lin regr wlh lin trend en seasonal dummie

*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, 30 Dec 2009 14:32:41 -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/30/t12622088227f00wv8lcu2sc7h.htm/, Retrieved Wed, 30 Dec 2009 22:33:53 +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/30/t12622088227f00wv8lcu2sc7h.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 «

612613 0 611324 0 594167 0 595454 0 590865 0 589379 0 584428 0 573100 0 567456 0 569028 0 620735 0 628884 0 628232 0 612117 0 595404 0 597141 0 593408 0 590072 0 579799 0 574205 0 572775 0 572942 0 619567 0 625809 0 619916 0 587625 0 565742 0 557274 0 560576 1 548854 1 531673 1 525919 1 511038 1 498662 1 555362 1 564591 1 541657 1 527070 1 509846 1 514258 1 516922 1 507561 1 492622 1 490243 1 469357 1 477580 1 528379 1 533590 1 517945 1 506174 1 501866 1 516141 1 528222 1 532638 1 536322 1 536535 1 523597 1 536214 1 586570 1 596594 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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation wlh[t] = + 632253.383333333 -66268.7222222223dummies[t] -19868.7458333334M1[t] -35007.1638888889M2[t] -50391.9819444444M3[t] -47671.2M4[t] -32400.2736111111M5[t] -36625.8916666666M6[t] -45285.7097222222M7[t] -50181.9277777778M8[t] -61265.5458333334M9[t] -59152.7638888889M10[t] -7843.18194444445M11[t] -72.1819444444439t + 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) 632253.383333333 10091.23607 62.6537 0 0 dummies -66268.7222222223 9710.296436 -6.8246 0 0 M1 -19868.7458333334 11776.445325 -1.6872 0.098344 0.049172 M2 -35007.1638888889 11746.38202 -2.9803 0.00459 0.002295 M3 -50391.9819444444 11722.946148 -4.2986 8.8e-05 4.4e-05 M4 -47671.2 11706.177514 -4.0723 0.000182 9.1e-05 M5 -32400.2736111111 11816.410752 -2.742 0.008671 0.004336 M6 -36625.8916666666 11773.108748 -3.111 0.003199 0.0016 M7 -45285.7097222222 11736.343805 -3.8586 0.000354 0.000177 M8 -50181.9277777778 11706.177514 -4.2868 9.2e-05 4.6e-05 M9 -61265.5458333334 11682.660991 -5.2441 4e-06 2e-06 M10 -59152.7638888889 11665.83445 -5.0706 7e-06 3e-06 M11 -7843.18194444445 11655.726866 -0.6729 0.504375 0.252188 t -72.1819444444439 280.312113 -0.2575 0.797937 0.398968 Multiple Linear Regression - Regression Statistics Multiple R 0.92315839266087 R-squared 0.852221417940203 Adjusted R-squared 0.810457905618956 F-TEST (value) 20.4058847202523 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 7.54951656745106e-15 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 18423.9920956829 Sum Squared Residuals 15614400298.1222 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 612613 612312.455555556 300.54444444418 2 611324 597101.855555555 14222.1444444445 3 594167 581644.855555556 12522.1444444445 4 595454 584293.455555556 11160.5444444445 5 590865 599492.2 -8627.19999999999 6 589379 595194.4 -5815.39999999994 7 584428 586462.4 -2034.39999999998 8 573100 581494 -8394.00000000005 9 567456 570338.2 -2882.20000000009 10 569028 572378.8 -3350.8 11 620735 623616.2 -2881.19999999996 12 628884 631387.2 -2503.19999999997 13 628232 611446.272222222 16785.7277777779 14 612117 596235.672222222 15881.3277777777 15 595404 580778.672222222 14625.3277777778 16 597141 583427.272222222 13713.7277777778 17 593408 598626.016666667 -5218.01666666666 18 590072 594328.216666667 -4256.21666666667 19 579799 585596.216666667 -5797.21666666668 20 574205 580627.816666667 -6422.81666666666 21 572775 569472.016666667 3302.98333333335 22 572942 571512.616666667 1429.38333333333 23 619567 622750.016666667 -3183.01666666667 24 625809 630521.016666667 -4712.01666666667 25 619916 610580.088888889 9335.91111111118 26 587625 595369.488888889 -7744.48888888891 27 565742 579912.488888889 -14170.4888888889 28 557274 582561.088888889 -25287.0888888889 29 560576 531491.111111111 29084.8888888889 30 548854 527193.311111111 21660.6888888889 31 531673 518461.311111111 13211.6888888889 32 525919 513492.911111111 12426.0888888889 33 511038 502337.111111111 8700.88888888892 34 498662 504377.711111111 -5715.7111111111 35 555362 555615.111111111 -253.111111111112 36 564591 563386.111111111 1204.88888888888 37 541657 543445.183333333 -1788.18333333327 38 527070 528234.583333333 -1164.58333333335 39 509846 512777.583333333 -2931.58333333334 40 514258 515426.183333333 -1168.18333333335 41 516922 530624.927777778 -13702.9277777778 42 507561 526327.127777778 -18766.1277777778 43 492622 517595.127777778 -24973.1277777778 44 490243 512626.727777778 -22383.7277777778 45 469357 