6 116.5 6.2 17664.9 18466.5 112.9 6.4 17862.9 16016.5 102 6.3 16162.3 17428.5 106 5.8 17463.6 17167.2 105.3 5.1 16772.1 19630 118.8 5.1 19106.9 17183.6 106.1 5.8 16721.3 18344.7 109.3 6.7 18161.3 19301.4 117.2 7.1 18509.9 18147.5 92.5 6.7 17802.7 16192.9 104.2 5.5 16409.9 18374.4 112.5 4.2 17967.7 20515.2 122.4 3.0 20286.6 18957.2 113.3 2.2 19537.3 16471.5 100 2.0 18021.9 18746.8 110.7 1.8 20194.3 19009.5 112.8 1.8 19049.6 19211.2 109 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 6 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation productie[t] = + 30.8115915919024 + 0.00348754152529101uitvoer[t] -0.167649538311852ondernemersvertrouwen[t] + 0.00103653952403590invoer[t] -0.159945387828218t + 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) 30.8115915919024 8.030548 3.8368 0.000291 0.000146 uitvoer 0.00348754152529101 0.001118 3.1203 0.002726 0.001363 ondernemersvertrouwen -0.167649538311852 0.097799 -1.7142 0.091405 0.045702 invoer 0.00103653952403590 0.001076 0.9632 0.339122 0.169561 t -0.159945387828218 0.046721 -3.4234 0.001092 0.000546 Multiple Linear Regression - Regression Statistics Multiple R 0.81693184425255 R-squared 0.667377638153872 Adjusted R-squared 0.646258758036657 F-TEST (value) 31.6009956233369 F-TEST (DF numerator) 4 F-TEST (DF denominator) 63 p-value 1.92068583260152e-14 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5.9179008697521 Sum Squared Residuals 2206.3576943654 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 94.6 96.4920694355582 -1.89206943555816 2 95.9 99.14746530553 -3.24746530553006 3 104.7 109.297347116634 -4.59734711663445 4 102.8 103.626099124336 -0.826099124336263 5 98.1 101.427691374314 -3.32769137431427 6 113.9 108.483853206901 5.41614679309907 7 80.9 97.6626439632436 -16.7626439632436 8 95.7 93.9250801225336 1.77491987746637 9 113.2 108.138058009995 5.06194199000545 10 105.9 101.808895489742 4.09110451025753 11 108.8 108.606147358124 0.193852641875925 12 102.3 105.299425574733 -2.99942557473304 13 99 102.589332485707 -3.58933248570688 14 100.7 103.261889022176 -2.56188902217642 15 115.5 116.432843575739 -0.93284357573852 16 100.7 99.4089457544048 1.29105424559523 17 109.9 108.007935124476 1.89206487552386 18 114.6 109.164516267156 5.43548373284383 19 85.4 99.1376017666393 -13.7376017666393 20 100.5 94.564952814138 5.93504718586194 21 114.8 107.588053045493 7.21194695450745 22 116.5 108.757514799711 7.74248520028854 23 112.9 108.978178067345 3.92182193265509 24 102 98.5277817818095 3.47221821819055 25 106 104.724918679476 1.27508132052402 26 105.3 103.054266287037 2.24573371296330 27 118.8 113.903550648414 4.89644935158579 28 106.1 102.621560307756 3.47843969224429 29 109.3 107.852731715074 1.44726828492608 30 117.2 111.323595167246 5.87640483275421 31 92.5 106.473394677311 -13.9733946773108 32 104.2 98.2541878210458 5.9458121789542 33 112.5 107.534979940988 4.96502005901153 34 122.4 117.445974398764 4.95402560123569 35 113.3 111.209879879822 2.09012012017791 36 100 100.843710435516 -0.843710435516367 37 110.7 110.904276649861 -0.204276649860744 38 112.8 110.473981627563 2.32601837243742 39 109.8 112.306536612054 -2.50653661205443 40 117.3 118.265597424151 -0.965597424151192 41 109.1 112.196709264968 -3.09670926496788 42 115.9 118.063900325064 -2.16390032506397 43 96 115.215218868311 -19.2152188683109 44 99.8 99.6742061521052 0.125793847894769 45 116.8 118.078596451203 -1.27859645120323 46 115.7 115.79216715038 -0.0921671503800888 47 99.4 99.213524066181 0.186475933818985 48 94.3 94.1382051557316 0.161794844268383 49 91 91.8141308632122 -0.814130863212235 50 93.2 92.9304124692956 0.269587530704357 51 103.1 97.402680487328 5.69731951267202 52 94.1 92.6148231549179 1.48517684508214 53 91.8 89.323269614453 2.47673038554706 54 102.7 97.002828636947 5.69717136305295 55 82.6 91.8293165268406 -9.22931652684064 56 89.1 82.8161166184192 6.28388338158083 57 104.5 97.7228462343832 6.77715376561684 58 105.1 97.2626419480767 7.83735805192327 59 95.1 95.4591140267216 -0.359114026721552 60 88.7 95.150416094328 -6.45041609432809 61 86.3 91.7328949952635 -5.43289499526352 62 91.8 93.120051539462 -1.32005153946191 63 111.5 106.781935590881 4.71806440911909 64 99.7 99.0309921008435 0.669007899156513 65 97.5 98.4989047590185 -0.998904759018524 66 111.7 110.809520562037 0.890479437962851 67 86.2 100.614043363794 -14.4140433637936 68 95.4 93.7830701213147 1.61692987868527 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.348218729852698 0.696437459705396 0.651781270147302 9 0.675071446534881 0.649857106930237 0.324928553465118 10 0.542001765669967 0.915996468660066 0.457998234330033 11 0.763662810268272 0.472674379463455 0.236337189731728 12 0.777099240682483 0.445801518635033 0.222900759317516 13 0.722014996314945 0.55597000737011 