*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 04:58:04 -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/t1258545576r8rdhi0g8c7vo2e.htm/, Retrieved Wed, 18 Nov 2009 12:59:51 +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/t1258545576r8rdhi0g8c7vo2e.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:

» Textbox « » Textfile « » CSV «

519 97.4 517 97 510 105.4 509 102.7 501 98.1 507 104.5 569 87.4 580 89.9 578 109.8 565 111.7 547 98.6 555 96.9 562 95.1 561 97 555 112.7 544 102.9 537 97.4 543 111.4 594 87.4 611 96.8 613 114.1 611 110.3 594 103.9 595 101.6 591 94.6 589 95.9 584 104.7 573 102.8 567 98.1 569 113.9 621 80.9 629 95.7 628 113.2 612 105.9 595 108.8 597 102.3 593 99 590 100.7 580 115.5 574 100.7 573 109.9 573 114.6 620 85.4 626 100.5 620 114.8 588 116.5 566 112.9 557 102 561 106 549 105.3 532 118.8 526 106.1 511 109.3 499 117.2 555 92.5 565 104.2 542 112.5 527 122.4 510 113.3 514 100 517 110.7 508 112.8 493 109.8 490 117.3 469 109.1 478 115.9 528 96 534 99.8 518 116.8 506 115.7 502 99.4 516 94.3

