Workshop 7

*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 08:32:51 -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/t1258644870gllazrrm9qt3ktc.htm/, Retrieved Thu, 19 Nov 2009 16:34:42 +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/t1258644870gllazrrm9qt3ktc.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:

Workshop 7

Dataseries X:

» Textbox « » Textfile « » CSV «

0.6348 1.5291 0.634 1.5358 0.62915 1.5355 0.62168 1.5287 0.61328 1.5334 0.6089 1.5225 0.60857 1.5135 0.62672 1.5144 0.62291 1.4913 0.62393 1.4793 0.61838 1.4663 0.62012 1.4749 0.61659 1.4745 0.6116 1.4775 0.61573 1.4678 0.61407 1.4658 0.62823 1.4572 0.64405 1.4721 0.6387 1.4624 0.63633 1.4636 0.63059 1.4649 0.62994 1.465 0.63709 1.4673 0.64217 1.4679 0.65711 1.4621 0.66977 1.4674 0.68255 1.4695 0.68902 1.4964 0.71322 1.5155 0.70224 1.5411 0.70045 1.5476 0.69919 1.54 0.69693 1.5474 0.69763 1.5485 0.69278 1.559 0.70196 1.5544 0.69215 1.5657 0.6769 1.5734 0.67124 1.567 0.66532 1.5547 0.67157 1.54 0.66428 1.5192 0.66576 1.527 0.66942 1.5387 0.6813 1.5431 0.69144 1.5426 0.69862 1.5216 0.695 1.5364 0.69867 1.5469 0.68968 1.5501 0.69233 1.5494 0.68293 1.5475 0.68399 1.5448 0.66895 1.5391 0.68756 1.5578 0.68527 1.5528 0.6776 1.5496 0.68137 1.549 0.67933 1.5449 0.67922 1.5479

