*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: Sat, 19 Dec 2009 07:42:40 -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/Dec/19/t1261233814vlt02eloli0511h.htm/, Retrieved Sat, 19 Dec 2009 15:43:46 +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/2009/Dec/19/t1261233814vlt02eloli0511h.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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97.4 0 97 0 105.4 0 102.7 0 98.1 0 104.5 0 87.4 0 89.9 0 109.8 0 111.7 0 98.6 0 96.9 0 95.1 0 97 0 112.7 0 102.9 0 97.4 0 111.4 0 87.4 0 96.8 0 114.1 0 110.3 0 103.9 0 101.6 0 94.6 0 95.9 0 104.7 0 102.8 0 98.1 0 113.9 0 80.9 0 95.7 0 113.2 0 105.9 0 108.8 0 102.3 0 99 0 100.7 0 115.5 0 100.7 0 109.9 0 114.6 0 85.4 0 100.5 0 114.8 0 116.5 0 112.9 0 102 0 106 0 105.3 0 118.8 0 106.1 0 109.3 0 117.2 0 92.5 0 104.2 0 112.5 0 122.4 0 113.3 0 100 0 110.7 0 112.8 0 109.8 0 117.3 0 109.1 0 115.9 0 96 0 99.8 0 116.8 0 115.7 1 99.4 1 94.3 1 91 1 93.2 1 103.1 1 94.1 1 91.8 1 102.7 1 82.6 1 89.1 1 104.5 1

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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Industriële_Productie[t] = + 94.3657986111111 -15.3487760416667Dummy_Crisis[t] + 0.149909784226187M1[t] + 1.12350508432540M2[t] + 10.6685289558532M3[t] + 4.28498139880953M4[t] + 2.25857669890873M5[t] + 11.5750291418651M6[t] -12.6085184151786M7[t] -3.6777802579365M8[t] + 11.8101007564484M9[t] + 14.6004284474206M10[t] + 6.81688089037698M11[t] + 0.183547557043651t + 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) 94.3657986111111 1.710317 55.1744 0 0 Dummy_Crisis -15.3487760416667 1.46798 -10.4557 0 0 M1 0.149909784226187 2.052979 0.073 0.942007 0.471004 M2 1.12350508432540 2.052114 0.5475 0.585864 0.292932 M3 10.6685289558532 2.051494 5.2004 2e-06 1e-06 M4 4.28498139880953 2.051118 2.0891 0.040501 0.02025 M5 2.25857669890873 2.050987 1.1012 0.274744 0.137372 M6 11.5750291418651 2.051101 5.6433 0 0 M7 -12.6085184151786 2.05146 -6.1461 0 0 M8 -3.6777802579365 2.052063 -1.7922 0.07761 0.038805 M9 11.8101007564484 2.05291 5.7529 0 0 M10 14.6004284474206 2.128691 6.8589 0 0 M11 6.81688089037698 2.128337 3.2029 0.002083 0.001041 t 0.183547557043651 0.022405 8.1922 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.931335595209805 R-squared 0.867385990904802 Adjusted R-squared 0.841654914513196 F-TEST (value) 33.7096659970189 F-TEST (DF numerator) 13 F-TEST (DF denominator) 67 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.68618399710860 Sum Squared Residuals 910.39281485615 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 97.4 94.699255952381 2.70074404761903 2 97 95.8563988095238 1.14360119047618 3 105.4 105.584970238095 -0.184970238095229 4 102.7 99.3849702380952 3.31502976190477 5 98.1 97.5421130952381 0.557886904761897 6 104.5 107.042113095238 -2.54211309523811 7 87.4 83.042113095238 4.35788690476192 8 89.9 92.1563988095238 -2.25639880952378 9 109.8 107.827827380952 1.9721726190476 10 111.7 110.801702628968 0.898297371031753 11 98.6 103.201702628968 -4.60170262896828 12 96.9 96.568369295635 0.331630704365086 13 95.1 96.9018266369048 -1.80182663690476 14 97 98.0589694940476 -1.05896949404762 15 112.7 107.787540922619 4.91245907738096 16 102.9 101.587540922619 1.31245907738096 17 97.4 99.7446837797619 -2.3446837797619 18 111.4 109.244683779762 2.1553162202381 19 87.4 85.2446837797619 2.15531622023809 20 96.8 94.3589694940476 2.44103050595237 21 114.1 110.030398065476 4.06960193452381 22 110.3 113.004273313492 -2.70427331349207 23 103.9 105.404273313492 -1.50427331349206 24 101.6 98.7709399801587 2.82906001984126 25 94.6 