Revieuw WS 7 lineair trend + seasonal dummies

*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, 27 Nov 2009 03:12:00 -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/27/t1259316819o2424nlrv955rl3.htm/, Retrieved Fri, 27 Nov 2009 11:13:50 +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/27/t1259316819o2424nlrv955rl3.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 «

101.3 0 106.3 0 94 0 102.8 0 102 0 105.1 1 92.4 0 81.4 0 105.8 0 120.3 1 100.7 0 88.8 0 94.3 0 99.9 0 103.4 0 103.3 0 98.8 0 104.2 0 91.2 0 74.7 0 108.5 0 114.5 0 96.9 0 89.6 0 97.1 0 100.3 0 122.6 0 115.4 1 109 0 129.1 1 102.8 1 96.2 0 127.7 1 128.9 1 126.5 1 119.8 1 113.2 1 114.1 1 134.1 1 130 1 121.8 1 132.1 1 105.3 1 103 1 117.1 1 126.3 1 138.1 1 119.5 1 138 1 135.5 1 178.6 1 162.2 1 176.9 1 204.9 1 132.2 1 142.5 1 164.3 1 174.9 1 175.4 1 143 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Omzet[t] = + 69.4259210526316 + 2.7171052631579Uitvoer[t] + 9.73680921052626M1[t] + 11.0355921052631M2[t] + 25.214375M3[t] + 19.7297368421053M4[t] + 18.0919407894737M5[t] + 29.2438815789473M6[t] -1.65391447368423M7[t] -7.4717105263158M8[t] + 15.9636513157895M9[t] + 22.5790131578947M10[t] + 16.5212171052632M11[t] + 1.14121710526316t + 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) 69.4259210526316 7.656133 9.068 0 0 Uitvoer 2.7171052631579 6.394931 0.4249 0.672902 0.336451 M1 9.73680921052626 9.187627 1.0598 0.294781 0.14739 M2 11.0355921052631 9.170611 1.2034 0.234992 0.117496 M3 25.214375 9.157078 2.7535 0.008413 0.004206 M4 19.7297368421053 9.216329 2.1407 0.037628 0.018814 M5 18.0919407894737 9.140523 1.9793 0.053784 0.026892 M6 29.2438815789473 9.373174 3.12 0.00312 0.00156 M7 -1.65391447368423 9.147976 -0.1808 0.857322 0.428661 M8 -7.4717105263158 9.142076 -0.8173 0.417977 0.208988 M9 15.9636513157895 9.119786 1.7504 0.08671 0.043355 M10 22.5790131578947 9.239718 2.4437 0.018433 0.009216 M11 16.5212171052632 9.105658 1.8144 0.076142 0.038071 t 1.14121710526316 0.179405 6.3611 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.878199108374596 R-squared 0.771233673949935 Adjusted R-squared 0.706582320935786 F-TEST (value) 11.9291188504771 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 1.04085629004658e-10 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 14.3945146815591 Sum Squared Residuals 9531.29443421052 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 101.3 80.3039473684212 20.9960526315788 2 106.3 82.743947368421 23.556052631579 3 94 98.063947368421 -4.06394736842103 4 102.8 93.7205263157895 9.07947368421052 5 102 93.223947368421 8.77605263157896 6 105.1 108.234210526316 -3.13421052631583 7 92.4 75.7605263157895 16.6394736842105 8 81.4 71.083947368421 10.3160526315790 9 105.8 95.6605263157895 10.1394736842105 10 120.3 106.134210526316 14.1657894736842 11 100.7 98.5005263157895 2.19947368421053 12 88.8 83.1205263157895 5.67947368421054 13 94.3 93.998552631579 0.301447368421092 14 99.9 96.438552631579 3.46144736842105 15 103.4 111.758552631579 -8.35855263157894 16 103.3 107.415131578947 -4.11513157894737 17 98.8 106.918552631579 -8.11855263157896 18 104.2 119.211710526316 -15.0117105263158 19 91.2 89.4551315789474 1.74486842105265 20 74.7 84.778552631579 -10.0785526315789 21 108.5 109.355131578947 -0.855131578947372 22 114.5 117.111710526316 -2.61171052631578 23 96.9 112.195131578947 -15.2951315789474 24 89.6 96.8151315789474 -7.21513157894738 25 97.1 107.693157894737 -10.5931578947368 26 100.3 110.133157894737 -9.83315789473684 27 122.6 125.453157894737 -2.85315789473685 28 115.4 123.826842105263 -8.42684210526314 29 109 120.613157894737 -11.6131578947368 30 129.1 135.623421052632 -6.52342105263157 31 102.8 105.866842105263 -3.06684210526315 32 96.2 98.4731578947369 -2.27315789473685 33 127.7 125.766842105263 1.93315789473684 34 128.9 133.523421052632 -4.62342105263158 35 126.5 128.606842105263 -2.10684210526316 36 119.8 113.226842105263 6.57315789473683 37 113.2 124.104868421053 -10.9048684210526 38 114.1 126.544868421053 -12.4448684210526 39 134.1 141.864868421053 -7.76486842105265 40 130 137.521447368421 -7.52144736842104 41 121.8 137.024868421053 -15.2248684210526 42 132.1 149.318026315789 -17.2180263157895 43 105.3 119.561447368421 -14.2614473684211 44 103 114.884868421053 -11.8848684210527 45 117.1 139.461447368421 -22.3614473684211 46 126.3 147.218026315789 -20.9180263157895 47 