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 10 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Britse_pond[t] = -0.191028651774354 + 0.561232545674784Zwitserse_frank[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) -0.191028651774354 0.134703 -1.4182 0.161497 0.080749 Zwitserse_frank 0.561232545674784 0.088703 6.3271 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.63902661439162 R-squared 0.408355013900816 Adjusted R-squared 0.398154238278416 F-TEST (value) 40.0317612127567 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 3.91446610681356e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.0246729447217217 Sum Squared Residuals 0.0353077436719859 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 0.6348 0.667152033816959 -0.0323520338169584 2 0.634 0.670912291872979 -0.0369122918729788 3 0.62915 0.670743922109276 -0.0415939221092765 4 0.62168 0.666927540798688 -0.0452475407986878 5 0.61328 0.669565333763359 -0.0562853337633594 6 0.6089 0.663447899015504 -0.0545478990155042 7 0.60857 0.658396806104431 -0.0498268061044311 8 0.62672 0.658901915395538 -0.0321819153955384 9 0.62291 0.645937443590451 -0.0230274435904510 10 0.62393 0.639202653042354 -0.0152726530423536 11 0.61838 0.631906629948581 -0.0135266299485813 12 0.62012 0.636733229841385 -0.0166132298413846 13 0.61659 0.636508736823115 -0.0199187368231146 14 0.6116 0.638192434460139 -0.0265924344601389 15 0.61573 0.632748478767094 -0.0170184787670936 16 0.61407 0.631626013675744 -0.0175560136757440 17 0.62823 0.626799413782941 0.00143058621705908 18 0.64405 0.635161778713495 0.0088882212865049 19 0.6387 0.62971782302045 0.00898217697955036 20 0.63633 0.63039130207526 0.00593869792474048 21 0.63059 0.631120904384637 -0.000530904384636748 22 0.62994 0.631177027639204 -0.00123702763920415 23 0.63709 0.632467862494256 0.00462213750574385 24 0.64217 0.632804602021661 0.009365397978339 25 0.65711 0.629549453256747 0.0275605467432527 26 0.66977 0.632523985748824 0.0372460142511763 27 0.68255 0.633702574094741 0.0488474259052593 28 0.68902 0.648799729573392 0.0402202704266076 29 0.71322 0.659519271195781 0.0537007288042192 30 0.70224 0.673886824365055 0.0283531756349448 31 0.70045 0.677534835911941 0.0229151640880587 32 0.69919 0.673269468564813 0.0259205314351870 33 0.69693 0.677422589402806 0.0195074105971937 34 0.69763 0.678039945203049 0.0195900547969514 35 0.69278 0.683932886932634 0.00884711306736617 36 0.70196 0.68135121722253 0.0206087827774702 37 0.69215 0.687693144988655 0.00445685501134513 38 0.6769 0.69201463559035 -0.0151146355903507 39 0.67124 0.688422747298032 -0.0171827472980321 40 0.66532 0.681519586986232 -0.0161995869862322 41 0.67157 0.673269468564813 -0.00169946856481294 42 0.66428 0.661595831614778 0.00268416838522250 43 0.66576 0.665973445471041 -0.000213445471040670 44 0.66942 0.672539866255436 -0.00311986625543566 45 0.6813 0.675009289456405 0.00629071054359531 46 0.69144 0.674728673183567 0.0167113268164327 47 0.69862 0.662942789724397 0.0356772102756031 48 0.695 0.671249031400384 0.0237509685996163 49 0.69867 0.677141973129969 0.0215280268700311 50 0.68968 0.678937917276128 0.0107420827238717 51 0.69233 0.678545054494156 0.0137849455058441 52 0.68293 0.677478712657374 0.00545128734262618 53 0.68399 0.675963384784052 0.00802661521594813 54 0.66895 0.672764359273706 -0.00381435927370552 55 0.68756 0.683259407877824 0.00430059212217584 56 0.68527 0.68045324514945 0.00481675485054992 57 0.6776 0.678657301003291 -0.00105730100329091 58 0.68137 0.678320561475886 0.00304943852411409 59 0.67933 0.67601950803862 0.00331049196138066 60 0.67922 0.677703205675644 0.00151679432435630 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.126043354969763 0.252086709939525 0.873956645030237 6 0.0861429766550455 0.172285953310091 0.913857023344955 7 0.0504129256064236 0.100825851212847 0.949587074393576 8 0.0703372455235365 0.140674491047073 0.929662754476464 9 0.0629751766872091 0.125950353374418 0.93702482331279 10 0.0396552290923163 0.0793104581846326 0.960344770907684 11 0.021127101955491 0.042254203910982 0.978872898044509 12 0.0115611430589839 0.0231222861179678 0.988438856941016 13 0.00701916988013395 0.0140383397602679 0.992980830119866 14 0.0067439511301425 0.013487902260285 0.993256048869858 15 0.00463429610455836 0.00926859220911671 0.995365703895442 16 0.00383443672920113 0.00766887345840226 0.996165563270799 17 0.00485282154807205 0.0097056430961441 0.995147178451928 18 0.027691664999013 0.055383329998026 0.972308335000987 19 0.0385425838647824 0.0770851677295647 0.961457416135218 20 0.0407561917156813 0.0815123834313626 0.959243808284319 21 0.0404317524695324 0.0808635049390648 0.959568247530468 22 0.0488706873247855 0.097741374649571 0.951129312675214 23 0.0765351252977267 0.153070250595453 0.923464874702273 24 0.155166966743271 0.310333933486542 0.844833033256729 25 0.38527597973667 0.77055195947334 0.61472402026333 26 0.73782771509952 0.524344569800961 0.262172284900481 27 0.941197298791298 0.117605402417404 0.0588027012087018 28 0.991542343810351 0.0169153123792974 0.0084576561896487 29 0.999927263362745 0.000145473274509459 7.27366372547294e-05 30 0.999990297292158 1.94054156843845e-05 9.70270784219224e-06 31 0.999996328852836 7.34229432767475e-06 3.67114716383737e-06 32 0.999998051530186 3.89693962802466e-06 1.94846981401233e-06 33 0.999998131318183 3.73736363461377e-06 1.86868181730689e-06 34 0.999998154550388 3.69089922397126e-06 1.84544961198563e-06 35 0.999996475053994 7.0498920114714e-06 3.5249460057357e-06 36 0.999997802584046 4.39483190830195e-06 2.19741595415098e-06 37 0.999995711635379 8.57672924248656e-06 4.28836462124328e-06 38 0.999989150712826 2.16985743481075e-05 1.08492871740538e-05 39 0.999983647584836 3.27048303285935e-05 1.63524151642967e-05 40 0.999989250469928 2.14990601447246e-05 1.07495300723623e-05 41 0.999979290399633 4.14192007349759e-05 2.07096003674879e-05 42 0.999972398413449 5.52031731026553e-05 2.76015865513276e-05 43 0.999984826000462 3.03479990752284e-05 1.51739995376142e-05 44 0.999991699023251 1.66019534971663e-05 8.30097674858314e-06 45 0.999976597493563 4.68050128736922e-05 2.34025064368461e-05 46 0.999936365777482 0.000127268445036850 6.36342225184251e-05 47 0.999917840512823 0.000164318974354568 8.21594871772838e-05 48 0.99996093013399 7.81397320197246e-05 3.90698660098623e-05 49 0.999995530430918 8.9391381636776e-06 4.4695690818388e-06 50 0.999987132998027 2.57340039459862e-05 1.28670019729931e-05 51 0.999994833533464 1.03329330728023e-05 5.16646653640115e-06 52 0.999970056933853 5.98861322940471e-05 2.99430661470236e-05 53 0.99998284440491 3.43111901788382e-05 1.71555950894191e-05 54 0.99989456924676 0.000210861506480853 0.000105430753240426 55 0.998685942905344 0.00262811418931198 0.00131405709465599 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 30 0.588235294117647 NOK 5% type I error level 35 0.686274509803922 NOK 10% type I error level 41 0.80392156862745 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644870gllazrrm9qt3ktc/1088rz1258644760.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644870gllazrrm9qt3ktc/1088rz1258644760.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644870gllazrrm9qt3ktc/1vovz1258644760.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644870gllazrrm9qt3ktc/1vovz1258644760.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644870gllazrrm9qt3ktc/2oifp1258644760.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644870gllazrrm9qt3ktc/2oifp1258644760.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644870gllazrrm9qt3ktc/3xd641258644760.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644870gllazrrm9qt3ktc/3xd641258644760.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644870gllazrrm9qt3ktc/490af1258644760.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644870gllazrrm9qt3ktc/490af1258644760.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644870gllazrrm9qt3ktc/5erem1258644760.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644870gllazrrm9qt3ktc/5erem1258644760.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644870gllazrrm9qt3ktc/63pd01258644760.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644870gllazrrm9qt3ktc/63pd01258644760.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644870gllazrrm9qt3ktc/724e01258644760.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644870gllazrrm9qt3ktc/724e01258644760.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644870gllazrrm9qt3ktc/8ftnx1258644760.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644870gllazrrm9qt3ktc/8ftnx1258644760.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644870gllazrrm9qt3ktc/9lblo1258644760.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258644870gllazrrm9qt3ktc/9lblo1258644760.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') }

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by