Workshop 8 - Model 2

*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: Mon, 29 Nov 2010 20:09:40 +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/Nov/29/t1291061338ku0fed9ftvkorjt.htm/, Retrieved Mon, 29 Nov 2010 21:09:08 +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/Nov/29/t1291061338ku0fed9ftvkorjt.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 «

9700 0 9081 0 9084 0 9743 0 8587 0 9731 0 9563 0 9998 0 9437 0 10038 0 9918 0 9252 0 9737 0 9035 0 9133 0 9487 0 8700 0 9627 0 8947 0 9283 0 8829 0 9947 1 9628 1 9318 1 9605 1 8640 1 9214 1 9567 1 8547 1 9185 1 9470 1 9123 1 9278 1 10170 1 9434 1 9655 1 9429 1 8739 1 9552 1 9687 1 9019 1 9672 1 9206 1 9069 1 9788 1 10312 1 10105 1 9863 1 9656 1 9295 1 9946 1 9701 1 9049 1 10190 1 9706 1 9765 1 9893 1 9994 1 10433 1 10073 1 10112 1 9266 1 9820 1 10097 1 9115 1 10411 1 9678 1 10408 1 10153 1 10368 1 10581 1 10597 1 10680 1 9738 1 9556 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 13 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Monthly_births[t] = + 9394.56324695302 -401.305821796236Dummy[t] + 92.0419582635944M1[t] -657.549905336646M2[t] -316.284626079755M3[t] -6.62558530689533M4[t] -901.574591764287M5[t] + 47.4764017783186M6[t] -344.305938012408M7[t] -182.421611136468M8[t] -244.537284260528M9[t] + 380.064679581452M10[t] + 240.949006457393M11[t] + 17.4490064573931t + 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) 9394.56324695302 126.030594 74.5419 0 0 Dummy -401.305821796236 110.821847 -3.6212 0.000598 0.000299 M1 92.0419582635944 149.134046 0.6172 0.539416 0.269708 M2 -657.549905336646 149.133792 -4.4091 4.3e-05 2.1e-05 M3 -316.284626079755 149.168548 -2.1203 0.038054 0.019027 M4 -6.62558530689533 155.004069 -0.0427 0.966045 0.483022 M5 -901.574591764287 154.963297 -5.818 0 0 M6 47.4764017783186 154.956216 0.3064 0.760354 0.380177 M7 -344.305938012408 154.98283 -2.2216 0.030033 0.015016 M8 -182.421611136468 155.043122 -1.1766 0.243932 0.121966 M9 -244.537284260528 155.137054 -1.5763 0.120137 0.060068 M10 380.064679581452 154.587541 2.4586 0.016803 0.008401 M11 240.949006457393 154.536865 1.5592 0.12413 0.062065 t 17.4490064573931 2.285108 7.636 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.87560757853449 R-squared 0.766688631587034 Adjusted R-squared 0.716966536679353 F-TEST (value) 15.4194756478089 F-TEST (DF numerator) 13 F-TEST (DF denominator) 61 p-value 1.13242748511766e-14 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 267.636437453185 Sum Squared Residuals 4369385.02181058 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 9700 9504.05421167407 195.945788325931 2 9081 8771.91135453116 309.08864546884 3 9084 9130.62564024545 -46.6256402454448 4 9743 9457.7336874757 285.266312524301 5 8587 8580.2336874757 6.76631252430321 6 9731 9546.7336874757 184.266312524302 7 9563 9172.40035414237 390.599645857634 8 9998 9351.7336874757 646.266312524301 9 9437 9307.06702080903 129.932979190968 10 10038 9949.1179911084 88.8820088915959 11 9918 9827.45132444174 90.5486755582625 12 9252 9603.95132444174 -351.951324441737 13 9737 9713.44228916273 23.5577108372739 14 9035 8981.29943201988 53.7005679801232 15 9133 9340.01371773416 -207.013717734163 16 9487 9667.12176496441 -180.121764964415 17 8700 8789.62176496441 -89.6217649644153 18 9627 9756.12176496441 -129.121764964415 19 8947 9381.78843163108 -434.788431631081 20 9283 9561.12176496441 -278.121764964415 21 8829 9516.45509829775 -687.455098297748 22 9947 9757.20024680088 189.799753199114 23 9628 9635.53358013422 -7.5335801342187 24 9318 9412.03358013422 -94.0335801342194 25 9605 9521.52454485521 83.4754551447925 26 8640 8789.38168771236 -149.381687712359 27 9214 9148.09597342665 65.9040265733554 28 9567 9475.2040206569 91.7959793431038 29 8547 8597.7040206569 -50.7040206568965 30 9185 9564.2040206569 -379.204020656896 31 9470 9189.87068732356 280.129312676437 32 9123 9369.2040206569 -246.204020656896 33 9278 9324.53735399023 -46.5373539902296 34 10170 9966.5883242896 203.411675710398 35 9434 9844.92165762294 -410.921657622936 36 9655 9621.42165762294 33.5783423770639 37 9429 9730.91262234392 -301.912622343924 38 8739 8998.76976520108 -259.769765201076 39 9552 9357.48405091536 194.515949084639 40 9687 9684.59209814561 2.4079018543869 41 9019 8807.09209814561 211.907901854387 42 9672 9773.59209814561 -101.592098145613 43 9206 9399.25876481228 -193.258764812280 44 9069 9578.59209814561 -509.592098145613 45 9788 9533.92543147895 254.074568521054 46 10312 10175.9764017783 136.023598221681 47 10105 10054.3097351117 50.6902648883475 48 9863 9830.80973511165 32.1902648883470 49 9656 9940.30069983264 -284.300699832641 50 9295 9208.15784268979 86.8421573102074 51 9946 9566.87212840408 379.127871595922 52 9701 9893.98017563433 -192.98017563433 53 9049 9016.48017563433 32.5198243656698 54 10190 9982.98017563433 207.01982436567 55 9706 9608.646842301 97.3531576990037 56 9765 9787.98017563433 -22.9801756343301 57 9893 9743.31350896766 149.686491032337 58 9994 10385.3644792670 -391.364479267036 59 10433 10263.6978126004 169.302187399631 60 10073 10040.1978126004 