Multipe Regression werkloosheid ecogr

*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, 17 Dec 2009 13:08:53 -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/Dec/17/t12610806868wnf32gpz4u5x22.htm/, Retrieved Thu, 17 Dec 2009 21:11:38 +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/Dec/17/t12610806868wnf32gpz4u5x22.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 «

8.2 103.9 8.7 9.3 9.3 8.3 101.6 8.2 8.7 9.3 8.5 94.6 8.3 8.2 8.7 8.6 95.9 8.5 8.3 8.2 8.5 104.7 8.6 8.5 8.3 8.2 102.8 8.5 8.6 8.5 8.1 98.1 8.2 8.5 8.6 7.9 113.9 8.1 8.2 8.5 8.6 80.9 7.9 8.1 8.2 8.7 95.7 8.6 7.9 8.1 8.7 113.2 8.7 8.6 7.9 8.5 105.9 8.7 8.7 8.6 8.4 108.8 8.5 8.7 8.7 8.5 102.3 8.4 8.5 8.7 8.7 99 8.5 8.4 8.5 8.7 100.7 8.7 8.5 8.4 8.6 115.5 8.7 8.7 8.5 8.5 100.7 8.6 8.7 8.7 8.3 109.9 8.5 8.6 8.7 8 114.6 8.3 8.5 8.6 8.2 85.4 8 8.3 8.5 8.1 100.5 8.2 8 8.3 8.1 114.8 8.1 8.2 8 8 116.5 8.1 8.1 8.2 7.9 112.9 8 8.1 8.1 7.9 102 7.9 8 8.1 8 106 7.9 7.9 8 8 105.3 8 7.9 7.9 7.9 118.8 8 8 7.9 8 106.1 7.9 8 8 7.7 109.3 8 7.9 8 7.2 117.2 7.7 8 7.9 7.5 92.5 7.2 7.7 8 7.3 104.2 7.5 7.2 7.7 7 112.5 7.3 7.5 7.2 7 122.4 7 7.3 7.5 7 113.3 7 7 7.3 7.2 100 7 7 7 7.3 110.7 7.2 7 7 7.1 112.8 7.3 7.2 7 6.8 109.8 7.1 7.3 7.2 6.4 117.3 6.8 7.1 7.3 6.1 109.1 6.4 6.8 7.1 6.5 115.9 6.1 6.4 6.8 7.7 96 6.5 6.1 6.4 7.9 99.8 7.7 6.5 6.1 7.5 116.8 7.9 7.7 6.5 6.9 115.7 7.5 7.9 7.7 6.6 etc...