99.1043973214286 -4.50439732142857 26 95.9 100.261540178571 -4.36154017857142 27 104.7 109.990111607143 -5.29011160714285 28 102.8 103.790111607143 -0.99011160714286 29 98.1 101.947254464286 -3.84725446428572 30 113.9 111.447254464286 2.45274553571429 31 80.9 87.4472544642857 -6.54725446428571 32 95.7 96.5615401785714 -0.861540178571431 33 113.2 112.23296875 0.967031250000007 34 105.9 115.206843998016 -9.30684399801587 35 108.8 107.606843998016 1.19315600198413 36 102.3 100.973510664683 1.32648933531746 37 99 101.306968005952 -2.30696800595238 38 100.7 102.464110863095 -1.76411086309523 39 115.5 112.192682291667 3.30731770833333 40 100.7 105.992682291667 -5.29268229166666 41 109.9 104.149825148810 5.75017485119048 42 114.6 113.649825148810 0.950174851190468 43 85.4 89.6498251488095 -4.24982514880952 44 100.5 98.7641108630952 1.73588913690476 45 114.8 114.435539434524 0.364460565476192 46 116.5 117.409414682540 -0.909414682539687 47 112.9 109.809414682540 3.09058531746033 48 102 103.176081349206 -1.17608134920635 49 106 103.509538690476 2.49046130952381 50 105.3 104.666681547619 0.633318452380951 51 118.8 114.395252976190 4.40474702380952 52 106.1 108.195252976190 -2.09525297619048 53 109.3 106.352395833333 2.94760416666667 54 117.2 115.852395833333 1.34760416666667 55 92.5 91.8523958333333 0.647604166666663 56 104.2 100.966681547619 3.23331845238095 57 112.5 116.638110119048 -4.13811011904761 58 122.4 119.611985367063 2.78801463293651 59 113.3 112.011985367063 1.28801463293651 60 100 105.378652033730 -5.37865203373016 61 110.7 105.712109375 4.98789062500001 62 112.8 106.869252232143 5.93074776785714 63 109.8 116.597823660714 -6.79782366071429 64 117.3 110.397823660714 6.90217633928571 65 109.1 108.554966517857 0.545033482142853 66 115.9 118.054966517857 -2.15496651785714 67 96 94.0549665178572 1.94503348214285 68 99.8 103.169252232143 -3.36925223214286 69 116.8 118.840680803571 -2.04068080357143 70 115.7 106.465780009921 9.23421999007937 71 99.4 98.8657800099206 0.534219990079373 72 94.3 92.2324466765873 2.06755332341270 73 91 92.5659040178571 -1.56590401785714 74 93.2 93.723046875 -0.523046874999994 75 103.1 103.451618303571 -0.351618303571436 76 94.1 97.2516183035714 -3.15161830357143 77 91.8 95.4087611607143 -3.60876116071429 78 102.7 104.908761160714 -2.20876116071428 79 82.6 80.9087611607143 1.69123883928570 80 89.1 90.023046875 -0.923046875000009 81 104.5 105.694475446429 -1.19447544642857 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.379064788486793 0.758129576973586 0.620935211513207 18 0.392628845531040 0.785257691062079 0.60737115446896 19 0.275700798030048 0.551401596060097 0.724299201969952 20 0.272366011365791 0.544732022731581 0.727633988634209 21 0.210987052446553 0.421974104893105 0.789012947553447 22 0.173544380344119 0.347088760688237 0.826455619655881 23 0.130652504575180 0.261305009150360 0.86934749542482 24 0.0978956993873596 0.195791398774719 0.90210430061264 25 0.119383211776915 0.238766423553831 0.880616788223085 26 0.108319030912723 0.216638061825446 0.891680969087277 27 0.155679997533555 0.31135999506711 0.844320002466445 28 0.108429933248460 0.216859866496920 0.89157006675154 29 0.0781585593886181 0.156317118777236 0.921841440611382 30 0.0861373061227901 0.172274612245580 0.91386269387721 31 0.170807071843871 0.341614143687743 0.829192928156129 32 0.125609300845188 0.251218601690376 0.874390699154812 33 0.0932353164164599 0.186470632832920 0.90676468358354 34 0.249306048283259 0.498612096566517 0.750693951716741 35 0.298444552670066 0.596889105340132 0.701555447329934 36 0.250564430077454 0.501128860154909 