32.8021873996302 61 10112 10149.6887773214 -37.688777321358 62 9266 9417.54592017851 -151.545920178510 63 9820 9776.2602058928 43.7397941072049 64 10097 10103.3682531230 -6.3682531230469 65 9115 9225.86825312305 -110.868253123047 66 10411 10192.3682531230 218.631746876953 67 9678 9818.03491978971 -140.034919789713 68 10408 9997.36825312305 410.631746876953 69 10153 9952.70158645638 200.29841354362 70 10368 10594.7525567558 -226.752556755753 71 10581 10473.0858900891 107.914109910914 72 10597 10249.5858900891 347.414109910913 73 10680 10359.0768548101 320.923145189925 74 9738 9626.93399766723 111.066002332773 75 9556 9985.6482833815 -429.648283381512 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.129209909522889 0.258419819045779 0.87079009047711 18 0.0583942985274169 0.116788597054834 0.941605701472583 19 0.332065864761555 0.664131729523111 0.667934135238444 20 0.576658759657813 0.846682480684375 0.423341240342187 21 0.585466042836542 0.829067914326916 0.414533957163458 22 0.495827054900449 0.991654109800898 0.504172945099551 23 0.403413001326875 0.806826002653749 0.596586998673125 24 0.339189162554008 0.678378325108016 0.660810837445992 25 0.269892135266888 0.539784270533777 0.730107864733112 26 0.223539919608508 0.447079839217016 0.776460080391492 27 0.227440585234970 0.454881170469940 0.77255941476503 28 0.180967320316690 0.361934640633381 0.81903267968331 29 0.126873690985811 0.253747381971622 0.873126309014189 30 0.159052039345535 0.318104078691069 0.840947960654465 31 0.22873113355383 0.45746226710766 0.77126886644617 32 0.238599414276501 0.477198828553003 0.761400585723499 33 0.232763097370446 0.465526194740893 0.767236902629554 34 0.322942415992655 0.64588483198531 0.677057584007345 35 0.327878318261676 0.655756636523352 0.672121681738324 36 0.420042841961298 0.840085683922595 0.579957158038702 37 0.364928393115911 0.729856786231822 0.635071606884089 38 0.313325698883186 0.626651397766371 0.686674301116814 39 0.441972124585552 0.883944249171105 0.558027875414448 40 0.394719008492181 0.789438016984363 0.605280991507819 41 0.472993520307928 0.945987040615856 0.527006479692072 42 0.439107578786023 0.878215157572046 0.560892421213977 43 0.364289202644912 0.728578405289825 0.635710797355088 44 0.580624930878698 0.838750138242604 0.419375069121302 45 0.645052858207686 0.709894283584628 0.354947141792314 46 0.717192793982526 0.565614412034949 0.282807206017474 47 0.6800808106739 0.6398383786522 0.3199191893261 48 0.638735097269915 0.722529805460171 0.361264902730085 49 0.681520047837735 0.63695990432453 0.318479952162265 50 0.615305744602649 0.769388510794702 0.384694255397351 51 0.860701252467385 0.278597495065230 0.139298747532615 52 0.798793871375885 0.402412257248231 0.201206128624115 53 0.744562475998056 0.510875048003888 0.255437524001944 54 0.672021418829337 0.655957162341326 0.327978581170663 55 0.658005964511227 0.683988070977546 0.341994035488773 56 0.627994162987721 0.744011674024557 0.372005837012279 57 0.490539842942782 0.981079685885564 0.509460157057218 58 0.353562935020145 0.70712587004029 0.646437064979855 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/Nov/29/t1291061338ku0fed9ftvkorjt/102ls61291061366.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291061338ku0fed9ftvkorjt/102ls61291061366.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291061338ku0fed9ftvkorjt/1vkwu1291061366.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291061338ku0fed9ftvkorjt/1vkwu1291061366.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291061338ku0fed9ftvkorjt/2vkwu1291061366.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291061338ku0fed9ftvkorjt/2vkwu1291061366.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291061338ku0fed9ftvkorjt/3vkwu1291061366.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291061338ku0fed9ftvkorjt/3vkwu1291061366.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291061338ku0fed9ftvkorjt/46bvf1291061366.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291061338ku0fed9ftvkorjt/46bvf1291061366.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291061338ku0fed9ftvkorjt/56bvf1291061366.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291061338ku0fed9ftvkorjt/56bvf1291061366.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291061338ku0fed9ftvkorjt/66bvf1291061366.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291061338ku0fed9ftvkorjt/66bvf1291061366.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291061338ku0fed9ftvkorjt/7z2ui1291061366.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291061338ku0fed9ftvkorjt/7z2ui1291061366.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291061338ku0fed9ftvkorjt/8rutl1291061366.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291061338ku0fed9ftvkorjt/8rutl1291061366.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291061338ku0fed9ftvkorjt/9rutl1291061366.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291061338ku0fed9ftvkorjt/9rutl1291061366.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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