Multivariate regressie calculator

*The author of this computation has been verified*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Thu, 19 Nov 2009 01:22:38 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/19/t1258619281ury9902malyxgs3.htm/, Retrieved Thu, 19 Nov 2009 09:28:15 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Nov/19/t1258619281ury9902malyxgs3.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 «

121.6 0 118.8 0 114.0 1 111.5 1 97.2 1 102.5 1 113.4 1 109.8 1 104.9 1 126.1 1 80.0 1 96.8 1 117.2 1 112.3 1 117.3 1 111.1 0 102.2 0 104.3 0 122.9 0 107.6 0 121.3 0 131.5 0 89.0 0 104.4 0 128.9 0 135.9 0 133.3 0 121.3 0 120.5 0 120.4 0 137.9 0 126.1 0 133.2 0 151.1 0 105.0 0 119.0 0 140.4 0 156.6 0 137.1 0 122.7 0 125.8 0 139.3 0 134.9 0 149.2 1 132.3 0 149.0 1 117.2 1 119.6 1 152.0 1 149.4 1 127.3 1 114.1 1 102.1 1 107.7 1 104.4 1 102.1 1 96.0 1 109.3 1 90.0 1 83.9 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 9 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Promet[t] = + 124.4 -11.4241379310345Dummy[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) 124.4 3.015906 41.248 0 0 Dummy -11.4241379310345 4.338049 -2.6335 0.010817 0.005408 Multiple Linear Regression - Regression Statistics Multiple R 0.32680517137013 R-squared 0.106801620034260 Adjusted R-squared 0.0914016479658856 F-TEST (value) 6.93518271072625 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0.0108165836382075 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 16.7918561457057 Sum Squared Residuals 16354.0531034483 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 121.6 124.4 -2.7999999999999 2 118.8 124.4 -5.59999999999997 3 114 112.975862068966 1.02413793103448 4 111.5 112.975862068966 -1.47586206896552 5 97.2 112.975862068966 -15.7758620689655 6 102.5 112.975862068966 -10.4758620689655 7 113.4 112.975862068966 0.42413793103449 8 109.8 112.975862068966 -3.17586206896552 9 104.9 112.975862068966 -8.0758620689655 10 126.1 112.975862068966 13.1241379310345 11 80 112.975862068966 -32.9758620689655 12 96.8 112.975862068966 -16.1758620689655 13 117.2 112.975862068966 4.22413793103449 14 112.3 112.975862068966 -0.675862068965519 15 117.3 112.975862068966 4.32413793103448 16 111.1 124.4 -13.3 17 102.2 124.4 -22.2 18 104.3 124.4 -20.1 19 122.9 124.4 -1.5 20 107.6 124.4 -16.8 21 121.3 124.4 -3.10000000000001 22 131.5 124.4 7.1 23 89 124.4 -35.4 24 104.4 124.4 -20 25 128.9 124.4 4.5 26 135.9 124.4 11.5 27 133.3 124.4 8.9 28 121.3 124.4 -3.10000000000001 29 120.5 124.4 -3.90000000000001 30 120.4 124.4 -4 31 137.9 124.4 13.5 32 126.1 124.4 1.69999999999999 33 133.2 124.4 8.79999999999998 34 151.1 124.4 26.7 35 105 124.4 -19.4 36 119 124.4 -5.40000000000001 37 140.4 124.4 16 38 156.6 124.4 32.2 39 137.1 124.4 12.7 40 122.7 124.4 -1.70000000000000 41 125.8 124.4 1.39999999999999 42 139.3 124.4 14.9 43 134.9 124.4 10.5 44 149.2 112.975862068966 36.2241379310345 45 132.3 124.4 7.9 46 149 112.975862068966 36.0241379310345 47 117.2 112.975862068966 4.22413793103449 48 119.6 112.975862068966 6.62413793103448 49 152 112.975862068966 39.0241379310345 50 149.4 112.975862068966 36.4241379310345 51 127.3 112.975862068966 14.3241379310345 52 114.1 112.975862068966 1.12413793103448 53 102.1 112.975862068966 -10.8758620689655 54 107.7 112.975862068966 -5.27586206896551 55 104.4 112.975862068966 -8.5758620689655 56 102.1 112.975862068966 -10.8758620689655 57 96 112.975862068966 -16.9758620689655 58 109.3 112.975862068966 -3.67586206896552 59 90 112.975862068966 -22.9758620689655 60 83.9 112.975862068966 -29.0758620689655 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0939902350407477 0.187980470081495 0.906009764959252 6 0.039226137632197 0.078452275264394 0.960773862367803 7 0.0196305945093801 0.0392611890187603 0.98036940549062 8 0.00667414288489343 0.0133482857697869 0.993325857115107 9 0.00231257681564677 0.00462515363129354 0.997687423184353 10 0.0116286147191797 0.0232572294383593 0.98837138528082 11 0.120958204106533 0.241916408213066 0.879041795893467 12 0.095980125353469 0.191960250706938 0.904019874646531 13 0.0757102056385647 0.151420411277129 0.924289794361435 14 0.0483690034528477 0.0967380069056953 0.951630996547152 15 0.0352310275067262 0.0704620550134524 0.964768972493274 16 0.024391017609826 0.048782035219652 0.975608982390174 17 0.0252511568283339 