*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: Tue, 14 Dec 2010 19:23:57 +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/14/t1292354520hocwp8shch3zk24.htm/, Retrieved Tue, 14 Dec 2010 20:22:10 +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/14/t1292354520hocwp8shch3zk24.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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6.469 3 1.194 3.738 3 2.116 4.094 1 2.526 3.219 3 2.803 6.436 4 1.361 5.193 4 2.282 3.555 1 2.981 5.971 4 1.825 4.143 1 2.674 5.438 1 2.272 4.718 4 2.526 5.638 5 1.361 0.000 2 2.332 5.900 5 1.131 3.738 1 2.128 3.332 2 2.152 3.738 2 2.370 4.787 2 2.370 0.000 1 1.808 0.000 1 2.896 5.991 5 0 4.997 5 1.335 2.773 2 2.667 5.529 1 2.485 5.737 1 1.825 4.143 1 2.565 3.332 3 2.625 4.220 4 2.104 5.817 5 1.065 4.605 1 2.380 3.497 4 0 3.068 4 2.208 3.912 1 2.991 5.587 1 2.079 3.401 1 2.361 3.807 3 2.416 2.944 3 2.580 3.401 3 2.549 2.485 1 2.965 4.787 1 2.856 6.087 5 0 4.942 2 2.833 5.136 4 2.389 2.833 2 2.617 4.745 4 2.128 3.434 5 2.128 4.143 2 2.526 3.045 3 2.580 3.951 1 2.282 5.100 2 2.262 5.416 2 1.887 5.416 3 1.686 5.011 5 0.956 5.017 5 1.335 4.500 2 2.398 0.000 2 2.332 4.094 2 2.588 5.298 3 1.686 3.829 2 2.760 5.347 4 2.332 2.639 1 2.965 3.638 1 0

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 8 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 R Framework error message Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values. Multiple Linear Regression - Estimated Regression Equation slaap[t] = + 3.17334321322817 -0.108024153179539`aantal-dagen-dat-baby-in-buik-is`[t] -0.241165836249599`danger-high-voltage`[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) 3.17334321322817 0.241151 13.1592 0 0 `aantal-dagen-dat-baby-in-buik-is` -0.108024153179539 0.057239 -1.8872 0.064051 0.032025 `danger-high-voltage` -0.241165836249599 0.059729 -4.0377 0.000158 7.9e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.583537843141241 R-squared 0.340516414377931 Adjusted R-squared 0.318161038594132 F-TEST (value) 15.2319700492221 F-TEST (DF numerator) 2 F-TEST (DF denominator) 59 p-value 4.63999722555286e-06 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.618940394838914 Sum Squared Residuals 22.6021455294377 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.194 1.75103745756092 -0.557037457560918 2 2.116 2.04605141989425 0.069948580105749 3 2.526 2.48992649386153 0.0360735061384691 4 2.803 2.10211595539443 0.700884044605569 5 1.361 1.51343641836626 -0.152436418366256 6 2.282 1.64771044076842 0.634289559231577 7 2.981 2.54815151242530 0.432848487574695 8 1.825 1.56366764959474 0.261332350405259 9 2.674 2.48463331035574 0.189366689644264 10 2.272 2.34474203198823 -0.0727420319882331 11 2.526 1.69902191352870 0.826978086471296 12 1.361 1.35847385635393 0.00252614364607151 13 2.332 2.69101154072897 -0.359011540728966 14 1.131 1.33017152822089 -0.199171528220889 15 2.128 2.52838309239345 -0.400383092393449 16 2.152 2.33107506233474 -0.179075062334743 17 2.37 2.28721725614385 0.0827827438561503 18 2.37 2.17389991945851 0.196100080541486 19 1.808 2.93217737697857 -1.12417737697857 20 2.896 2.93217737697857 -0.0361773769785655 21 0 1.32034133028155 -1.32034133028155 22 1.335 1.42771733854201 -0.092717338542013 23 2.667 2.39146056396210 0.275539436037895 24 2.485 2.33491183404889 0.150088165951105 25 1.825 2.31244281018755 -0.487442810187551 26 2.565 2.48463331035574 0.0803666896442642 27 2.625 2.08990922608514 0.535090773914856 28 2.104 1.75281794181211 0.351182058187886 29 1.065 1.33913753293479 -0.274137532934791 30 2.38 2.43472615158679 -0.0547261515867888 31 0 1.83091940456092 -1.83091940456092 32 2.208 1.87726176627494 0.330738233725058 33 2.991 2.50958688974021 0.481413110259791 34 2.079 2.32864643316448 -0.249646433164481 35 2.361 2.56478723201495 -0.203787232014953 36 2.416 2.03859775332486 0.377402246675137 37 2.58 2.13182259751880 0.448177402481196 38 2.549 2.08245555951576 0.466544440484245 39 2.965 2.66373735632741 0.301262643672589 40 2.856 2.41506575570811 0.440934244291887 41 0 1.30997101157632 -1.30997101157632 42 2.833 2.15715617571569 0.675843824284315 43 2.389 1.65386781749966 0.735132182500344 44 2.617 2.38497911477133 0.232020885228667 45 2.128 1.69610526139286 0.431894738607144 46 2.128 1.59655908996163 0.531440910038368 47 2.526 2.24346747410614 0.282532525893863 48 2.58 2.12091215804767 0.459087841952329 49 2.282 2.50537394776621 -0.223373947766207 50 2.262 2.14008835951332 0.121911640486682 51 1.887 2.10595272710858 -0.218952727108584 52 1.686 1.86478689085898 -0.178786890858984 53 0.956 1.4262050003975 -0.470205000397499 54 1.335 1.42555685547842 -0.090556855478422 55 2.398 2.20490285142104 0.193097148578959 56 2.332 2.69101154072897 -0.359011540728966 57 2.588 2.24876065761193 0.339239342388066 58 1.686 1.87753374093417 -0.19153374093417 59 2.76 2.27738705820451 0.482612941795488 60 2.332 1.63107472117877 0.700925278821227 61 2.965 2.64710163673776 0.317898363262238 62 0 2.5391855077114 -2.5391855077114 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.175485409385978 0.350970818771956 0.824514590614022 