werkloosheidscijfers(met maandelijkse dummies)

*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: Sun, 22 Nov 2009 03:52:07 -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/22/t1258888870lef65pin9bklw7y.htm/, Retrieved Sun, 22 Nov 2009 12:21:22 +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/22/t1258888870lef65pin9bklw7y.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 «

9.3 8.1 10.9 25.6 8.7 7.7 10 23.7 8.2 7.5 9.2 22 8.3 7.6 9.2 21.3 8.5 7.8 9.5 20.7 8.6 7.8 9.6 20.4 8.5 7.8 9.5 20.3 8.2 7.5 9.1 20.4 8.1 7.5 8.9 19.8 7.9 7.1 9 19.5 8.6 7.5 10.1 23.1 8.7 7.5 10.3 23.5 8.7 7.6 10.2 23.5 8.5 7.7 9.6 22.9 8.4 7.7 9.2 21.9 8.5 7.9 9.3 21.5 8.7 8.1 9.4 20.5 8.7 8.2 9.4 20.2 8.6 8.2 9.2 19.4 8.5 8.2 9 19.2 8.3 7.9 9 18.8 8 7.3 9 18.8 8.2 6.9 9.8 22.6 8.1 6.6 10 23.3 8.1 6.7 9.8 23 8 6.9 9.3 21.4 7.9 7 9 19.9 7.9 7.1 9 18.8 8 7.2 9.1 18.6 8 7.1 9.1 18.4 7.9 6.9 9.1 18.6 8 7 9.2 19.9 7.7 6.8 8.8 19.2 7.2 6.4 8.3 18.4 7.5 6.7 8.4 21.1 7.3 6.6 8.1 20.5 7 6.4 7.7 19.1 7 6.3 7.9 18.1 7 6.2 7.9 17 7.2 6.5 8 17.1 7.3 6.8 7.9 17.4 7.1 6.8 7.6 16.8 6.8 6.4 7.1 15.3 6.4 6.1 6.8 14.3 6.1 5.8 6.5 13.4 6.5 6.1 6.9 15.3 7.7 7.2 8.2 22.1 7.9 7.3 8.7 23.7 7.5 6.9 8.3 22.2 6.9 6.1 7.9 19.5 6.6 5.8 7.5 16.6 6.9 6.2 7.8 17.3 7.7 7.1 8.3 19.8 8 7.7 8.4 21.2 8 7.9 8.2 21.5 7.7 7.7 7.7 20.6 7.3 7.4 7.2 19.1 7.4 7.5 7.3 19.6 8.1 8 8.1 23.5 8.3 8. 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 TW[t] = + 0.213248198481854 + 0.527823582814739WM[t] + 0.421384268649569WV[t] + 0.00838439145702868WJ[t] + 0.00112705179224741M1[t] -0.000597572515737364M2[t] + 0.0260612098421007M3[t] + 0.0101492243658692M4[t] + 0.0331631595605292M5[t] + 0.0182520149957518M6[t] + 0.0279408241045159M7[t] + 0.0125698503514514M8[t] -0.00644616521266334M9[t] -0.0114890758015339M10[t] + 0.0275237939749787M11[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) 0.213248198481854 0.055594 3.8358 0.000379 0.00019 WM 0.527823582814739 0.013187 40.0256 0 0 WV 0.421384268649569 0.007031 59.9362 0 0 WJ 0.00838439145702868 0.00515 1.6282 0.11032 0.05516 M1 0.00112705179224741 0.019993 0.0564 0.955288 0.477644 M2 -0.000597572515737364 0.021732 -0.0275 0.978182 0.489091 M3 0.0260612098421007 0.02426 1.0742 0.288323 0.144161 M4 0.0101492243658692 0.026459 0.3836 0.703055 0.351527 M5 0.0331631595605292 0.028515 1.163 0.250819 0.12541 M6 0.0182520149957518 0.029423 0.6203 0.538098 0.269049 M7 0.0279408241045159 0.029781 0.9382 0.353037 0.176519 M8 0.0125698503514514 0.028749 0.4372 0.663997 0.331999 M9 -0.00644616521266334 0.02952 -0.2184 0.828107 0.414054 M10 -0.0114890758015339 0.027239 -0.4218 0.675145 0.337572 M11 0.0275237939749787 0.02093 1.315 0.195023 0.097511 Multiple Linear Regression - Regression Statistics Multiple R 0.999109598718977 R-squared 0.998219990252394 Adjusted R-squared 0.997678248155297 F-TEST (value) 1842.61107933234 F-TEST (DF numerator) 14 F-TEST (DF denominator) 46 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.0328059008988326 Sum Squared Residuals 0.0495064481540651 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 9.3 9.29747522065373 0.00252477934627300 2 8.7 8.68944497766688 0.0105550223331183 3 8.2 8.25917816306516 -0.059178163065164 4 8.3 8.29017946185049 0.00982053814951402 5 8.5 8.54014275932875 -0.0401427593287484 6 8.6 8.56485472419182 0.0351452758081805 7 8.5 8.53156666728992 -0.0315666672899237 8 8.2 8.19013335037831 0.00986664962168678 9 8.1 8.08180984621007 0.0181901537899325 10 7.9 7.90526061192315 -0.0052606119231483 11 8.6 8.64910941958539 -0.0491094195853866 12 8.7 8.70921623592313 -0.00921623592313388 13 8.7 8.7209872191319 -0.0209872191318976 14 8.5 8.51418375704143 -0.0141837570414279 15 8.4 8.3639044404824 0.0360955595175908 16 8.5 8.49234184185127 0.00765815814872786 17 8.7 8.6546745290168 0.0453254709831916 18 8.7 8.6900304252964 0.00996957470360378 19 8.6 8.60873486750962 -0.00873486750962285 20 8.5 8.50741016173524 -0.00741016173523876 21 8.3 8.32669331474389 -0.0266933147438904 22 8 8.00495625446618 -0.00495625446617654 23 8.2 8.20180779357316 -0.00180779357315876 24 8.1 8.10608285250359 -0.00608285250359102 25 8.1 8.0732000914103 0.0267999085897094 26 8 7.95293302300922 0.047066976990777 27 7.9 7.89338229586812 0.00661770413187936 28 7.9 7.92102983807063 -0.0210298380706314 29 8 8.03728768012032 -0.0372876801203169 30 8 7.96791729898266 0.0320827010173405 31 7.9 7.87371826981988 0.0262817301801185 32 8 7.96416779010739 0.0358322098926148 33 7.7 7.66516427650058 0.0348357234994248 34 7.2 7.2315922852954 -0.0315922852954016 35 7.5 7.49372851371527 0.00627148628472959 36 7.3 7.28197644598973 0.0180235540102706 37 7 6.99724692571936 