Multiple Regression zonder LT-trend

*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: Fri, 18 Dec 2009 10:57:07 -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/Dec/18/t1261159121tww3jzxwwbzebk3.htm/, Retrieved Fri, 18 Dec 2009 18:58:53 +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/18/t1261159121tww3jzxwwbzebk3.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 «

113 14.3 110 14.2 107 15.9 103 15.3 98 15.5 98 15.1 137 15 148 12.1 147 15.8 139 16.9 130 15.1 128 13.7 127 14.8 123 14.7 118 16 114 15.4 108 15 111 15.5 151 15.1 159 11.7 158 16.3 148 16.7 138 15 137 14.9 136 14.6 133 15.3 126 17.9 120 16.4 114 15.4 116 17.9 153 15.9 162 13.9 161 17.8 149 17.9 139 17.4 135 16.7 130 16 127 16.6 122 19.1 117 17.8 112 17.2 113 18.6 149 16.3 157 15.1 157 19.2 147 17.7 137 19.1 132 18 125 17.5 123 17.8 117 21.1 114 17.2 111 19.4 112 19.8 144 17.6 150 16.2 149 19.5 134 19.9 123 20 116 17.3

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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation WK<25j[t] = + 165.733743201702 -2.98888626152753ExpBE[t] -1.75138330574603M1[t] -4.24913691179949M2[t] -2.96911799479788M3[t] -12.4262000472925M4[t] -17.5217309056515M5[t] -13.8261527547884M6[t] + 18.4547647197919M7[t] + 20.0043509103807M8[t] + 30.5861432962875M9[t] + 19.5503901631591M10[t] + 7.7213052731142M11[t] + 0.334641759281152t + 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) 165.733743201702 18.800964 8.8152 0 0 ExpBE -2.98888626152753 1.403729 -2.1292 0.038618 0.019309 M1 -1.75138330574603 4.264698 -0.4107 0.683221 0.341611 M2 -4.24913691179949 4.287849 -0.991 0.326884 0.163442 M3 -2.96911799479788 5.61351 -0.5289 0.599402 0.299701 M4 -12.4262000472925 4.444025 -2.7962 0.007522 0.003761 M5 -17.5217309056515 4.438739 -3.9475 0.000268 0.000134 M6 -13.8261527547884 4.891554 -2.8265 0.006941 0.003471 M7 18.4547647197919 4.241027 4.3515 7.5e-05 3.7e-05 M8 20.0043509103807 5.062632 3.9514 0.000265 0.000133 M9 30.5861432962875 4.95176 6.1768 0 0 M10 19.5503901631591 4.963575 3.9388 0.000276 0.000138 M11 7.7213052731142 4.584688 1.6842 0.098927 0.049464 t 0.334641759281152 0.126068 2.6545 0.010873 0.005437 Multiple Linear Regression - Regression Statistics Multiple R 0.939979664256332 R-squared 0.883561769215446 Adjusted R-squared 0.850655312689376 F-TEST (value) 26.8507114558341 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6.666554877216 Sum Squared Residuals 2044.37588082289 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 113 121.575928115394 -8.57592811539369 2 110 119.711704894774 -9.7117048947742 3 107 116.245258926460 -9.24525892646015 4 103 108.916150390163 -5.91615039016317 5 98 103.557484038780 -5.55748403877987 6 98 108.783258453535 -10.7832584535351 7 137 141.697706313549 -4.69770631354935 8 148 152.249704421849 -4.24970442184912 9 147 152.107259399385 -5.10725939938521 10 139 138.118373137858 0.881626862142338 11 130 132.003925277843 -2.00392527784344 12 128 128.801702530149 -0.801702530148966 13 127 124.097186096004 2.90281390399620 14 123 122.232962875384 0.767037124615754 15 118 119.962071411681 -1.96207141168123 16 114 112.632962875384 1.36703712461575 17 108 109.067628280917 -1.06762828091747 18 111 111.603405060298 -0.603405060297926 19 151 145.414518798770 5.58548120122961 20 159 157.460960037834 1.53903996216601 21 158 154.628517379995 3.37148262000474 22 148 142.731851501537 5.26814849846299 23 138 136.31851501537 1.68148498462995 24 137 129.230740127690 7.76925987231025 25 136 128.710664459683 7.28933554031686 26 133 124.455332229842 8.54466777015844 27 126 118.298888626153 7.70111137384724 28 120 113.659777725231 6.34022227476945 29 114 111.887774887680 2.11222511231972 30 116 108.445779144006 7.55422085599431 31 153 147.039110900922 5.9608890990778 32 162 154.901111373847 7.09888862615275 33 161 154.160889099078 6.83911090092221 34 149 143.160889099078 5.8391109009222 35 139 133.160889099078 5.8391109009222 36 135 127.866445968314 7.13355403168597 37 130 128.541924804918 1.45807519508158 38 127 124.585481201230 2.4145187987704 39 122 118.727926223694 3.27207377630646 40 117 113.491038070466 3.50896192953416 41 112 110.523480728305 1.47651927169544 42 113 110.369259872310 2.63074012768976 43 149 149.859257507685 -0.859257507685013 44 157 155.330148971388 1.66985102861196 45 157 153.992149444313 3.00785055568692 46 147 147.774367462757 -0.774367462757131 