*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, 20 Nov 2009 07:00:11 -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/20/t125872574307bpfiu7s1fvuea.htm/, Retrieved Fri, 20 Nov 2009 15:02:39 +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/20/t125872574307bpfiu7s1fvuea.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 «

1318 1427 1081 831 557 280 1578 934 1318 1081 831 557 1859 709 1578 1318 1081 831 2141 1186 1859 1578 1318 1081 2428 986 2141 1859 1578 1318 2715 1033 2428 2141 1859 1578 3004 1257 2715 2428 2141 1859 3309 1105 3004 2715 2428 2141 269 1179 3309 3004 2715 2428 537 1092 269 3309 3004 2715 813 1092 537 269 3309 3004 1068 1087 813 537 269 3309 1411 2028 1068 813 537 269 1675 2039 1411 1068 813 537 1958 2010 1675 1411 1068 813 2242 754 1958 1675 1411 1068 2524 760 2242 1958 1675 1411 2836 715 2524 2242 1958 1675 3143 855 2836 2524 2242 1958 3522 971 3143 2836 2524 2242 285 815 3522 3143 2836 2524 574 915 285 3522 3143 2836 865 843 574 285 3522 3143 1147 761 865 574 285 3522 1516 1858 1147 865 574 285 1789 2968 1516 1147 865 574 2087 4061 1789 1516 1147 865 2372 3661 2087 1789 1516 1147 2669 3269 2372 2087 1789 1516 2966 2857 2669 2372 2087 1789 3270 2568 2966 2669 2372 2087 3652 2274 3270 2966 2669 2372 329 1987 3652 3270 2966 2669 658 683 329 3652 3270 2966 988 381 658 329 3652 3 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 Y[t] = + 633.895997338495 + 0.00669906119430542X[t] + 0.656397458381625Y1[t] -0.0466774298093399Y2[t] -0.0549640904534444Y3[t] -0.0357103741220868Y4[t] + 37.8694134566582M1[t] + 156.142482881967M2[t] + 295.305233485109M3[t] + 437.323832062408M4[t] + 588.831879163271M5[t] + 736.098639832787M6[t] + 890.325206224805M7[t] + 1078.14615314873M8[t] -2425.28631966955M9[t] + 83.7658990470096M10[t] + 65.1693704083077M11[t] + 4.86711279747431t + 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) 633.895997338495 540.000071 1.1739 0.247747 0.123874 X 0.00669906119430542 0.01108 0.6046 0.549036 0.274518 Y1 0.656397458381625 0.163511 4.0144 0.000271 0.000135 Y2 -0.0466774298093399 0.194883 -0.2395 0.811993 0.405996 Y3 -0.0549640904534444 0.194595 -0.2825 0.779128 0.389564 Y4 -0.0357103741220868 0.15857 -0.2252 0.823028 0.411514 M1 37.8694134566582 552.399222 0.0686 0.945704 0.472852 M2 156.142482881967 541.025196 0.2886 0.774452 0.387226 M3 295.305233485109 531.179177 0.5559 0.581509 0.290754 M4 437.323832062408 533.546023 0.8197 0.417524 0.208762 M5 588.831879163271 533.396365 1.1039 0.276565 0.138283 M6 736.098639832787 541.397555 1.3596 0.181962 0.090981 M7 890.325206224805 552.074384 1.6127 0.115087 0.057544 M8 1078.14615314873 568.47264 1.8966 0.065506 0.032753 M9 -2425.28631966955 586.559103 -4.1348 0.000189 9.4e-05 M10 83.7658990470096 706.011076 0.1186 0.90618 0.45309 M11 65.1693704083077 704.191604 0.0925 0.926751 0.463376 t 4.86711279747431 1.534478 3.1718 0.002994 0.001497 Multiple Linear Regression - Regression Statistics Multiple R 0.998836388403883 R-squared 0.997674130799712 Adjusted R-squared 0.996633610368004 F-TEST (value) 958.82223971542 F-TEST (DF numerator) 17 F-TEST (DF denominator) 38 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 61.0872216019478 Sum Squared Residuals 141802.648435728 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1318 1316.35488911912 1.64511088087917 2 1578 1555.13733994116 22.8626600588374 3 1859 1833.73503776466 25.2649622353445 4 2141 2134.17367241593 6.82632758406626 5 2428 2438.44272337777 -10.4427233777739 6 2715 2741.38488138103 -26.3848813810282 7 3004 3051.43430984211 -47.4343098421122 8 3309 3393.56153591642 -84.5615359164223 9 269 56.1797826823087 212.820217317691 10 537 533.697906786367 3.30209321363307 11 813 810.698050702226 2.30194929777444 12 1068 1075.21761598108 -7.21761598107972 13 1411 1372.58550116861 38.4144988313882 14 1675 1684.30048745818 -9.3004874581777 15 1958 1961.54274234898 -3.54274234898012 16 2242 2245.49324368938 -3.49324368937669 17 2524 2548.35658529563 -24.3565852956338 18 2836 2847.05431784009 -11.0543178400900 19 3143 3173.00299984029 -30.0029998402896 20 3522 3527.77719252433 -5.77719252433067 21 285 235.392323008476 49.607676991524 22 574 579.516629467582 -5.51662946758245 23 865 874.304111848152 -9.30411184815186 24 1147 1155.35894339907 -8.35894339906818 