7 0.108371790201164 0.216743580402329 0.891628209798836 8 0.0598575971759643 0.119715194351929 0.940142402824036 9 0.0276002375747361 0.0552004751494721 0.972399762425264 10 0.0130503454899462 0.0261006909798923 0.986949654510054 11 0.0122808548853073 0.0245617097706146 0.987719145114693 12 0.00864568254346702 0.0172913650869340 0.991354317456533 13 0.0828488489981015 0.165697697996203 0.917151151001899 14 0.0643675843769192 0.128735168753838 0.935632415623081 15 0.0540089954600544 0.108017990920109 0.945991004539946 16 0.0352301032637759 0.0704602065275519 0.964769896736224 17 0.0199339031443318 0.0398678062886636 0.980066096855668 18 0.0115720567889136 0.0231441135778271 0.988427943211087 19 0.0474852729596224 0.0949705459192448 0.952514727040378 20 0.0327826724836874 0.0655653449673749 0.967217327516313 21 0.196725938877762 0.393451877755523 0.803274061122238 22 0.144101778843321 0.288203557686642 0.85589822115668 23 0.113012887630123 0.226025775260246 0.886987112369877 24 0.0795517648918625 0.159103529783725 0.920448235108137 25 0.0717439340485376 0.143487868097075 0.928256065951462 26 0.0485758511228021 0.0971517022456042 0.951424148877198 27 0.0459268766028926 0.0918537532057853 0.954073123397107 28 0.0346909934291146 0.0693819868582292 0.965309006570885 29 0.0243699418039446 0.0487398836078893 0.975630058196055 30 0.0151724075730922 0.0303448151461844 0.984827592426908 31 0.239602758715941 0.479205517431882 0.760397241284059 32 0.203867294531201 0.407734589062402 0.796132705468799 33 0.182371949358405 0.36474389871681 0.817628050641595 34 0.142474727032918 0.284949454065835 0.857525272967082 35 0.106963233255355 0.213926466510709 0.893036766744645 36 0.0859633971985239 0.171926794397048 0.914036602801476 37 0.071159030294112 0.142318060588224 0.928840969705888 38 0.0589256345418056 0.117851269083611 0.941074365458194 39 0.0429913069820912 0.0859826139641824 0.95700869301791 40 0.035357425028751 0.070714850057502 0.96464257497125 41 0.142699384716878 0.285398769433755 0.857300615283123 42 0.154037095776062 0.308074191552124 0.845962904223938 43 0.155493296517905 0.31098659303581 0.844506703482095 44 0.119257394830840 0.238514789661679 0.88074260516916 45 0.0924052496900713 0.184810499380143 0.907594750309929 46 0.0725009742152554 0.145001948430511 0.927499025784745 47 0.0540847327477676 0.108169465495535 0.945915267252232 48 0.0441520517692663 0.0883041035385326 0.955847948230734 49 0.0276519904301458 0.0553039808602915 0.972348009569854 50 0.0171928285463753 0.0343856570927506 0.982807171453625 51 0.00941648338389326 0.0188329667677865 0.990583516616107 52 0.00474501928183965 0.0094900385636793 0.99525498071816 53 0.00435584399836720 0.00871168799673439 0.995644156001633 54 0.00509521445501176 0.0101904289100235 0.994904785544988 55 0.00272629450944733 0.00545258901889467 0.997273705490553 56 0.064532639380982 0.129065278761964 0.935467360619018 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 3 0.0588235294117647 NOK 5% type I error level 13 0.254901960784314 NOK 10% type I error level 24 0.470588235294118 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354520hocwp8shch3zk24/10vndm1292354627.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354520hocwp8shch3zk24/10vndm1292354627.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354520hocwp8shch3zk24/1o4gs1292354627.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354520hocwp8shch3zk24/1o4gs1292354627.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354520hocwp8shch3zk24/2o4gs1292354627.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354520hocwp8shch3zk24/2o4gs1292354627.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354520hocwp8shch3zk24/3gdxv1292354627.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354520hocwp8shch3zk24/3gdxv1292354627.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354520hocwp8shch3zk24/4gdxv1292354627.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354520hocwp8shch3zk24/4gdxv1292354627.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354520hocwp8shch3zk24/5gdxv1292354627.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354520hocwp8shch3zk24/5gdxv1292354627.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354520hocwp8shch3zk24/694wg1292354627.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354520hocwp8shch3zk24/694wg1292354627.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354520hocwp8shch3zk24/7kwwj1292354627.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354520hocwp8shch3zk24/7kwwj1292354627.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354520hocwp8shch3zk24/8kwwj1292354627.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354520hocwp8shch3zk24/8kwwj1292354627.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354520hocwp8shch3zk24/9kwwj1292354627.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292354520hocwp8shch3zk24/9kwwj1292354627.ps (open in new window)

Parameters (Session):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 3 ; 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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