WS7.1

*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: Sat, 21 Nov 2009 04:46:46 -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/21/t1258805031fbtpzjogdtwtivv.htm/, Retrieved Sat, 21 Nov 2009 13:04:03 +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/21/t1258805031fbtpzjogdtwtivv.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 «

269285 8.2 269829 8 270911 7.5 266844 6.8 271244 6.5 269907 6.6 271296 7.6 270157 8 271322 8.1 267179 7.7 264101 7.5 265518 7.6 269419 7.8 268714 7.8 272482 7.8 268351 7.5 268175 7.5 270674 7.1 272764 7.5 272599 7.5 270333 7.6 270846 7.7 270491 7.7 269160 7.9 274027 8.1 273784 8.2 276663 8.2 274525 8.2 271344 7.9 271115 7.3 270798 6.9 273911 6.6 273985 6.7 271917 6.9 273338 7 270601 7.1 273547 7.2 275363 7.1 281229 6.9 277793 7 279913 6.8 282500 6.4 280041 6.7 282166 6.6 290304 6.4 283519 6.3 287816 6.2 285226 6.5 287595 6.8 289741 6.8 289148 6.4 288301 6.1 290155 5.8 289648 6.1 288225 7.2 289351 7.3 294735 6.9 305333 6.1 309030 5.8 310215 6.2 321935 7.1 325734 7.7 320846 7.9 323023 7.7 319753 7.4 321753 7.5 320757 8 324479 8.1

