WS 7 regressie analyse: seizoenaliteit modelleren

*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 09:09:15 -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/t125856083378nicvble1y2642.htm/, Retrieved Wed, 18 Nov 2009 17:14:05 +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/t125856083378nicvble1y2642.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 «

1901 10436 1395 9314 1639 9717 1643 8997 1751 9062 1797 8885 1373 9058 1558 9095 1555 9149 2061 9857 2010 9848 2119 10269 1985 10341 1963 9690 2017 10125 1975 9349 1589 9224 1679 9224 1392 9454 1511 9347 1449 9430 1767 9933 1899 10148 2179 10677 2217 10735 2049 9760 2343 10567 2175 9333 1607 9409 1702 9502 1764 9348 1766 9319 1615 9594 1953 10160 2091 10182 2411 10810 2550 11105 2351 9874 2786 10958 2525 9311 2474 9610 2332 9398 1978 9784 1789 9425 1904 9557 1997 10166 2207 10337 2453 10770 1948 11265 1384 10183 1989 10941 2140 9628 2100 9709 2045 9637 2083 9579 2022 9741 1950 9754 1422 10508 1859 10749 2147 11079

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 aanbod[t] = -1859.31715563635 + 0.384396712586172invoer[t] -162.895577877275M1[t] -65.6092253975502M2[t] -7.28749275514697M3[t] + 366.955966167917M4[t] + 149.111746531092M5[t] + 184.203344577435M6[t] -53.1560360550097M7[t] -19.1997506699084M8[t] -96.6215444520079M9[t] -192.622679956124M10[t] -68.6254591671542M11[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) -1859.31715563635 1360.024466 -1.3671 0.178091 0.089045 invoer 0.384396712586172 0.126332 3.0428 0.00383 0.001915 M1 -162.895577877275 174.79961 -0.9319 0.356151 0.178076 M2 -65.6092253975502 212.406574 -0.3089 0.758774 0.379387 M3 -7.28749275514697 177.70714 -0.041 0.967463 0.483731 M4 366.955966167917 248.336452 1.4777 0.14617 0.073085 M5 149.111746531092 241.326412 0.6179 0.539634 0.269817 M6 184.203344577435 247.834036 0.7433 0.461028 0.230514 M7 -53.1560360550097 237.713129 -0.2236 0.824027 0.412013 M8 -19.1997506699084 242.848508 -0.0791 0.93732 0.46866 M9 -96.6215444520079 233.290124 -0.4142 0.680634 0.340317 M10 -192.622679956124 190.207498 -1.0127 0.31639 0.158195 M11 -68.6254591671542 184.403042 -0.3721 0.711454 0.355727 Multiple Linear Regression - Regression Statistics Multiple R 0.652045551001417 R-squared 0.425163400580741 Adjusted R-squared 0.278396609239654 F-TEST (value) 2.89686377071948 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0.00451362788080933 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 276.160826034476 Sum Squared Residuals 3584445.68629409 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1901 1989.35135903567 -88.3513590356689 2 1395 1655.34459999370 -260.344599993704 3 1639 1868.57820780834 -229.578207808336 4 1643 1966.05603366936 -323.056033669356 5 1751 1773.19760035063 -22.1976003506323 6 1797 1740.25098026922 56.7490197307776 7 1373 1569.39223091419 -196.392230914186 8 1558 1617.57119466498 -59.5711946649756 9 1555 1560.90682336253 -5.90682336252936 10 2061 1737.05856036942 323.941439630577 11 2010 1857.59621074512 152.403789254883 12 2119 2088.05268591105 30.9473140889499 13 1985 1952.83367133998 32.1663286600199 14 1963 1799.87776392611 163.122236073894 15 2017 2025.41206654349 -8.41206654349432 16 1975 2101.36367649969 -126.363676499689 17 1589 1835.46986778959 -246.469867789592 18 1679 1870.56146583593 -191.561465835935 19 1392 1721.61332909831 -329.61332909831 20 1511 1714.43916623669 -203.439166236691 21 1449 1668.92229959924 -219.922299599244 22 1767 1766.27271052597 0.727289474027781 23 1899 1972.91522452097 -73.9152245209692 24 2179 2244.88654464621 -65.8865446462085 25 2217 2104.28597609893 112.714023901068 26 2049 1826.78553380714 222.214466192862 27 2343 2195.31541350658 147.684586493417 28 2175 2095.21332909831 79.78667090169 29 1607 1906.58325961803 -299.583259618034 30 1702 1977.42375193489 -275.423751934890 31 1764 1680.86727756418 83.1327224358243 32 1766 1703.67605828428 62.3239417157218 33 1615 1731.96336046338 -116.963360463376 34 1953 1853.53076428303 99.4692357169667 35 2091 1985.9847127489 105.015287251101 36 2411 2296.01130742017 114.988692579831 37 2550 2246.51275975582 303.487240244184 38 2351 1870.60675904196 480.393240958038 39 2786 2345.61452812778 440.385471872224 40 2525 2086.75660142141 438.243398578586 41 2474 1983.84699884786 490.153001152145 42 2332 1937.44649382593 394.553506174071 43 1978 1848.46424425175 129.535755748253 44 1789 1744.42210981841 44.5778901815875 