Workshop7 Wegwerken seizonaliteit

*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: Thu, 19 Nov 2009 13:39:45 -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/19/t1258663298qhqv30kl4ruge5v.htm/, Retrieved Thu, 19 Nov 2009 21:41:49 +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/19/t1258663298qhqv30kl4ruge5v.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 «

562325 0 560854 0 555332 0 543599 0 536662 0 542722 0 593530 0 610763 0 612613 0 611324 0 594167 0 595454 0 590865 0 589379 0 584428 0 573100 0 567456 0 569028 0 620735 0 628884 0 628232 0 612117 0 595404 0 597141 0 593408 0 590072 0 579799 0 574205 0 572775 0 572942 0 619567 0 625809 0 619916 0 587625 0 565742 0 557274 0 560576 0 548854 0 531673 0 525919 0 511038 0 498662 1 555362 1 564591 1 541657 1 527070 1 509846 1 514258 1 516922 1 507561 1 492622 1 490243 1 469357 1 477580 1 528379 1 533590 1 517945 1 506174 1 501866 1 516141 1 528222 1 532638 1 536322 1 536535 1 523597 1 536214 1 586570 1 596594 1 580523 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 579977.785245902 -59810.4631147541X[t] -1321.29754098364M1[t] -5147.96420765032M2[t] -13344.9642076503M3[t] -19440.7975409836M4[t] -29893.4642076503M5[t] -17214.5536885246M6[t] + 33951.2796448087M7[t] + 43299.2796448087M8[t] + 33408.4463114754M9[t] + 12808.4M10[t] -2648.6M11[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) 579977.785245902 9811.90954 59.1096 0 0 X -59810.4631147541 5310.851826 -11.2619 0 0 M1 -1321.29754098364 12975.106521 -0.1018 0.919253 0.459626 M2 -5147.96420765032 12975.106521 -0.3968 0.693055 0.346527 M3 -13344.9642076503 12975.106521 -1.0285 0.308134 0.154067 M4 -19440.7975409836 12975.106521 -1.4983 0.139668 0.069834 M5 -29893.4642076503 12975.106521 -2.3039 0.024962 0.012481 M6 -17214.5536885246 12981.14342 -1.3261 0.190184 0.095092 M7 33951.2796448087 12981.14342 2.6154 0.011431 0.005715 M8 43299.2796448087 12981.14342 3.3356 0.001516 0.000758 M9 33408.4463114754 12981.14342 2.5736 0.012739 0.006369 M10 12808.4 13547.010393 0.9455 0.348477 0.174238 M11 -2648.6 13547.010393 -0.1955 0.8457 0.42285 Multiple Linear Regression - Regression Statistics Multiple R 0.87681888043466 R-squared 0.768811349086691 Adjusted R-squared 0.719270923890982 F-TEST (value) 15.5188686017431 F-TEST (DF numerator) 12 F-TEST (DF denominator) 56 p-value 1.04360964314765e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 21419.7041633918 Sum Squared Residuals 25693008681.0445 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 562325 578656.487704918 -16331.4877049182 2 560854 574829.821038252 -13975.8210382515 3 555332 566632.821038251 -11300.8210382514 4 543599 560536.987704918 -16937.987704918 5 536662 550084.321038251 -13422.3210382514 6 542722 562763.231557377 -20041.2315573770 7 593530 613929.06489071 -20399.0648907104 8 610763 623277.06489071 -12514.0648907104 9 612613 613386.231557377 -773.231557377052 10 611324 592786.185245902 18537.8147540984 11 594167 577329.185245902 16837.8147540984 12 595454 579977.785245902 15476.2147540984 13 590865 578656.487704918 12208.512295082 14 589379 574829.821038251 14549.1789617487 15 584428 566632.821038251 17795.1789617486 16 573100 560536.987704918 12563.0122950820 17 567456 550084.321038251 17371.6789617486 18 569028 562763.231557377 6264.76844262296 19 620735 613929.06489071 6805.93510928962 20 628884 623277.06489071 5606.93510928962 21 628232 613386.231557377 14845.7684426230 22 612117 592786.185245902 19330.8147540983 23 595404 577329.185245902 18074.8147540984 24 597141 579977.785245902 17163.2147540984 25 593408 578656.487704918 14751.512295082 26 590072 574829.821038251 15242.1789617487 27 579799 566632.821038251 13166.1789617486 28 574205 560536.987704918 13668.0122950820 29 572775 550084.321038251 22690.6789617486 30 572942 562763.231557377 10178.7684426230 31 619567 613929.06489071 5637.93510928961 32 625809 623277.06489071 2531.93510928962 33 619916 613386.231557377 6529.76844262296 34 587625 592786.185245902 -5161.18524590164 35 565742 577329.185245902 -11587.1852459016 36 557274 579977.785245902 -22703.7852459016 37 560576 578656.487704918 -18080.487704918 38 548854 574829.821038251 -25975.8210382513 39 531673 566632.821038251 -34959.8210382514 40 525919 560536.987704918 -34617.987704918 41 511038 550084.321038251 -39046.3210382514 42 498662 502952.768442623 -4290.76844262295 43 555362 554118.601775956 1243.39822404372 44 564591 563466.601775956 1124.39822404371 45 541657 553575.768442623 -11918.7684426229 46 527070 532975.722131148 -5905.72213114755 47 509846 517518.722131148 -7672.72213114755 48 514258 520167.322131148 -5909.32213114755 49 516922 518846.024590164 -1924.0245901639 50 507561 515019.357923497 -7458.35792349725 51 492622 506822.357923497 -14200.3579234973 52 490243 500726.524590164 -10483.5245901639 53 469357 490273.857923497 -20916.8579234973 54 477580 502952.768442623 -25372.7684426230 55 528379 554118.601775956 -25739.6017759563 56 533590 563466.601775956 -29876.6017759563 57 517945 553575.768442623 -35630.768442623 58 506174 532975.722131148 -26801.7221311475 59 501866 517518.722131148 -15652.7221311475 60 516141 520167.322131148 -4026.32213114755 61 528222 518846.024590164 9375.9754098361 62 532638 515019.357923497 17618.6420765028 63 536322 506822.357923497 29499.6420765027 64 536535 500726.524590164 35808.4754098361 65 523597 490273.857923497 33323.1420765027 66 536214 502952.768442623 33261.231557377 67 586570 554118.601775956 32451.3982240437 68 596594 563466.601775956 33127.3982240437 69 580523 553575.768442623 26947.2315573770 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.613935162588891 0.772129674822218 0.386064837411109 17 0.573694560867506 0.852610878264989 0.426305439132494 18 0.504465221186488 0.991069557627024 0.495534778813512 19 0.447367628734733 0.894735257469465 0.552632371265267 20 0.355817947496966 0.711635894993932 0.644182052503034 21 0.276641275992534 0.553282551985068 0.723358724007466 22 0.205845408833137 0.411690817666275 0.794154591166863 23 0.149500688627390 0.299001377254779 0.85049931137261 24 0.106510998846314 0.213021997692627 0.893489001153686 25 0.0850573625634396 0.170114725126879 0.91494263743656 26 0.0666413726513684 0.133282745302737 0.933358627348632 27 0.0478024953891648 0.0956049907783297 0.952197504610835 28 0.0376025644499594 0.0752051288999189 0.96239743555004 29 0.0415001543649843 0.0830003087299687 0.958499845635016 30 0.0341952294270010 0.0683904588540019 0.965804770573 31 0.0245122751394213 0.0490245502788426 0.975487724860579 32 0.0158655466764610 0.0317310933529219 0.984134453323539 33 0.0125958449358681 0.0251916898717362 0.987404155064132 34 0.0167071760884845 0.0334143521769691 0.983292823911516 35 0.0234887796108521 0.0469775592217042 0.976511220389148 36 0.0378405962587916 0.0756811925175833 0.962159403741208 37 0.0332138014656297 0.0664276029312593 0.96678619853437 38 0.0358881319562016 0.0717762639124032 0.964111868043798 39 0.0491743544721764 0.0983487089443528 0.950825645527824 40 0.0536812492301169 0.107362498460234 0.946318750769883 41 0.0716896033993708 0.143379206798742 0.92831039660063 42 0.0463694011897022 0.0927388023794044 0.953630598810298 43 0.0283293212039898 0.0566586424079796 0.97167067879601 44 0.0162909669770051 0.0325819339540102 0.983709033022995 45 0.0101245441798726 0.0202490883597452 0.989875455820127 46 0.00622488392530948 0.0124497678506190 0.99377511607469 47 0.00318928030695518 0.00637856061391035 0.996810719693045 48 0.00145474713596436 0.00290949427192873 0.998545252864036 49 0.000655783816870145 0.00131156763374029 0.99934421618313 50 0.000316384307918566 0.000632768615837133 0.999683615692081 51 0.000234200191497188 0.000468400382994376 0.999765799808503 52 0.000188556877370691 0.000377113754741382 0.99981144312263 53 0.000232556375310099 0.000465112750620198 0.99976744362469 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 7 0.184210526315789 NOK 5% type I error level 15 0.394736842105263 NOK 10% type I error level 25 0.657894736842105 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663298qhqv30kl4ruge5v/10dwll1258663180.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663298qhqv30kl4ruge5v/10dwll1258663180.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663298qhqv30kl4ruge5v/1deaq1258663180.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663298qhqv30kl4ruge5v/1deaq1258663180.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663298qhqv30kl4ruge5v/200241258663180.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663298qhqv30kl4ruge5v/200241258663180.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663298qhqv30kl4ruge5v/3kr6p1258663180.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663298qhqv30kl4ruge5v/3kr6p1258663180.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663298qhqv30kl4ruge5v/45w3m1258663180.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663298qhqv30kl4ruge5v/45w3m1258663180.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663298qhqv30kl4ruge5v/5dzka1258663180.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663298qhqv30kl4ruge5v/5dzka1258663180.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663298qhqv30kl4ruge5v/603el1258663180.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663298qhqv30kl4ruge5v/603el1258663180.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663298qhqv30kl4ruge5v/7bnji1258663180.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663298qhqv30kl4ruge5v/7bnji1258663180.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663298qhqv30kl4ruge5v/88sjp1258663180.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663298qhqv30kl4ruge5v/88sjp1258663180.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663298qhqv30kl4ruge5v/95m821258663180.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258663298qhqv30kl4ruge5v/95m821258663180.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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