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 311562.654532305 -3981.77595628416X[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) 311562.654532305 23351.994457 13.342 0 0 X -3981.77595628416 3229.946175 -1.2328 0.222035 0.111018 Multiple Linear Regression - Regression Statistics Multiple R 0.150025894245578 R-squared 0.0225077689441853 Adjusted R-squared 0.00769728059485475 F-TEST (value) 1.51971821680024 F-TEST (DF numerator) 1 F-TEST (DF denominator) 66 p-value 0.222035285073425 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 17477.5607884229 Sum Squared Residuals 20160698653.4591 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 269285 278912.091690775 -9627.09169077527 2 269829 279708.446882032 -9879.44688203148 3 270911 281699.334860174 -10788.3348601736 4 266844 284486.578029572 -17642.5780295725 5 271244 285681.110816458 -14437.1108164577 6 269907 285282.933220829 -15375.9332208293 7 271296 281301.157264545 -10005.1572645451 8 270157 279708.446882032 -9551.44688203148 9 271322 279310.269286403 -7988.26928640307 10 267179 280902.979668917 -13723.9796689167 11 264101 281699.334860174 -17598.3348601736 12 265518 281301.157264545 -15783.1572645451 13 269419 280504.802073288 -11085.8020732883 14 268714 280504.802073288 -11790.8020732883 15 272482 280504.802073288 -8022.80207328832 16 268351 281699.334860174 -13348.3348601736 17 268175 281699.334860174 -13524.3348601736 18 270674 283292.045242687 -12618.0452426872 19 272764 281699.334860174 -8935.33486017357 20 272599 281699.334860174 -9100.33486017357 21 270333 281301.157264545 -10968.1572645451 22 270846 280902.979668917 -10056.9796689167 23 270491 280902.979668917 -10411.9796689167 24 269160 280106.62447766 -10946.6244776599 25 274027 279310.269286403 -5283.26928640307 26 273784 278912.091690775 -5128.09169077465 27 276663 278912.091690775 -2249.09169077465 28 274525 278912.091690775 -4387.09169077465 29 271344 280106.62447766 -8762.6244776599 30 271115 282495.690051430 -11380.6900514304 31 270798 284088.400433944 -13290.4004339441 32 273911 285282.933220829 -11371.9332208293 33 273985 284884.755625201 -10899.7556252009 34 271917 284088.400433944 -12171.4004339441 35 273338 283690.222838316 -10352.2228383156 36 270601 283292.045242687 -12691.0452426872 37 273547 282893.867647059 -9346.86764705881 38 275363 283292.045242687 -7929.04524268723 39 281229 284088.400433944 -2859.40043394406 40 277793 283690.222838316 -5897.22283831565 41 279913 284486.578029572 -4573.57802957248 42 282500 286079.288412086 -3579.28841208614 43 280041 284884.755625201 -4843.7556252009 44 282166 285282.933220829 -3116.93322082931 45 290304 286079.288412086 4224.71158791386 46 283519 286477.466007715 -2958.46600771456 47 287816 286875.643603343 940.356396657022 48 285226 285681.110816458 -455.110816457729 49 287595 284486.578029572 3108.42197042752 50 289741 284486.578029572 5254.42197042752 51 289148 286079.288412086 3068.71158791386 52 288301 287273.821198971 1027.17880102860 53 290155 288468.353985857 1686.64601414335 54 289648 287273.821198971 2374.1788010286 55 288225 282893.867647059 5331.13235294119 56 289351 282495.690051430 6855.3099485696 57 294735 284088.400433944 10646.5995660559 58 305333 287273.821198971 18059.1788010286 59 309030 288468.353985857 20561.6460141434 60 310215 286875.643603343 23339.3563966570 61 321935 283292.045242687 38642.9547573128 62 325734 280902.979668917 44831.0203310833 63 320846 280106.62447766 40739.3755223401 64 323023 280902.979668917 42120.0203310833 65 319753 282097.512455802 37655.487544198 66 321753 281699.334860174 40053.6651398264 67 320757 279708.446882032 41048.5531179685 68 324479 279310.269286403 45168.7307135969 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0020058487269281 0.0040116974538562 0.997994151273072 6 0.000190158156546950 0.000380316313093899 0.999809841843453 7 2.40151084270120e-05 4.80302168540239e-05 0.999975984891573 8 2.01599156761962e-06 4.03198313523925e-06 0.999997984008432 9 2.07716965778153e-07 4.15433931556306e-07 0.999999792283034 10 8.36675606699323e-08 1.67335121339865e-07 0.99999991633244 11 2.90416468428217e-07 5.80832936856435e-07 0.999999709583532 12 1.16086187489854e-07 2.32172374979708e-07 0.999999883913812 13 1.65156872502482e-08 3.30313745004964e-08 0.999999983484313 14 2.29259196724410e-09 4.58518393448820e-09 0.999999997707408 15 7.85498430792042e-10 1.57099686158408e-09 0.999999999214502 16 1.19213602012867e-10 2.38427204025734e-10 0.999999999880786 17 1.84268032240312e-11 3.68536064480624e-11 0.999999999981573 18 3.28480449430806e-12 6.56960898861613e-12 0.999999999996715 19 1.37275567845065e-12 2.74551135690130e-12 0.999999999998627 20 4.6274258030726e-13 9.2548516061452e-13 0.999999999999537 21 7.44665881053109e-14 1.48933176210622e-13 0.999999999999926 22 1.30489044567176e-14 2.60978089134353e-14 0.999999999999987 23 2.19793466628213e-15 4.39586933256425e-15 0.999999999999998 24 3.97641717920781e-16 7.95283435841563e-16 1 25 3.21939586256910e-16 6.43879172513821e-16 1 26 1.73058940387907e-16 3.46117880775814e-16 1 27 5.05857902088659e-16 1.01171580417732e-15 1 28 3.32244128979549e-16 6.64488257959098e-16 1 29 1.60675717780487e-16 3.21351435560974e-16 1 30 7.14070191491346e-17 1.42814038298269e-16 1 31 2.85025700691607e-17 5.70051401383214e-17 1 32 3.63350958092215e-17 7.2670191618443e-17 1 33 3.10732990803252e-17 6.21465981606503e-17 1 34 1.56649510135539e-17 3.13299020271078e-17 1 35 1.34404610214585e-17 2.68809220429171e-17 1 36 1.43247123692562e-17 2.86494247385124e-17 1 37 3.41850188700562e-17 6.83700377401124e-17 1 38 1.84743284355229e-16 3.69486568710459e-16 1 39 2.28638158201404e-14 4.57276316402808e-14 0.999999999999977 40 2.12038543749753e-13 4.24077087499505e-13 0.999999999999788 41 2.31853880557266e-12 4.63707761114533e-12 0.999999999997681 42 1.42616669644684e-11 2.85233339289369e-11 0.999999999985738 43 6.67973670179142e-11 1.33594734035828e-10 0.999999999933203 44 3.43503798950823e-10 6.87007597901645e-10 0.999999999656496 45 9.54479023843408e-09 1.90895804768682e-08 0.99999999045521 46 1.20432240071567e-08 2.40864480143134e-08 0.999999987956776 47 2.02752176780964e-08 4.05504353561929e-08 0.999999979724782 48 4.58274016737668e-08 9.16548033475336e-08 0.999999954172598 49 3.87487338080827e-07 7.74974676161653e-07 0.999999612512662 50 3.46052429744490e-06 6.92104859488981e-06 0.999996539475703 51 6.92324455975777e-06 1.38464891195155e-05 0.99999307675544 52 7.11212649500533e-06 1.42242529900107e-05 0.999992887873505 53 4.631222671657e-06 9.262445343314e-06 0.999995368777328 54 6.82453739931599e-06 1.36490747986320e-05 0.9999931754626 55 0.000850554918636385 0.00170110983727277 0.999149445081364 56 0.195392974256281 0.390785948512562 0.80460702574372 57 0.988849041097086 0.0223019178058276 0.0111509589029138 58 0.996657180869081 0.00668563826183742 0.00334281913091871 59 0.99411788458931 0.0117642308213789 0.00588211541068947 60 0.998221925211146 0.00355614957770775 0.00177807478885388 61 0.997732913659855 0.00453417268028951 0.00226708634014476 62 0.99913820035562 0.00172359928876114 0.000861799644380572 63 0.996833657501418 0.00633268499716341 0.00316634249858170 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 56 0.949152542372881 NOK 5% type I error level 58 0.983050847457627 NOK 10% type I error level 58 0.983050847457627 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805031fbtpzjogdtwtivv/10nzyh1258804002.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805031fbtpzjogdtwtivv/10nzyh1258804002.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805031fbtpzjogdtwtivv/1fj2n1258804002.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805031fbtpzjogdtwtivv/1fj2n1258804002.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805031fbtpzjogdtwtivv/2heut1258804002.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805031fbtpzjogdtwtivv/2heut1258804002.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805031fbtpzjogdtwtivv/3cylv1258804002.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805031fbtpzjogdtwtivv/3cylv1258804002.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805031fbtpzjogdtwtivv/4etd01258804002.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805031fbtpzjogdtwtivv/4etd01258804002.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805031fbtpzjogdtwtivv/58fke1258804002.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805031fbtpzjogdtwtivv/58fke1258804002.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805031fbtpzjogdtwtivv/6jc4f1258804002.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805031fbtpzjogdtwtivv/6jc4f1258804002.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805031fbtpzjogdtwtivv/7gut21258804002.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805031fbtpzjogdtwtivv/7gut21258804002.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805031fbtpzjogdtwtivv/8zuez1258804002.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805031fbtpzjogdtwtivv/8zuez1258804002.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805031fbtpzjogdtwtivv/99j691258804002.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258805031fbtpzjogdtwtivv/99j691258804002.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