138.1 142.301447368421 -4.20144736842106 48 119.5 126.921447368421 -7.42144736842106 49 138 137.799473684210 0.200526315789507 50 135.5 140.239473684211 -4.73947368421053 51 178.6 155.559473684211 23.0405263157895 52 162.2 151.216052631579 10.9839473684210 53 176.9 150.719473684211 26.1805263157895 54 204.9 163.012631578947 41.8873684210526 55 132.2 133.256052631579 -1.05605263157896 56 142.5 128.579473684211 13.9205263157895 57 164.3 153.156052631579 11.1439473684211 58 174.9 160.912631578947 13.9873684210526 59 175.4 155.996052631579 19.4039473684211 60 143 140.616052631579 2.38394736842104 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.095485951931507 0.190971903863014 0.904514048068493 18 0.0336619457870656 0.0673238915741311 0.966338054212935 19 0.0113212257780773 0.0226424515561546 0.988678774221923 20 0.00456502298296293 0.00913004596592585 0.995434977017037 21 0.00184566097187101 0.00369132194374201 0.99815433902813 22 0.000674315990673293 0.00134863198134659 0.999325684009327 23 0.000197948343236005 0.000395896686472011 0.999802051656764 24 5.52006808433995e-05 0.000110401361686799 0.999944799319157 25 1.35594276635181e-05 2.71188553270361e-05 0.999986440572336 26 3.20813177835689e-06 6.41626355671378e-06 0.999996791868222 27 0.00166747032661439 0.00333494065322877 0.998332529673386 28 0.000734268213903925 0.00146853642780785 0.999265731786096 29 0.000370697082947868 0.000741394165895736 0.999629302917052 30 0.000711071799560891 0.00142214359912178 0.99928892820044 31 0.00049130586242005 0.0009826117248401 0.99950869413758 32 0.000463387276866277 0.000926774553732553 0.999536612723134 33 0.000580925908628456 0.00116185181725691 0.999419074091372 34 0.000509959116617443 0.00101991823323489 0.999490040883383 35 0.000617402399788 0.001234804799576 0.999382597600212 36 0.0221626625196755 0.0443253250393511 0.977837337480324 37 0.0206910576958606 0.0413821153917213 0.97930894230414 38 0.0332434089604381 0.0664868179208762 0.966756591039562 39 0.0201748534168948 0.0403497068337896 0.979825146583105 40 0.0146974187154036 0.0293948374308072 0.985302581284596 41 0.010103405518916 0.020206811037832 0.989896594481084 42 0.211315558121126 0.422631116242252 0.788684441878874 43 0.213279777283600 0.426559554567201 0.7867202227164 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 16 0.592592592592593 NOK 5% type I error level 22 0.814814814814815 NOK 10% type I error level 24 0.888888888888889 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/27/t1259316819o2424nlrv955rl3/10ast61259316715.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259316819o2424nlrv955rl3/10ast61259316715.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259316819o2424nlrv955rl3/1v0x71259316715.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259316819o2424nlrv955rl3/1v0x71259316715.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259316819o2424nlrv955rl3/2qiip1259316715.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259316819o2424nlrv955rl3/2qiip1259316715.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259316819o2424nlrv955rl3/3bjg21259316715.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259316819o2424nlrv955rl3/3bjg21259316715.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259316819o2424nlrv955rl3/4stwo1259316715.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259316819o2424nlrv955rl3/4stwo1259316715.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259316819o2424nlrv955rl3/5vgky1259316715.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259316819o2424nlrv955rl3/5vgky1259316715.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259316819o2424nlrv955rl3/6ocyn1259316715.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259316819o2424nlrv955rl3/6ocyn1259316715.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259316819o2424nlrv955rl3/7i8nh1259316715.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259316819o2424nlrv955rl3/7i8nh1259316715.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259316819o2424nlrv955rl3/8ad9e1259316715.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259316819o2424nlrv955rl3/8ad9e1259316715.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259316819o2424nlrv955rl3/9zh9v1259316715.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259316819o2424nlrv955rl3/9zh9v1259316715.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