501470.927777778 -32113.9277777778 46 477580 503511.527777778 -25931.5277777778 47 528379 554748.927777778 -26369.9277777778 48 533590 562519.927777778 -28929.9277777778 49 517945 542579 -24633.9999999999 50 506174 527368.4 -21194.4 51 501866 511911.4 -10045.4000000000 52 516141 514560 1580.99999999997 53 528222 529758.744444444 -1536.74444444445 54 532638 525460.944444444 7177.05555555553 55 536322 516728.944444444 19593.0555555555 56 536535 511760.544444444 24774.4555555556 57 523597 500604.744444444 22992.2555555556 58 536214 502645.344444444 33568.6555555556 59 586570 553882.744444444 32687.2555555555 60 596594 561653.744444444 34940.2555555555 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0202643356444807 0.0405286712889615 0.97973566435552 18 0.00451437449788936 0.00902874899577872 0.99548562550211 19 0.00168549439102486 0.00337098878204972 0.998314505608975 20 0.000329289098795788 0.000658578197591576 0.999670710901204 21 6.43539299267122e-05 0.000128707859853424 0.999935646070073 22 1.07515733547039e-05 2.15031467094077e-05 0.999989248426645 23 2.09658915752406e-06 4.19317831504812e-06 0.999997903410842 24 5.04350453537493e-07 1.00870090707499e-06 0.999999495649546 25 1.15107723931483e-07 2.30215447862966e-07 0.999999884892276 26 2.45395537718283e-05 4.90791075436566e-05 0.999975460446228 27 0.00016825052185954 0.00033650104371908 0.99983174947814 28 0.000959498805784551 0.00191899761156910 0.999040501194215 29 0.000715774496874084 0.00143154899374817 0.999284225503126 30 0.000555752203488987 0.00111150440697797 0.999444247796511 31 0.000455113021516109 0.000910226043032218 0.999544886978484 32 0.000292871689405026 0.000585743378810052 0.999707128310595 33 0.000389103790991179 0.000778207581982358 0.99961089620901 34 0.000618990127300548 0.00123798025460110 0.9993810098727 35 0.000543396058334212 0.00108679211666842 0.999456603941666 36 0.00059427367106025 0.0011885473421205 0.99940572632894 37 0.00273167945109566 0.00546335890219132 0.997268320548904 38 0.0149574880657280 0.0299149761314561 0.985042511934272 39 0.0570023880619358 0.114004776123872 0.942997611938064 40 0.179403664373240 0.358807328746479 0.82059633562676 41 0.539247450906018 0.921505098187964 0.460752549093982 42 0.88567733909151 0.228645321816980 0.114322660908490 43 0.896667863461163 0.206664273077674 0.103332136538837 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 20 0.740740740740741 NOK 5% type I error level 22 0.814814814814815 NOK 10% type I error level 22 0.814814814814815 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/30/t12622088227f00wv8lcu2sc7h/102j7s1262208755.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t12622088227f00wv8lcu2sc7h/102j7s1262208755.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t12622088227f00wv8lcu2sc7h/11cfp1262208755.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t12622088227f00wv8lcu2sc7h/11cfp1262208755.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t12622088227f00wv8lcu2sc7h/2e2d01262208755.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t12622088227f00wv8lcu2sc7h/2e2d01262208755.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t12622088227f00wv8lcu2sc7h/361qz1262208755.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t12622088227f00wv8lcu2sc7h/361qz1262208755.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t12622088227f00wv8lcu2sc7h/4e5u01262208755.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t12622088227f00wv8lcu2sc7h/4e5u01262208755.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t12622088227f00wv8lcu2sc7h/59t451262208755.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t12622088227f00wv8lcu2sc7h/59t451262208755.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t12622088227f00wv8lcu2sc7h/6w5te1262208755.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t12622088227f00wv8lcu2sc7h/6w5te1262208755.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t12622088227f00wv8lcu2sc7h/7dl1a1262208755.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t12622088227f00wv8lcu2sc7h/7dl1a1262208755.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t12622088227f00wv8lcu2sc7h/8wnto1262208755.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t12622088227f00wv8lcu2sc7h/8wnto1262208755.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t12622088227f00wv8lcu2sc7h/9af5c1262208755.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t12622088227f00wv8lcu2sc7h/9af5c1262208755.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