0.0505023136566677 0.974748843171666 18 0.0203398035027947 0.0406796070055895 0.979660196497205 19 0.015166849075496 0.030333698150992 0.984833150924504 20 0.0108320007467589 0.0216640014935178 0.98916799925324 21 0.00725367810343577 0.0145073562068715 0.992746321896564 22 0.00826849796271445 0.0165369959254289 0.991731502037286 23 0.0381984537522667 0.0763969075045335 0.961801546247733 24 0.0380777494208406 0.0761554988416811 0.96192225057916 25 0.0367641710719259 0.0735283421438517 0.963235828928074 26 0.0467182238031297 0.0934364476062594 0.95328177619687 27 0.0455435735306809 0.0910871470613618 0.95445642646932 28 0.0321449776512332 0.0642899553024664 0.967855022348767 29 0.0223929576451614 0.0447859152903227 0.977607042354839 30 0.0154752036853188 0.0309504073706376 0.984524796314681 31 0.017191535844247 0.034383071688494 0.982808464155753 32 0.0117012339220897 0.0234024678441793 0.98829876607791 33 0.00925259510631792 0.0185051902126358 0.990747404893682 34 0.0232151234027526 0.0464302468055051 0.976784876597247 35 0.0322136910459226 0.0644273820918451 0.967786308954077 36 0.0249951142329334 0.0499902284658669 0.975004885767067 37 0.023544676644929 0.047089353289858 0.976455323355071 38 0.0591536349816425 0.118307269963285 0.940846365018357 39 0.0464669571657812 0.0929339143315623 0.953533042834219 40 0.0322210981439874 0.0644421962879748 0.967778901856013 41 0.0215553633977488 0.0431107267954975 0.978444636602251 42 0.0163199943813772 0.0326399887627544 0.983680005618623 43 0.0106675301307135 0.0213350602614269 0.989332469869286 44 0.0480117725883037 0.0960235451766075 0.951988227411696 45 0.0312996057356753 0.0625992114713506 0.968700394264325 46 0.105796586506138 0.211593173012276 0.894203413493862 47 0.0716986003852237 0.143397200770447 0.928301399614776 48 0.0482531454457816 0.0965062908915633 0.951746854554218 49 0.239642722424996 0.479285444849992 0.760357277575004 50 0.776972366306014 0.446055267387971 0.223027633693986 51 0.912437622040365 0.175124755919270 0.0875623779596351 52 0.917728271410407 0.164543457179185 0.0822717285895927 53 0.852323028364514 0.295353943270972 0.147676971635486 54 0.796587032969806 0.406825934060388 0.203412967030194 55 0.690384406450747 0.619231187098506 0.309615593549253 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0196078431372549 NOK 5% type I error level 21 0.411764705882353 NOK 10% type I error level 37 0.725490196078431 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258619281ury9902malyxgs3/10yg0y1258618948.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258619281ury9902malyxgs3/10yg0y1258618948.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258619281ury9902malyxgs3/1i64j1258618947.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258619281ury9902malyxgs3/1i64j1258618947.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258619281ury9902malyxgs3/2eegc1258618948.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258619281ury9902malyxgs3/2eegc1258618948.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258619281ury9902malyxgs3/35lh21258618948.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258619281ury9902malyxgs3/35lh21258618948.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258619281ury9902malyxgs3/4tfq21258618948.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258619281ury9902malyxgs3/4tfq21258618948.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258619281ury9902malyxgs3/5m72d1258618948.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258619281ury9902malyxgs3/5m72d1258618948.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258619281ury9902malyxgs3/6l1651258618948.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258619281ury9902malyxgs3/6l1651258618948.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258619281ury9902malyxgs3/76v9h1258618948.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258619281ury9902malyxgs3/76v9h1258618948.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258619281ury9902malyxgs3/8s9rq1258618948.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258619281ury9902malyxgs3/8s9rq1258618948.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258619281ury9902malyxgs3/9fqgc1258618948.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258619281ury9902malyxgs3/9fqgc1258618948.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