0.749435569922546 37 0.2229826063834 0.4459652127668 0.7770173936166 38 0.202148845439107 0.404297690878214 0.797851154560893 39 0.233303020395262 0.466606040790524 0.766696979604738 40 0.284102045053861 0.568204090107721 0.71589795494614 41 0.469437062938766 0.93887412587753 0.530562937061234 42 0.403231158656392 0.806462317312783 0.596768841343609 43 0.441000429011185 0.882000858022371 0.558999570988814 44 0.389691736036539 0.779383472073078 0.610308263963461 45 0.32249455982074 0.64498911964148 0.67750544017926 46 0.418970129437623 0.837940258875246 0.581029870562377 47 0.404418165077327 0.808836330154655 0.595581834922673 48 0.334559726044629 0.669119452089259 0.665440273955371 49 0.304400379767875 0.60880075953575 0.695599620232125 50 0.276591417413261 0.553182834826523 0.723408582586739 51 0.328524664946225 0.657049329892449 0.671475335053775 52 0.329907809958988 0.659815619917976 0.670092190041012 53 0.289393583705149 0.578787167410299 0.71060641629485 54 0.234113387246656 0.468226774493312 0.765886612753344 55 0.182787867240605 0.36557573448121 0.817212132759395 56 0.197187715236735 0.394375430473471 0.802812284763264 57 0.167153342990854 0.334306685981709 0.832846657009146 58 0.221009413094185 0.442018826188370 0.778990586905815 59 0.153411803786598 0.306823607573196 0.846588196213402 60 0.279313527658527 0.558627055317055 0.720686472341473 61 0.275290693603107 0.550581387206215 0.724709306396893 62 0.291080103440991 0.582160206881982 0.70891989655901 63 0.447980327904201 0.895960655808401 0.552019672095799 64 0.843893152034388 0.312213695931225 0.156106847965612 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/2009/Dec/19/t1261233814vlt02eloli0511h/10pzqx1261233755.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261233814vlt02eloli0511h/10pzqx1261233755.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261233814vlt02eloli0511h/1l8ix1261233755.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261233814vlt02eloli0511h/1l8ix1261233755.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261233814vlt02eloli0511h/2d7yh1261233755.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261233814vlt02eloli0511h/2d7yh1261233755.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261233814vlt02eloli0511h/32u6j1261233755.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261233814vlt02eloli0511h/32u6j1261233755.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261233814vlt02eloli0511h/48ti31261233755.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261233814vlt02eloli0511h/48ti31261233755.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261233814vlt02eloli0511h/5k7sf1261233755.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261233814vlt02eloli0511h/5k7sf1261233755.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261233814vlt02eloli0511h/605ag1261233755.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261233814vlt02eloli0511h/605ag1261233755.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261233814vlt02eloli0511h/70re21261233755.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261233814vlt02eloli0511h/70re21261233755.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261233814vlt02eloli0511h/8ythe1261233755.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261233814vlt02eloli0511h/8ythe1261233755.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261233814vlt02eloli0511h/9g30a1261233755.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261233814vlt02eloli0511h/9g30a1261233755.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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