0.277985003685055 14 0.644666301711256 0.710667396577487 0.355333698288744 15 0.576198285991203 0.847603428017593 0.423801714008797 16 0.505933204214819 0.988133591570362 0.494066795785181 17 0.412477451231433 0.824954902462865 0.587522548768567 18 0.350899868105889 0.701799736211778 0.649100131894111 19 0.695927849037424 0.608144301925152 0.304072150962576 20 0.739663190982875 0.52067361803425 0.260336809017125 21 0.740072359877407 0.519855280245185 0.259927640122593 22 0.719366333988926 0.561267332022149 0.280633666011074 23 0.649789527576373 0.700420944847254 0.350210472423627 24 0.577149937066743 0.845700125866514 0.422850062933257 25 0.507933113102185 0.98413377379563 0.492066886897815 26 0.430093812642165 0.86018762528433 0.569906187357835 27 0.370595403263229 0.741190806526458 0.629404596736771 28 0.304217795959067 0.608435591918134 0.695782204040933 29 0.253416797823707 0.506833595647414 0.746583202176293 30 0.228728977668315 0.457457955336629 0.771271022331685 31 0.670437828164425 0.65912434367115 0.329562171835575 32 0.651632963688255 0.69673407262349 0.348367036311745 33 0.618552099385639 0.762895801228722 0.381447900614361 34 0.617943668086417 0.764112663827166 0.382056331913583 35 0.583319499832852 0.833361000334296 0.416680500167148 36 0.517169346635433 0.965661306729133 0.482830653364567 37 0.459101540482201 0.918203080964401 0.540898459517799 38 0.497591349098721 0.995182698197442 0.502408650901279 39 0.459294527652185 0.91858905530437 0.540705472347815 40 0.422077626103163 0.844155252206326 0.577922373896837 41 0.410754130102331 0.821508260204663 0.589245869897669 42 0.419568081202301 0.839136162404603 0.580431918797699 43 0.721406849038762 0.557186301922477 0.278593150961239 44 0.688065640697869 0.623868718604263 0.311934359302131 45 0.624441301895924 0.751117396208151 0.375558698104076 46 0.578513247696227 0.842973504607546 0.421486752303773 47 0.551090648317052 0.897818703365896 0.448909351682948 48 0.537760101607639 0.924479796784721 0.462239898392361 49 0.590958498189072 0.818083003621855 0.409041501810928 50 0.557609741082351 0.884780517835297 0.442390258917649 51 0.528942332745132 0.942115334509736 0.471057667254868 52 0.460145740963246 0.920291481926491 0.539854259036755 53 0.368525402099397 0.737050804198793 0.631474597900603 54 0.287397106153928 0.574794212307857 0.712602893846072 55 0.463529732044378 0.927059464088756 0.536470267955622 56 0.374063606662936 0.748127213325873 0.625936393337064 57 0.292748526027326 0.585497052054652 0.707251473972674 58 0.357331467398289 0.714662934796578 0.642668532601711 59 0.432102288710193 0.864204577420385 0.567897711289807 60 0.310528715252648 0.621057430505296 0.689471284747352 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/17/t1292605035gvsrc82er52kyn4/10tqti1292605120.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292605035gvsrc82er52kyn4/10tqti1292605120.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292605035gvsrc82er52kyn4/147eo1292605120.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292605035gvsrc82er52kyn4/147eo1292605120.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292605035gvsrc82er52kyn4/2fywr1292605120.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292605035gvsrc82er52kyn4/2fywr1292605120.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292605035gvsrc82er52kyn4/3fywr1292605120.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292605035gvsrc82er52kyn4/3fywr1292605120.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292605035gvsrc82er52kyn4/48qvu1292605120.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292605035gvsrc82er52kyn4/48qvu1292605120.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292605035gvsrc82er52kyn4/58qvu1292605120.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292605035gvsrc82er52kyn4/58qvu1292605120.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292605035gvsrc82er52kyn4/68qvu1292605120.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292605035gvsrc82er52kyn4/68qvu1292605120.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292605035gvsrc82er52kyn4/71zcx1292605120.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292605035gvsrc82er52kyn4/71zcx1292605120.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292605035gvsrc82er52kyn4/81zcx1292605120.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292605035gvsrc82er52kyn4/81zcx1292605120.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292605035gvsrc82er52kyn4/9tqti1292605120.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292605035gvsrc82er52kyn4/9tqti1292605120.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Do not include Seasonal 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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