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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 690.68970616088 -1.2874820710457X[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) 690.68970616088 55.123377 12.5299 0 0 X -1.2874820710457 0.525574 -2.4497 0.016803 0.008402 Multiple Linear Regression - Regression Statistics Multiple R 0.280994807358491 R-squared 0.0789580817624354 Adjusted R-squared 0.0658003400733274 F-TEST (value) 6.00088401399433 F-TEST (DF numerator) 1 F-TEST (DF denominator) 70 p-value 0.016803462470376 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 39.5369962059785 Sum Squared Residuals 109422.184829409 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 519 565.288952441026 -46.2889524410262 2 517 565.803945269447 -48.8039452694473 3 510 554.989095872663 -44.9890958726634 4 509 558.465297464487 -49.4652974644868 5 501 564.387714991297 -63.387714991297 6 507 556.147829736605 -49.1478297366045 7 569 578.163773151486 -9.16377315148601 8 580 574.945067973872 5.05493202612824 9 578 549.324174760062 28.6758252399377 10 565 546.877958825075 18.1220411749245 11 547 563.743973955774 -16.7439739557742 12 555 565.932693476552 -10.9326934765518 13 562 568.250161204434 -6.25016120443413 14 561 565.803945269447 -4.80394526944728 15 555 545.59047675403 9.40952324597023 16 544 558.207801050278 -14.2078010502776 17 537 565.288952441029 -28.288952441029 18 543 547.264203446389 -4.26420344638918 19 594 578.163773151486 15.836226848514 20 611 566.061441683656 44.9385583163436 21 613 543.788001854566 69.2119981454342 22 611 548.680433724540 62.3195662754605 23 594 556.920318979232 37.0796810207681 24 595 559.881527742637 35.1184722573629 25 591 568.893902239957 22.1060977600430 26 589 567.220175547598 21.7798244524025 27 584 555.890333322395 28.1096666776046 28 573 558.336549257382 14.6634507426178 29 567 564.387714991297 2.61228500870298 30 569 544.045498268775 24.9545017312251 31 621 586.532406613283 34.4675933867169 32 629 567.477671961807 61.5223280381933 33 628 544.946735718507 83.053264281493 34 612 554.345354837141 57.6546451628595 35 595 550.611656831108 44.388343168892 36 597 558.980290292905 38.0197097070949 37 593 563.228981127356 29.7710188726441 38 590 561.040261606578 28.9597383934218 39 580 541.985526955102 38.0144730448982 40 574 561.040261606578 12.9597383934218 41 573 549.195426552958 23.8045734470423 42 573 543.144260819043 29.8557391809571 43 620 580.738737293577 39.2612627064226 44 626 561.297758020787 64.7022419792127 45 620 542.886764404834 77.1132355951662 46 588 540.698044884056 47.3019551159439 47 566 545.332980339821 20.6670196601794 48 557 559.366534914219 -2.36653491421878 49 561 554.216606630036 6.78339336996403 50 549 555.117844079768 -6.11784407976797 51 532 537.736836120651 -5.736836120651 52 526 554.087858422931 -28.0878584229314 53 511 549.967915795585 -38.9679157955852 54 499 539.796807434324 -40.7968074343241 55 555 571.597614589153 -16.5976145891529 56 565 556.534074357918 8.46592564208177 57 542 545.847973168239 -3.84797316823891 58 527 533.101900664886 -6.10190066488647 59 510 544.817987511402 -34.8179875114024 60 514 561.94149905631 -47.9414990563102 61 517 548.165440896121 -31.1654408961212 62 508 545.461728546925 -37.4617285469252 63 493 549.324174760062 -56.3241747600623 64 490 539.66805922722 -49.6680592272196 65 469 550.225412209794 -81.2254122097943 66 478 541.470534126684 -63.4705341266835 67 528 567.091427340493 -39.091427340493 68 534 562.198995470519 -28.1989954705193 69 518 540.311800262742 -22.3118002627424 70 506 541.728030540893 -35.7280305408927 71 502 562.713988298938 -60.7139882989376 72 516 569.280146861271 -53.2801468612707 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.011877691833674 0.023755383667348 0.988122308166326 6 0.00213321583931494 0.00426643167862988 0.997866784160685 7 0.0121304932545001 0.0242609865090002 0.9878695067455 8 0.0175959214849322 0.0351918429698644 0.982404078515068 9 0.362739814483858 0.725479628967715 0.637260185516142 10 0.387681234090001 0.775362468180002 0.612318765909999 11 0.296583269193904 0.593166538387808 0.703416730806096 12 0.227118037244082 0.454236074488164 0.772881962755918 13 0.176185744244875 0.352371488489749 0.823814255755125 14 0.132446151117680 0.264892302235361 0.86755384888232 15 0.101174638161287 0.202349276322574 0.898825361838713 16 0.0667999612619813 0.133599922523963 0.933200038738019 17 0.0455645864576396 0.0911291729152792 0.95443541354236 18 0.0280801160868231 0.0561602321736462 0.971919883913177 19 0.0341727082746726 0.0683454165493451 0.965827291725327 20 0.0742676708333081 0.148535341666616 0.925732329166692 21 0.191543743594763 0.383087487189525 0.808456256405237 22 0.278690375843783 0.557380751687566 0.721309624156217 23 0.275064771390535 0.55012954278107 0.724935228609465 24 0.268381901268354 0.536763802536709 0.731618098731646 25 0.244068532781955 0.488137065563911 0.755931467218045 26 0.214206859276139 0.428413718552278 0.785793140723861 27 0.183570392243716 0.367140784487432 0.816429607756284 28 0.143129619387248 0.286259238774497 0.856870380612752 29 0.106589259129832 0.213178518259665 0.893410740870168 30 0.082302574109709 0.164605148219418 0.91769742589029 31 0.0964790974727624 0.192958194945525 0.903520902527238 32 0.149365933286106 0.298731866572213 0.850634066713894 33 0.288965231060441 0.577930462120882 0.711034768939559 34 0.343516775906376 0.687033551812751 0.656483224093624 35 0.348871584448182 0.697743168896364 0.651128415551818 36 0.344141299657668 0.688282599315335 0.655858700342332 37 0.323416182250834 0.646832364501669 0.676583817749166 38 0.303382909274226 0.606765818548451 0.696617090725774 39 0.299823910266792 0.599647820533585 0.700176089733208 40 0.256576677730916 0.513153355461832 0.743423322269084 41 0.232634058088175 0.465268116176351 0.767365941911825 42 0.22480208638241 0.44960417276482 0.77519791361759 43 0.292584365233931 0.585168730467863 0.707415634766069 44 0.549286539069867 0.901426921860265 0.450713460930133 45 0.860055237119841 0.279889525760318 0.139944762880159 46 0.949420213729171 0.101159572541658 0.0505797862708288 47 0.970189560937398 0.0596208781252029 0.0298104390626015 48 0.969960902515557 0.0600781949688862 0.0300390974844431 49 0.978859153072537 0.0422816938549252 0.0211408469274626 50 0.979921957746687 0.040156084506626 0.020078042253313 51 0.981390918950324 0.0372181620993513 0.0186090810496757 52 0.975730317500082 0.0485393649998353 0.0242696824999176 53 0.969994697345969 0.0600106053080625 0.0300053026540312 54 0.96445598072284 0.0710880385543191 0.0355440192771596 55 0.961427619315652 0.0771447613686953 0.0385723806843477 56 0.98828623685673 0.0234275262865393 0.0117137631432697 57 0.993391809403764 0.0132163811924715 0.00660819059623573 58 0.996148275459702 0.0077034490805959 0.00385172454029795 59 0.99368548191223 0.0126290361755395 0.00631451808776976 60 0.988450874050503 0.0230982518989937 0.0115491259494969 61 0.983065700900822 0.0338685981983569 0.0169342990991784 62 0.971395621260602 0.0572087574787964 0.0286043787393982 63 0.952277212433601 0.095445575132797 0.0477227875663985 64 0.914249734753584 0.171500530492832 0.0857502652464158 65 0.96076861383008 0.0784627723398388 0.0392313861699194 66 0.978167719176133 0.0436645616477348 0.0218322808238674 67 0.941916941683648 0.116166116632704 0.0580830583163518 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 2 0.0317460317460317 NOK 5% type I error level 15 0.238095238095238 NOK 10% type I error level 26 0.412698412698413 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545576r8rdhi0g8c7vo2e/10dtzk1258545479.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545576r8rdhi0g8c7vo2e/10dtzk1258545479.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545576r8rdhi0g8c7vo2e/1pxn11258545479.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545576r8rdhi0g8c7vo2e/1pxn11258545479.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545576r8rdhi0g8c7vo2e/2b9wy1258545479.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545576r8rdhi0g8c7vo2e/2b9wy1258545479.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545576r8rdhi0g8c7vo2e/3lqc71258545479.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545576r8rdhi0g8c7vo2e/3lqc71258545479.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545576r8rdhi0g8c7vo2e/4jrje1258545479.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545576r8rdhi0g8c7vo2e/4jrje1258545479.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545576r8rdhi0g8c7vo2e/592vv1258545479.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545576r8rdhi0g8c7vo2e/592vv1258545479.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545576r8rdhi0g8c7vo2e/6zgh51258545479.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545576r8rdhi0g8c7vo2e/6zgh51258545479.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545576r8rdhi0g8c7vo2e/7rxh21258545479.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545576r8rdhi0g8c7vo2e/7rxh21258545479.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545576r8rdhi0g8c7vo2e/88yrh1258545479.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545576r8rdhi0g8c7vo2e/88yrh1258545479.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545576r8rdhi0g8c7vo2e/93x9e1258545479.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545576r8rdhi0g8c7vo2e/93x9e1258545479.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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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