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] = + 4.56263427303848 -0.0175379205114101X[t] + 1.11287158351717Y1[t] -0.505979172235887Y2[t] + 0.0744444233726834Y3[t] -0.0082845241626587t + 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) 4.56263427303848 0.932637 4.8922 1e-05 5e-06 X -0.0175379205114101 0.003539 -4.9562 8e-06 4e-06 Y1 1.11287158351717 0.125019 8.9016 0 0 Y2 -0.505979172235887 0.182097 -2.7786 0.007623 0.003811 Y3 0.0744444233726834 0.127874 0.5822 0.563017 0.281508 t -0.0082845241626587 0.003277 -2.5277 0.014621 0.007311 Multiple Linear Regression - Regression Statistics Multiple R 0.946941707853524 R-squared 0.89669859807255 Adjusted R-squared 0.88657100964829 F-TEST (value) 88.5401894812963 F-TEST (DF numerator) 5 F-TEST (DF denominator) 51 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.222763915082659 Sum Squared Residuals 2.53081185501066 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.2 8.40086941991187 -0.200869419911869 2 8.3 8.1800738245084 0.119926175491602 3 8.5 8.61416483437166 -0.114164834371665 4 8.6 8.71783520133767 -0.117835201337670 5 8.5 8.5727527429164 -0.0727527429164101 6 8.2 8.45079407682466 -0.250794076824663 7 8.1 8.24911866357134 -0.149118663571338 8 7.9 7.99679714631018 -0.09679714631018 9 8.6 8.3729542725324 0.227045727467594 10 8.7 8.9778700253728 -0.277870025372803 11 8.7 8.40488474537253 0.295115254627473 12 8.5 8.52614022008045 -0.0261402200804513 13 8.4 8.25186585206854 0.148134147931461 14 8.5 8.3474864873255 0.152513512674493 15 8.7 8.54407329175127 0.155926708248730 16 8.7 8.67050625986179 0.0294937401382100 17 8.6 8.30890912002035 0.291090879979647 18 8.5 8.46378754574938 0.0362124542506163 19 8.3 8.23346491175362 0.0665350882463766 20 8 7.96333131937023 0.0366686806297745 21 8.2 8.2270439911955 -0.0270439911955003 22 8.1 8.31341605101021 -0.213416051010211 23 8.1 7.81952294372369 0.280477056276311 24 8 7.84691075658976 0.153089243410242 25 7.9 7.78303114557919 0.116968854420809 26 7.9 7.90522071386278 -0.00522071386277465 27 8 7.8699379825408 0.130062017459204 28 8 7.97777271875057 0.0222272812494275 29 7.9 7.68212835046029 0.217871649539712 30 8 7.79273270077809 0.207267299221910 31 7.7 7.89021190655422 -0.190211906554224 32 7.2 7.35147397573542 -0.151473975735419 33 7.5 7.37917849045404 0.120821509545960 34 7.3 7.73021803046917 -0.430218030469172 35 7 7.16477848600127 -0.164778486001268 36 7 6.77253623517948 0.227463764820518 37 7 7.06075165466688 -0.060751654666884 38 7.2 7.26338814629417 -0.0633881462941747 39 7.3 7.29002218936286 0.00997781063713825 40 7.1 7.25499935603078 -0.154999356030781 41 6.8 7.04104524414987 -0.241045244149867 42 6.4 6.67600511788093 -0.276005117880928 43 6.1 6.5032877755012 -0.403287775501195 44 6.5 6.22194225868835 0.278057741311654 45 7.7 7.12982696843131 0.570173031568691 46 7.9 8.16561925063973 -0.265619250639732 47 7.5 7.50436715715255 -0.00436715715254513 48 6.9 7.05836318574561 -0.158363185745613 49 6.6 6.88550436937753 -0.285504369377531 50 6.9 6.90661149876037 -0.00661149876037031 51 7.7 7.39719068498767 0.302809315012327 52 8 8.06649292383107 -0.0664929238310741 53 8 7.8359944508837 0.164005549116299 54 7.7 7.89331299835111 -0.193312998351114 55 7.3 7.61383754332135 -0.313837543321354 56 7.4 7.12103480384822 0.278965196151777 57 8.1 7.75660798219918 0.343392017800824 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 9 0.136037830364078 0.272075660728156 0.863962169635922 10 0.127742922435929 0.255485844871857 0.872257077564071 11 0.469709634607101 0.939419269214203 0.530290365392899 12 0.342688888707516 0.685377777415033 0.657311111292484 13 0.248493382167568 0.496986764335137 0.751506617832432 14 0.168846467818522 0.337692935637045 0.831153532181478 15 0.110363116340142 0.220726232680284 0.889636883659858 16 0.0673959283883964 0.134791856776793 0.932604071611604 17 0.053230723131857 0.106461446263714 0.946769276868143 18 0.0418575146189453 0.0837150292378907 0.958142485381055 19 0.0295304837754423 0.0590609675508847 0.970469516224558 20 0.0277488777441011 0.0554977554882022 0.97225112225590 21 0.0303516023019395 0.060703204603879 0.96964839769806 22 0.0412900335196967 0.0825800670393933 0.958709966480303 23 0.0309499442687708 0.0618998885375416 0.96905005573123 24 0.0197786248995422 0.0395572497990843 0.980221375100458 25 0.0132477508499499 0.0264955016998999 0.98675224915005 26 0.00948585514355937 0.0189717102871187 0.99051414485644 27 0.00567275939108807 0.0113455187821761 0.994327240608912 28 0.00348173856956286 0.00696347713912572 0.996518261430437 29 0.00346267095019548 0.00692534190039096 0.996537329049805 30 0.00409561310255039 0.00819122620510078 0.99590438689745 31 0.00669591769112131 0.0133918353822426 0.993304082308879 32 0.0145380559582981 0.0290761119165961 0.985461944041702 33 0.0191427087283043 0.0382854174566086 0.980857291271696 34 0.0267794108781365 0.0535588217562729 0.973220589121863 35 0.0278935672396622 0.0557871344793244 0.972106432760338 36 0.0635677611421891 0.127135522284378 0.936432238857811 37 0.0507479147563507 0.101495829512701 0.94925208524365 38 0.0434316243988446 0.0868632487976892 0.956568375601155 39 0.0656427405889232 0.131285481177846 0.934357259411077 40 0.0993914254407714 0.198782850881543 0.900608574559229 41 0.191813012167737 0.383626024335474 0.808186987832263 42 0.184014981637450 0.368029963274899 0.81598501836255 43 0.295186682128474 0.590373364256949 0.704813317871526 44 0.392811891247157 0.785623782494315 0.607188108752843 45 0.849322880546909 0.301354238906182 0.150677119453091 46 0.758540813577627 0.482918372844746 0.241459186422373 47 0.630603367696482 0.738793264607035 0.369396632303518 48 0.655637035277866 0.688725929444268 0.344362964722134 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 3 0.075 NOK 5% type I error level 10 0.25 NOK 10% type I error level 19 0.475 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/17/t12610806868wnf32gpz4u5x22/10kkey1261080527.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t12610806868wnf32gpz4u5x22/10kkey1261080527.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t12610806868wnf32gpz4u5x22/1m3wb1261080527.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t12610806868wnf32gpz4u5x22/1m3wb1261080527.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t12610806868wnf32gpz4u5x22/2frsw1261080527.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t12610806868wnf32gpz4u5x22/2frsw1261080527.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t12610806868wnf32gpz4u5x22/3m5zw1261080527.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t12610806868wnf32gpz4u5x22/3m5zw1261080527.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t12610806868wnf32gpz4u5x22/42gga1261080527.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t12610806868wnf32gpz4u5x22/42gga1261080527.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t12610806868wnf32gpz4u5x22/5v66c1261080527.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t12610806868wnf32gpz4u5x22/5v66c1261080527.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t12610806868wnf32gpz4u5x22/6qfsi1261080527.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t12610806868wnf32gpz4u5x22/6qfsi1261080527.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t12610806868wnf32gpz4u5x22/7vuut1261080527.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t12610806868wnf32gpz4u5x22/7vuut1261080527.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t12610806868wnf32gpz4u5x22/8dizx1261080527.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t12610806868wnf32gpz4u5x22/8dizx1261080527.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t12610806868wnf32gpz4u5x22/9nlty1261080527.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t12610806868wnf32gpz4u5x22/9nlty1261080527.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

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