*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: Wed, 18 Nov 2009 10:29:14 -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/18/t1258565445a8959vf10siyn9q.htm/, Retrieved Wed, 18 Nov 2009 18:30:57 +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/18/t1258565445a8959vf10siyn9q.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:

WS7

Dataseries X:

» Textbox « » Textfile « » CSV «

7357 4922 7862 8031 6820 7291 7213 4879 7357 7862 8031 6820 7079 4853 7213 7357 7862 8031 7012 4545 7079 7213 7357 7862 7319 4733 7012 7079 7213 7357 8148 5191 7319 7012 7079 7213 7599 4983 8148 7319 7012 7079 6908 4593 7599 8148 7319 7012 7878 4656 6908 7599 8148 7319 7407 4513 7878 6908 7599 8148 7911 4857 7407 7878 6908 7599 7323 4681 7911 7407 7878 6908 7179 4897 7323 7911 7407 7878 6758 4547 7179 7323 7911 7407 6934 4692 6758 7179 7323 7911 6696 4390 6934 6758 7179 7323 7688 5341 6696 6934 6758 7179 8296 5415 7688 6696 6934 6758 7697 4890 8296 7688 6696 6934 7907 5120 7697 8296 7688 6696 7592 4422 7907 7697 8296 7688 7710 4797 7592 7907 7697 8296 9011 5689 7710 7592 7907 7697 8225 5171 9011 7710 7592 7907 7733 4265 8225 9011 7710 7592 8062 5215 7733 8225 9011 7710 7859 4874 8062 7733 8225 9011 8221 4590 7859 8062 7733 8225 8330 4994 8221 7859 8062 7733 8868 4988 8330 8221 7859 8062 9053 5110 8868 8330 8221 7859 8811 5141 9053 8868 8330 8221 8120 4395 8811 9053 8868 8330 7 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation UitEu[t] = + 1328.02804198041 + 0.462614678524535UitnietEU[t] + 0.247069152859301Y1[t] + 0.26469216676593Y2[t] + 0.0735500539413867Y3[t] -0.116615043335462Y4[t] -22.1976639065588M1[t] -89.166980723682M2[t] + 148.942807094104M3[t] + 303.475954632116M4[t] + 355.970057199878M5[t] + 834.309587042934M6[t] + 442.887565246856M7[t] -169.601359213052M8[t] + 423.025057139143M9[t] -24.1838037625282M10[t] + 686.388612891013M11[t] + 20.2978188290086t + 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) 1328.02804198041 702.754994 1.8897 0.064597 0.032298 UitnietEU 0.462614678524535 0.123934 3.7328 0.000485 0.000243 Y1 0.247069152859301 0.121292 2.037 0.046963 0.023482 Y2 0.26469216676593 0.128105 2.0662 0.044009 0.022004 Y3 0.0735500539413867 0.128175 0.5738 0.56866 0.28433 Y4 -0.116615043335462 0.122405 -0.9527 0.345328 0.172664 M1 -22.1976639065588 285.232341 -0.0778 0.938279 0.46914 M2 -89.166980723682 257.743286 -0.346 0.73083 0.365415 M3 148.942807094104 241.97191 0.6155 0.540992 0.270496 M4 303.475954632116 232.906966 1.303 0.198546 0.099273 M5 355.970057199878 228.06213 1.5608 0.124868 0.062434 M6 834.309587042934 230.776656 3.6152 0.000697 0.000348 M7 442.887565246856 230.404844 1.9222 0.060286 0.030143 M8 -169.601359213052 257.006612 -0.6599 0.51234 0.25617 M9 423.025057139143 271.987089 1.5553 0.126179 0.06309 M10 -24.1838037625282 247.643326 -0.0977 0.922596 0.461298 M11 686.388612891013 281.167585 2.4412 0.018221 0.009111 t 20.2978188290086 7.613552 2.666 0.010311 0.005156 Multiple Linear Regression - Regression Statistics Multiple R 0.982961362758252 R-squared 0.96621304067556 Adjusted R-squared 0.95472547450525 F-TEST (value) 84.1094646464612 F-TEST (DF numerator) 17 F-TEST (DF denominator) 50 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 335.87177104827 Sum Squared Residuals 5640492.32935508 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 7357 7322.68920259903 34.3107974009748 2 7213 7230.61717579098 -17.6171757909846 3 7079 7154.09852197227 -75.0985219722706 4 7012 7097.78639393959 -85.7863939395869 5 7319 7253.82688042761 65.1731195723882 6 8148 8029.39446563056 118.605534369439 7 7599 7858.82579464077 -259.82579464077 8 6908 7200.39688017798 -292.396880177983 9 7878 7551.59723233938 326.402767660618 10 7407 6978.23423173706 428.765768262944 11 7911 8021.82431891594 -110.824318915938 12 7323 7426.0907311959 -103.090731195901 13 7179 7364.48517940662 -185.485179406617 14 6758 7056.67650446227 -298.676504462271 15 6934 7138.01004056845 -204.010040568454 16 6696 7163.15858042954 -467.158580429539 17 7688 7649.50841760441 38.4915823955855 18 8296 8426.51485917134 -130.514859171344 19 7697 8187.28146388406 -490.28146388406 20 7907 7815.14618296846 91.8538170315371 21 7592 7927.53559655479 -335.535596554787 22 7710 7536.90520214014 173.094797859861 23 9011 8711.50178065834 299.498219341663 24 8225 8110.78980057697 114.210199423031 25 7733 7885.34185658701 -152.341856587009 26 8062 8030.47628197656 31.5237180234350 27 7859 7872.662974711 -13.6629747109992 28 8221 8008.51285473512 212.487145264884 29 8330 8385.48019880515 -55.4801988051456 30 8868 8950.79395122953 -82.7939512295247 31 9053 8848.18136278212 204.818637217880 32 8811 8424.2458013767 386.754198623304 33 8120 8708.09569153524 -588.095691535243 34 7953 8056.48690599527 -103.486905995268 35 8878 8886.04870292889 -8.04870292889158 36 8601 8408.98530265846 192.014697341540 37 8361 8712.38821532068 -351.388215320676 38 9116 8690.46371904218 425.536280957824 39 9310 8849.26674162785 460.733258372152 40 9891 