0.00275307428063837 38 7 7.01863240540279 -0.0186324054027878 39 7 6.98328599887642 0.0167140011235795 40 7.2 7.16869795425527 0.0313020457447297 41 7.3 7.3104358548665 -0.0104358548665042 42 7.1 7.16407879483264 -0.0640787948326388 43 6.8 6.73936944930518 0.0606305506948204 44 6.4 6.43085172865579 -0.0308517286557931 45 6.1 6.11952740534106 -0.0195274053410608 46 6.5 6.45731562082479 0.0426843791752061 47 7.7 7.68174784284975 0.0182521571502456 48 7.9 7.93111356781228 -0.0311135678122797 49 7.5 7.53998089183326 -0.0399808918332619 50 6.9 6.92480583687968 -0.0248058368796796 51 6.6 6.60024910170789 -0.000249101707885629 52 6.9 6.92775090397234 -0.0277509039723402 53 7.7 7.65745917666762 0.0425408233323778 54 8 8.01311875669649 -0.0131187566964859 55 8 8.0466107460754 -0.0466107460753923 56 7.7 7.70743696912327 -0.00743696912326973 57 7.3 7.3068051572044 -0.00680515720440614 58 7.4 7.40087522749048 -0.00087522749047966 59 8.1 8.07360643027643 0.0263935697235702 60 8.3 8.27161089777127 0.028389102228734 61 8.2 8.17110965125146 0.0288903487485387 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 18 0.704931628811259 0.590136742377482 0.295068371188741 19 0.657794752056929 0.684410495886143 0.342205247943071 20 0.742970127676529 0.514059744646942 0.257029872323471 21 0.681547480598755 0.63690503880249 0.318452519401245 22 0.588829000244044 0.822341999511912 0.411170999755956 23 0.59517943820537 0.80964112358926 0.40482056179463 24 0.48685086330960 0.97370172661920 0.5131491366904 25 0.414363387261395 0.82872677452279 0.585636612738605 26 0.436529003407144 0.873058006814288 0.563470996592856 27 0.342826166813103 0.685652333626206 0.657173833186897 28 0.318678263015811 0.637356526031621 0.681321736984189 29 0.490590187246927 0.981180374493853 0.509409812753073 30 0.407191289345552 0.814382578691104 0.592808710654448 31 0.365207032858689 0.730414065717378 0.634792967141311 32 0.317737849046266 0.635475698092533 0.682262150953734 33 0.347427813889265 0.69485562777853 0.652572186110735 34 0.328966137232654 0.657932274465309 0.671033862767346 35 0.246170826586593 0.492341653173186 0.753829173413407 36 0.183402212944723 0.366804425889446 0.816597787055277 37 0.127327796660430 0.254655593320860 0.87267220333957 38 0.102094143533875 0.204188287067750 0.897905856466125 39 0.0671354334809404 0.134270866961881 0.93286456651906 40 0.047094603499404 0.094189206998808 0.952905396500596 41 0.0644317887379243 0.128863577475849 0.935568211262076 42 0.265552743825532 0.531105487651064 0.734447256174468 43 0.683346221184733 0.633307557630535 0.316653778815268 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 1 0.0384615384615385 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/22/t1258888870lef65pin9bklw7y/10zm2q1258887121.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258888870lef65pin9bklw7y/10zm2q1258887121.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258888870lef65pin9bklw7y/1j6891258887121.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258888870lef65pin9bklw7y/1j6891258887121.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258888870lef65pin9bklw7y/2i5z51258887121.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258888870lef65pin9bklw7y/2i5z51258887121.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258888870lef65pin9bklw7y/3yl7b1258887121.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258888870lef65pin9bklw7y/3yl7b1258887121.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258888870lef65pin9bklw7y/4ksl61258887121.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258888870lef65pin9bklw7y/4ksl61258887121.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258888870lef65pin9bklw7y/58p221258887121.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258888870lef65pin9bklw7y/58p221258887121.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258888870lef65pin9bklw7y/603d01258887121.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258888870lef65pin9bklw7y/603d01258887121.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258888870lef65pin9bklw7y/7lfmh1258887121.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258888870lef65pin9bklw7y/7lfmh1258887121.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258888870lef65pin9bklw7y/8ed4i1258887121.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258888870lef65pin9bklw7y/8ed4i1258887121.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258888870lef65pin9bklw7y/9ik2n1258887121.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258888870lef65pin9bklw7y/9ik2n1258887121.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') }

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