47 137 132.095483565855 4.90451643414517 48 132 127.996594939702 4.00340506029793 49 125 128.074296524001 -3.07429652400096 50 123 125.014518798770 -2.01451879877039 51 117 116.765854812012 0.234145187987687 52 114 119.300070938756 -5.30007093875619 53 111 107.963632064318 3.03636793568217 54 112 110.798297469851 1.20170253014896 55 144 149.989406479073 -5.98940647907305 56 150 156.058075195082 -6.05807519508159 57 149 157.111184677229 -8.11118467722866 58 134 145.214518798770 -11.2145187987704 59 123 133.421187041854 -10.4211870418539 60 116 134.104516434145 -18.1045164341452 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00589557865258369 0.0117911573051674 0.994104421347416 18 0.00111266102964024 0.00222532205928048 0.99888733897036 19 0.00349789206442785 0.00699578412885571 0.996502107935572 20 0.00101001320057903 0.00202002640115805 0.998989986799421 21 0.00167211238484181 0.00334422476968363 0.998327887615158 22 0.00202615177534028 0.00405230355068056 0.99797384822466 23 0.00374422469235083 0.00748844938470166 0.99625577530765 24 0.0133630645071658 0.0267261290143316 0.986636935492834 25 0.00621546424712366 0.0124309284942473 0.993784535752876 26 0.00270044719856279 0.00540089439712558 0.997299552801437 27 0.00307873171639656 0.00615746343279312 0.996921268283604 28 0.00923583467286824 0.0184716693457365 0.990764165327132 29 0.0265422671746758 0.0530845343493516 0.973457732825324 30 0.0310161031522474 0.0620322063044948 0.968983896847753 31 0.0760951574481819 0.152190314896364 0.923904842551818 32 0.0752806607426773 0.150561321485355 0.924719339257323 33 0.0697609239116624 0.139521847823325 0.930239076088338 34 0.149803650076296 0.299607300152592 0.850196349923704 35 0.134625996049997 0.269251992099993 0.865374003950003 36 0.138711674472809 0.277423348945619 0.861288325527191 37 0.281776439972804 0.563552879945609 0.718223560027196 38 0.308722428913063 0.617444857826125 0.691277571086937 39 0.245844774073829 0.491689548147657 0.754155225926171 40 0.364377502859451 0.728755005718903 0.635622497140549 41 0.344811488328574 0.689622976657148 0.655188511671426 42 0.513126600502159 0.973746798995682 0.486873399497841 43 0.619203462206698 0.761593075586604 0.380796537793302 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 8 0.296296296296296 NOK 5% type I error level 12 0.444444444444444 NOK 10% type I error level 14 0.518518518518518 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/18/t1261159121tww3jzxwwbzebk3/10s8i91261159021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261159121tww3jzxwwbzebk3/10s8i91261159021.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261159121tww3jzxwwbzebk3/1p6mo1261159021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261159121tww3jzxwwbzebk3/1p6mo1261159021.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261159121tww3jzxwwbzebk3/2w9dt1261159021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261159121tww3jzxwwbzebk3/2w9dt1261159021.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261159121tww3jzxwwbzebk3/33bty1261159021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261159121tww3jzxwwbzebk3/33bty1261159021.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261159121tww3jzxwwbzebk3/4cuni1261159021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261159121tww3jzxwwbzebk3/4cuni1261159021.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261159121tww3jzxwwbzebk3/5t4xu1261159021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261159121tww3jzxwwbzebk3/5t4xu1261159021.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261159121tww3jzxwwbzebk3/6r0mh1261159021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261159121tww3jzxwwbzebk3/6r0mh1261159021.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261159121tww3jzxwwbzebk3/7jehp1261159021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261159121tww3jzxwwbzebk3/7jehp1261159021.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261159121tww3jzxwwbzebk3/8aeuq1261159021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261159121tww3jzxwwbzebk3/8aeuq1261159021.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261159121tww3jzxwwbzebk3/9epo41261159021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261159121tww3jzxwwbzebk3/9epo41261159021.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

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