9334.17137550387 556.828624496133 41 10147 9854.69562853072 292.304371469281 42 10317 10382.3273563766 -65.327356376565 43 10682 10278.9345412208 403.065458779167 44 10276 9725.81012115537 550.189878844625 45 10614 10088.6927133518 525.30728664822 46 9413 9761.88875122688 -348.888751226878 47 11068 10722.8704495087 345.129550491317 48 9772 9911.42544639346 -139.425446393460 49 10350 10161.0173091432 188.982690856759 50 10541 10285.5304185084 255.469581491643 51 10049 10149.088007252 -100.088007251992 52 10714 10503.9264017425 210.073598257548 53 10759 10696.6823472326 62.3176527673875 54 11684 11308.7317144751 375.268285524856 55 11462 11407.8611317655 54.1388682344847 56 10485 10808.3661725801 -323.366172580090 57 11056 10984.0787662188 71.921233781192 58 10184 10333.4849089007 -149.484908900659 59 11082 11607.7547479882 -525.754747988151 60 10554 10617.7087191752 -63.7087191752088 61 11315 10849.0782369434 465.921763056568 62 10847 11243.2359002196 -396.235900219646 63 11104 11171.8737138684 -67.8737138684364 64 11026 11452.4443936494 -426.444393649438 65 11073 11475.8065273995 -402.806527399496 66 12073 12288.2376531169 -215.237653116862 67 12328 12239.9157057067 88.0842942932987 68 11172 11585.0348417414 -413.034841741395 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.139635969443483 0.279271938886966 0.860364030556517 22 0.0926634927329105 0.185326985465821 0.90733650726709 23 0.0407849374580868 0.0815698749161736 0.959215062541913 24 0.0283426806283062 0.0566853612566123 0.971657319371694 25 0.0602243438456512 0.120448687691302 0.939775656154349 26 0.0334200142365550 0.0668400284731099 0.966579985763445 27 0.0233336481627324 0.0466672963254648 0.976666351837268 28 0.0314090666608677 0.0628181333217354 0.968590933339132 29 0.0235275427995504 0.0470550855991008 0.97647245720045 30 0.0156690400507477 0.0313380801014955 0.984330959949252 31 0.0165183613924538 0.0330367227849076 0.983481638607546 32 0.0103194060591099 0.0206388121182199 0.98968059394089 33 0.0896537731922097 0.179307546384419 0.91034622680779 34 0.107296344264843 0.214592688529685 0.892703655735157 35 0.0836371838824764 0.167274367764953 0.916362816117524 36 0.0559164640276253 0.111832928055251 0.944083535972375 37 0.292251122776516 0.584502245553032 0.707748877223484 38 0.284386440111125 0.56877288022225 0.715613559888875 39 0.324933232854005 0.64986646570801 0.675066767145995 40 0.236510501947915 0.47302100389583 0.763489498052085 41 0.200926126709937 0.401852253419873 0.799073873290064 42 0.341677811559063 0.683355623118125 0.658322188440937 43 0.558136214143336 0.883727571713327 0.441863785856664 44 0.445823168825131 0.891646337650262 0.554176831174869 45 0.348893920630311 0.697787841260623 0.651106079369689 46 0.432679572641439 0.865359145282877 0.567320427358561 47 0.481204567428644 0.96240913485729 0.518795432571356 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 5 0.185185185185185 NOK 10% type I error level 9 0.333333333333333 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258565445a8959vf10siyn9q/10limf1258565350.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258565445a8959vf10siyn9q/10limf1258565350.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258565445a8959vf10siyn9q/1z9u01258565350.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258565445a8959vf10siyn9q/1z9u01258565350.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258565445a8959vf10siyn9q/2y5te1258565350.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258565445a8959vf10siyn9q/2y5te1258565350.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258565445a8959vf10siyn9q/3x7511258565350.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258565445a8959vf10siyn9q/3x7511258565350.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258565445a8959vf10siyn9q/4grxx1258565350.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258565445a8959vf10siyn9q/4grxx1258565350.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258565445a8959vf10siyn9q/5gfw71258565350.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258565445a8959vf10siyn9q/5gfw71258565350.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258565445a8959vf10siyn9q/65bqh1258565350.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258565445a8959vf10siyn9q/65bqh1258565350.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258565445a8959vf10siyn9q/74ejw1258565350.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258565445a8959vf10siyn9q/74ejw1258565350.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258565445a8959vf10siyn9q/8nyts1258565350.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258565445a8959vf10siyn9q/8nyts1258565350.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258565445a8959vf10siyn9q/9h2g41258565350.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258565445a8959vf10siyn9q/9h2g41258565350.ps (open in new window)

Parameters (Session):

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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