Multiple regression model 3

*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: Tue, 21 Dec 2010 17:06:28 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/21/t12929511203dqzucaq9blrnml.htm/, Retrieved Tue, 21 Dec 2010 18:05:31 +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/2010/Dec/21/t12929511203dqzucaq9blrnml.htm/}, year = {2010}, } @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 = {2010}, 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 «

1038.00 0 934.00 0 988.00 0 870.00 0 854.00 0 834.00 0 872.00 0 954.00 0 870.00 0 1238.00 0 1082.00 0 1053.00 0 934.00 0 787.00 0 1081.00 0 908.00 0 995.00 0 825.00 0 822.00 0 856.00 0 887.00 0 1094.00 0 990.00 0 936.00 0 1097.00 0 918.00 0 926.00 0 907.00 0 899.00 0 971.00 0 1087.00 0 1000.00 0 1071.00 0 1190.00 0 1116.00 0 1070.00 0 1314.00 0 1068.00 0 1185.00 0 1215.00 0 1145.00 0 1251.00 1 1363.00 1 1368.00 1 1535.00 1 1853.00 1 1866.00 1 2023.00 1 1373.00 1 1968.00 1 1424.00 1 1160.00 1 1243.00 1 1375.00 1 1539.00 1 1773.00 1 1906.00 1 2076.00 1 2004.00 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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Asielaanvragen[t] = + 980.866071428572 + 433.157738095238Verandering[t] -67.4180307539678M1[t] -89.6628472222222M2[t] -109.907663690476M3[t] -224.75248015873M4[t] -215.597296626984M5[t] -284.273660714286M6[t] -204.918477182540M7[t] -157.363293650794M8[t] -99.8081101190475M9[t] + 130.547073412699M10[t] + 45.9022569444446M11[t] + 6.04481646825396t + 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) 980.866071428572 95.984556 10.219 0 0 Verandering 433.157738095238 78.714044 5.5029 2e-06 1e-06 M1 -67.4180307539678 110.81713 -0.6084 0.546 0.273 M2 -89.6628472222222 110.695264 -0.81 0.422205 0.211102 M3 -109.907663690476 110.613691 -0.9936 0.325722 0.162861 M4 -224.75248015873 110.572502 -2.0326 0.048016 0.024008 M5 -215.597296626984 110.571741 -1.9498 0.057442 0.028721 M6 -284.273660714286 111.170147 -2.5571 0.013997 0.006998 M7 -204.918477182540 111.011432 -1.8459 0.071488 0.035744 M8 -157.363293650794 110.892817 -1.4191 0.162772 0.081386 M9 -99.8081101190475 110.814429 -0.9007 0.372555 0.186277 M10 130.547073412699 110.776354 1.1785 0.244802 0.122401 M11 45.9022569444446 110.778633 0.4144 0.680579 0.340289 t 6.04481646825396 2.114492 2.8588 0.006423 0.003211 Multiple Linear Regression - Regression Statistics Multiple R 0.91005535453701 R-squared 0.828200748321482 Adjusted R-squared 0.778569853392133 F-TEST (value) 16.6872015807984 F-TEST (DF numerator) 13 F-TEST (DF denominator) 45 p-value 4.51638726417514e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 164.785340947761 Sum Squared Residuals 1221939.38660714 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1038 919.492857142856 118.507142857144 2 934 903.292857142857 30.7071428571429 3 988 889.092857142857 98.9071428571428 4 870 780.292857142857 89.707142857143 5 854 795.492857142857 58.5071428571426 6 834 732.86130952381 101.13869047619 7 872 818.26130952381 53.7386904761904 8 954 871.86130952381 82.1386904761905 9 870 935.46130952381 -65.4613095238097 10 1238 1171.86130952381 66.1386904761906 11 1082 1093.26130952381 -11.2613095238096 12 1053 1053.40386904762 -0.40386904761896 13 934 992.030654761905 -58.0306547619052 14 787 975.830654761905 -188.830654761905 15 1081 961.630654761905 119.369345238095 16 908 852.830654761905 55.1693452380951 17 995 868.030654761905 126.969345238095 18 825 805.399107142857 19.6008928571429 19 822 890.799107142857 -68.7991071428571 20 856 944.399107142857 -88.3991071428572 21 887 1007.99910714286 -120.999107142857 22 1094 1244.39910714286 -150.399107142857 23 990 1165.79910714286 -175.799107142857 24 936 1125.94166666667 -189.941666666667 25 1097 1064.56845238095 32.4315476190473 26 918 1048.36845238095 -130.368452380952 27 926 1034.16845238095 -108.168452380952 28 907 925.368452380952 -18.3684523809524 29 899 940.568452380952 -41.5684523809524 30 971 877.936904761905 93.0630952380954 31 1087 963.336904761905 123.663095238095 32 1000 1016.93690476190 -16.9369047619048 33 1071 1080.53690476190 -9.53690476190468 34 1190 1316.93690476190 -126.936904761905 35 1116 1238.33690476190 -122.336904761905 36 1070 1198.47946428571 -128.479464285714 37 1314 1137.10625 176.893750000000 38 1068 1120.90625 -52.90625 39 1185 1106.70625 78.2937500000002 40 1215 997.90625 217.09375 41 1145 1013.10625 131.89375 42 1251 1383.63244047619 -132.632440476190 43 1363 1469.03244047619 -106.032440476190 44 1368 1522.63244047619 -154.632440476190 45 1535 1586.23244047619 -51.2324404761905 46 1853 1822.63244047619 30.3675595238094 47 1866 1744.03244047619 121.967559523809 48 2023 1704.175 318.825 49 1373 1642.80178571429 -269.801785714286 50 1968 1626.60178571429 341.398214285714 51 1424 1612.40178571429 -188.401785714286 52 1160 1503.60178571429 -343.601785714286 53 1243 1518.80178571429 -275.801785714286 54 1375 1456.17023809524 -81.1702380952379 55 1539 1541.57023809524 -2.57023809523802 56 1773 1595.17023809524 177.829761904762 57 1906 1658.77023809524 247.229761904762 58 2076 1895.17023809524 180.829761904762 59 2004 1816.57023809524 187.429761904762 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.212169056590948 0.424338113181896 0.787830943409052 18 0.102256660133767 0.204513320267535 0.897743339866233 19 0.0461899907924728 0.0923799815849456 0.953810009207527 20 0.0236498788343137 0.0472997576686274 0.976350121165686 21 0.00916771130885201 0.0183354226177040 0.990832288691148 22 0.00606194515254852 0.0121238903050970 0.993938054847451 23 0.00257494035475718 0.00514988070951436 0.997425059645243 24 0.00130382517541656 0.00260765035083311 0.998696174824584 25 0.00169913510126015 0.00339827020252030 0.99830086489874 26 0.000906908549107572 0.00181381709821514 0.999093091450892 27 0.000474981361547341 0.000949962723094682 0.999525018638453 28 0.000199299270589833 0.000398598541179666 0.99980070072941 29 7.59769774509428e-05 0.000151953954901886 0.99992402302255 30 0.000109158716579441 0.000218317433158882 0.99989084128342 31 0.000435958584864162 0.000871917169728323 0.999564041415136 32 0.000193866056721776 0.000387732113443552 0.999806133943278 33 0.000163275211458692 0.000326550422917385 0.999836724788541 34 7.72650041082006e-05 0.000154530008216401 0.999922734995892 35 5.89521512398906e-05 0.000117904302479781 0.99994104784876 36 0.000706580614100323 0.00141316122820065 0.9992934193859 37 0.0017999706987823 0.0035999413975646 0.998200029301218 38 0.443425380500493 0.886850761000985 0.556574619499507 39 0.527859170487476 0.944281659025048 0.472140829512524 40 0.555344662883935 0.88931067423213 0.444655337116065 41 0.419135094080727 0.838270188161453 0.580864905919273 42 0.369601208211153 0.739202416422305 0.630398791788847 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 15 0.576923076923077 NOK 5% type I error level 18 0.692307692307692 NOK 10% type I error level 19 0.730769230769231 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t12929511203dqzucaq9blrnml/103c491292951181.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929511203dqzucaq9blrnml/103c491292951181.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929511203dqzucaq9blrnml/1eb7x1292951181.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929511203dqzucaq9blrnml/1eb7x1292951181.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929511203dqzucaq9blrnml/2p2701292951181.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929511203dqzucaq9blrnml/2p2701292951181.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929511203dqzucaq9blrnml/3p2701292951181.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929511203dqzucaq9blrnml/3p2701292951181.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929511203dqzucaq9blrnml/4p2701292951181.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929511203dqzucaq9blrnml/4p2701292951181.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929511203dqzucaq9blrnml/5p2701292951181.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929511203dqzucaq9blrnml/5p2701292951181.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929511203dqzucaq9blrnml/60tol1292951181.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929511203dqzucaq9blrnml/60tol1292951181.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929511203dqzucaq9blrnml/7a2no1292951181.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929511203dqzucaq9blrnml/7a2no1292951181.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929511203dqzucaq9blrnml/8a2no1292951181.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929511203dqzucaq9blrnml/8a2no1292951181.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929511203dqzucaq9blrnml/9a2no1292951181.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929511203dqzucaq9blrnml/9a2no1292951181.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