25 1516 1476.67514986460 39.3248501353964 26 1789 1809.98406850642 -20.9840685064161 27 2087 2097.41694795355 -10.4169479535464 28 2372 2394.13446423062 -22.1344642306232 29 2669 2692.86466895153 -23.8646689515327 30 2966 2997.75727575969 -31.7572757596936 31 3270 3309.69531748239 -39.6953174823864 32 3652 3659.59369241783 -7.59369241783452 33 329 368.729276295068 -39.7292762950684 34 658 647.558435010314 10.4415649896860 35 988 971.017529466067 16.9824705339327 36 1303 1278.89715940582 24.1028405941785 37 1603 1624.97282375311 -21.9728237531087 38 1929 1911.91748199048 17.0825180095211 39 2235 2238.19318371312 -3.19318371312122 40 2544 2538.27893929193 5.72106070806572 41 2872 2849.89696893166 22.1030310683432 42 3198 3169.65546054025 28.3445394597490 43 3544 3495.64117838291 48.3588216170918 44 3903 3867.23753945419 35.7624605458114 45 332 554.698618014147 -222.698618014147 46 665 673.227028735737 -8.2270287357366 47 1001 1010.98030798356 -9.9803079835553 48 1329 1337.52628121403 -8.52628121403101 49 1639 1696.41163609455 -57.411636094555 50 1975 1984.66062210376 -9.66062210376465 51 2304 2312.11208821970 -8.11208821969641 52 2640 2626.91968037213 13.0803196278679 53 2992 2955.4390534434 36.5609465565971 54 3330 3289.14806447894 40.8519355210629 55 3690 3621.22619445230 68.7738055476964 56 4063 4000.83003968722 62.1699603127762 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.688204193348966 0.623591613302068 0.311795806651034 22 0.65850971923646 0.68298056152708 0.34149028076354 23 0.525704441123929 0.948591117752142 0.474295558876071 24 0.465882898069605 0.93176579613921 0.534117101930395 25 0.503415399790467 0.993169200419067 0.496584600209533 26 0.37537134814842 0.75074269629684 0.62462865185158 27 0.325985426142605 0.65197085228521 0.674014573857395 28 0.225590843439161 0.451181686878323 0.774409156560839 29 0.16177392476659 0.32354784953318 0.83822607523341 30 0.0965017037100377 0.193003407420075 0.903498296289962 31 0.0872139515837148 0.174427903167430 0.912786048416285 32 0.261942791266143 0.523885582532287 0.738057208733857 33 0.999986424780777 2.71504384454100e-05 1.35752192227050e-05 34 0.999872363694189 0.000255272611622119 0.000127636305811059 35 0.999441940340965 0.00111611931806926 0.000558059659034632 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 3 0.2 NOK 5% type I error level 3 0.2 NOK 10% type I error level 3 0.2 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t125872574307bpfiu7s1fvuea/10bcky1258725607.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872574307bpfiu7s1fvuea/10bcky1258725607.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872574307bpfiu7s1fvuea/1zxdm1258725607.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872574307bpfiu7s1fvuea/1zxdm1258725607.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872574307bpfiu7s1fvuea/2htco1258725607.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872574307bpfiu7s1fvuea/2htco1258725607.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872574307bpfiu7s1fvuea/34y561258725607.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872574307bpfiu7s1fvuea/34y561258725607.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872574307bpfiu7s1fvuea/42krk1258725607.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872574307bpfiu7s1fvuea/42krk1258725607.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872574307bpfiu7s1fvuea/5h3nd1258725607.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872574307bpfiu7s1fvuea/5h3nd1258725607.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872574307bpfiu7s1fvuea/64asl1258725607.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872574307bpfiu7s1fvuea/64asl1258725607.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872574307bpfiu7s1fvuea/7ejhd1258725607.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872574307bpfiu7s1fvuea/7ejhd1258725607.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872574307bpfiu7s1fvuea/89jx71258725607.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872574307bpfiu7s1fvuea/89jx71258725607.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872574307bpfiu7s1fvuea/9z6ta1258725607.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872574307bpfiu7s1fvuea/9z6ta1258725607.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