45 1904 1717.74068209769 186.259317902312 46 1997 1855.83714455855 141.162855441450 47 2207 2045.56620319976 161.433796800244 48 2453 2280.63543891672 172.364561083277 49 1948 2308.01623376960 -360.016233769603 50 1384 1989.38534323109 -605.385343231089 51 1989 2339.07978401381 -350.079784013811 52 2140 2208.61035931123 -68.6103593112309 53 2100 2021.90227339389 78.097726606114 54 2045 2029.31730813402 15.6826918659762 55 2083 1769.66291817158 313.337081828418 56 2022 1865.89147099564 156.108529004357 57 1950 1793.46683447716 156.533165522837 58 1422 1987.30082026302 -565.300820263021 59 1859 2203.93764878526 -344.937648785259 60 2147 2399.41402310585 -252.41402310585 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0535111912257077 0.107022382451415 0.946488808774292 17 0.088719816948217 0.177439633896434 0.911280183051783 18 0.137517876470940 0.275035752941879 0.86248212352906 19 0.117117716788265 0.234235433576529 0.882882283211735 20 0.0828575285855943 0.165715057171189 0.917142471414406 21 0.0705118207134458 0.141023641426892 0.929488179286554 22 0.0677817527717616 0.135563505543523 0.932218247228238 23 0.0498977639161549 0.0997955278323097 0.950102236083845 24 0.0287499140878563 0.0574998281757125 0.971250085912144 25 0.0186279927403553 0.0372559854807106 0.981372007259645 26 0.0163549305471744 0.0327098610943488 0.983645069452826 27 0.0116447159361898 0.0232894318723796 0.98835528406381 28 0.0108069739776591 0.0216139479553182 0.98919302602234 29 0.0247444415228109 0.0494888830456218 0.97525555847719 30 0.0334032239925883 0.0668064479851767 0.966596776007412 31 0.0621503745642536 0.124300749128507 0.937849625435746 32 0.0618271266463521 0.123654253292704 0.938172873353648 33 0.0545146865579159 0.109029373115832 0.945485313442084 34 0.0335666649298214 0.0671333298596428 0.966433335070179 35 0.0272171502174939 0.0544343004349878 0.972782849782506 36 0.0155197851002555 0.031039570200511 0.984480214899744 37 0.0175300779659550 0.0350601559319101 0.982469922034045 38 0.079717109499202 0.159434218998404 0.920282890500798 39 0.496010708748793 0.992021417497586 0.503989291251207 40 0.548002230037566 0.903995539924868 0.451997769962434 41 0.656813567842043 0.686372864315914 0.343186432157957 42 0.600929955636256 0.798140088727488 0.399070044363744 43 0.449294173831402 0.898588347662804 0.550705826168598 44 0.738881067376396 0.522237865247208 0.261118932623604 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 7 0.241379310344828 NOK 10% type I error level 12 0.413793103448276 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t125856083378nicvble1y2642/10rffv1258560550.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856083378nicvble1y2642/10rffv1258560550.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856083378nicvble1y2642/125ke1258560550.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856083378nicvble1y2642/125ke1258560550.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856083378nicvble1y2642/2tsxg1258560550.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856083378nicvble1y2642/2tsxg1258560550.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856083378nicvble1y2642/3w4pb1258560550.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856083378nicvble1y2642/3w4pb1258560550.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856083378nicvble1y2642/4xq4v1258560550.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856083378nicvble1y2642/4xq4v1258560550.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856083378nicvble1y2642/502hy1258560550.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856083378nicvble1y2642/502hy1258560550.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856083378nicvble1y2642/60ky01258560550.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856083378nicvble1y2642/60ky01258560550.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856083378nicvble1y2642/71hgy1258560550.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856083378nicvble1y2642/71hgy1258560550.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856083378nicvble1y2642/8ty201258560550.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856083378nicvble1y2642/8ty201258560550.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856083378nicvble1y2642/9ta7w1258560550.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856083378nicvble1y2642/9ta7w1258560550.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by