Paper: Regressie analyse (seasonal dummies, linear trend en gegevens van vorige jaren)

*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: Sat, 19 Dec 2009 23:08:45 -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/20/t126128965073gdx3s0vm9kmei.htm/, Retrieved Sun, 20 Dec 2009 07:14:22 +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/20/t126128965073gdx3s0vm9kmei.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 «

79 75 74 78 84 79 79 75 74 78 82 79 79 75 74 88 82 79 79 75 81 88 82 79 79 69 81 88 82 79 62 69 81 88 82 62 62 69 81 88 68 62 62 69 81 57 68 62 62 69 67 57 68 62 62 72 67 57 68 62 75 72 67 57 68 81 75 72 67 57 80 81 75 72 67 79 80 81 75 72 81 79 80 81 75 83 81 79 80 81 84 83 81 79 80 90 84 83 81 79 84 90 84 83 81 90 84 90 84 83 92 90 84 90 84 93 92 90 84 90 85 93 92 90 84 93 85 93 92 90 94 93 85 93 92 94 94 93 85 93 102 94 94 93 85 96 102 94 94 93 96 96 102 94 94 92 96 96 102 94 90 92 96 96 102 84 90 92 96 96 86 84 90 92 96 70 86 84 90 92 67 70 86 84 90 60 67 70 86 84 62 60 67 70 86 61 62 60 67 70 54 61 62 60 67 50 54 61 62 60 45 50 54 61 62 34 45 50 54 61 37 34 45 50 54 44 37 34 45 50 34 44 37 34 45 37 34 44 37 34 31 37 34 44 37 31 31 37 34 44 28 31 31 37 34 31 28 31 31 37 33 31 28 31 31 36 33 31 28 31 39 36 33 31 28 42 39 36 33 31

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 8 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation Y(t)[t] = + 6.64321270528053 + 0.872702527227202`Y(t-1)`[t] + 0.258918872405125`Y(t-2)`[t] -0.0448813312088279`Y(t-3)`[t] -0.167125823727214`Y(t-4) `[t] -0.64550965373359M1[t] + 2.56198536203161M2[t] + 2.28982093315230M3[t] + 2.77357881188303M4[t] + 0.862448902919936M5[t] -2.21338885997563M6[t] -0.527309563378737M7[t] + 0.85100122897588M8[t] + 2.81894668946818M9[t] + 1.30992470299167M10[t] + 2.642957463527M11[t] -0.0856389618791044t + 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) 6.64321270528053 5.957366 1.1151 0.271621 0.135811 `Y(t-1)` 0.872702527227202 0.158814 5.4951 3e-06 1e-06 `Y(t-2)` 0.258918872405125 0.20961 1.2352 0.22413 0.112065 `Y(t-3)` -0.0448813312088279 0.20937 -0.2144 0.83138 0.41569 `Y(t-4) ` -0.167125823727214 0.161755 -1.0332 0.307875 0.153937 M1 -0.64550965373359 4.208103 -0.1534 0.878876 0.439438 M2 2.56198536203161 4.207623 0.6089 0.546127 0.273064 M3 2.28982093315230 4.184255 0.5472 0.587327 0.293664 M4 2.77357881188303 4.196965 0.6609 0.512591 0.256296 M5 0.862448902919936 4.178364 0.2064 0.837546 0.418773 M6 -2.21338885997563 4.188079 -0.5285 0.600149 0.300074 M7 -0.527309563378737 4.230848 -0.1246 0.901453 0.450727 M8 0.85100122897588 4.205754 0.2023 0.840701 0.420351 M9 2.81894668946818 4.51871 0.6238 0.536366 0.268183 M10 1.30992470299167 4.429982 0.2957 0.769032 0.384516 M11 2.642957463527 4.420052 0.5979 0.553332 0.276666 t -0.0856389618791044 0.066964 -1.2789 0.208494 0.104247 Multiple Linear Regression - Regression Statistics Multiple R 0.971615849687214 R-squared 0.944037359363406 Adjusted R-squared 0.921078327307367 F-TEST (value) 41.118343188824 F-TEST (DF numerator) 16 F-TEST (DF denominator) 39 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6.21182366115827 Sum Squared Residuals 1504.88337469570 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 79 72.9854371623126 6.01456283768739 2 79 81.0393024647114 -2.03930246471138 3 82 82.3407965272735 -0.340796527273466 4 88 85.0103718772442 2.98962812275582 5 81 88.3580714920717 -7.35807149207171 6 69 80.5065463175109 -11.5065463175109 7 62 69.0514587602318 -7.05145876023176 8 62 60.4396008073539 1.56039919264613 9 68 62.2179319397276 5.78206806027238 10 57 68.1791653579236 -11.1791653579236 11 67 62.5502253576018 4.44977464239818 12 72 65.4312586207584 6.56874137924159 13 75 71.1437510662668 3.85624893373321 14 81 79.5678798127712 1.4321201872288 15 80 83.3273833092751 -3.32738330927510 16 79 83.4360398210677 -4.43603982106772 17 81 79.5369840921586 1.46301590784141 18 83 76.9041199382787 6.09588006172126 19 84 80.9798102271972 3.02018977280278 20 90 83.7403854910198 6.25961450898025 21 84 90.6938117155292 -6.6938117155292 22 90 85.0373158595779 4.96268414042212 23 92 89.5309977761864 2.46900222381363 24 93 89.3678526845551 3.63214731544489 25 85 90.7607112960902 -5.76071129609018 26 93 86.0673483997829 6.93265160021715 27 94 90.2406812689378 3.75931873106221 28 94 93.774778518201 0.225221481798963 29 102 93.014884459911 8.98511554008894 30 96 95.4531400319275 0.546859968072538 31 96 93.7215903587958 2.27840964120418 32 92 93.10169830517 -1.10169830516997 33 90 90.4254760923096 -0.425476092309607 34 84 87.0524895422424 -3.05248954224237 35 86 82.7253557575604 3.27464424243956 36 70 80.9469171095045 -10.9469171095045 37 67 67.3739054377742 -0.373905437774209 38 60 64.6479442314423 -4.64794423144232 39 62 57.7883161847649 4.21168381523508 40 61 60.928065222497 0.0719347775030117 41 54 59.3919783588813 -5.39197835888127 42 50 50.9427831747839 -0.942783174783904 43 45 46.9506109775114 -1.9506109775114 44 34 43.3253898244194 -9.32538982441942 45 37 35.6627802524336 1.33721974756643 46 44 34.7310292402562 9.26897075974382 47 34 44.1934211086514 -10.1934211086514 48 37 36.253971585182 0.746028414818011 49 31 34.7361950375562 -3.7361950375562 50 31 32.6775250912922 -1.67752509129224 51 28 32.3028227097487 -4.30282270974872 52 31 29.8507445609901 1.14925543900993 53 33 30.6980815969774 2.30191840302261 54 36 30.193410537499 5.80658946250101 55 39 35.2965296762638 3.70347032373621 56 42 39.392925572037 2.60707442796301 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 20 0.678301415248194 0.643397169503613 0.321698584751806 21 0.745948032159053 0.508103935681895 0.254051967840947 22 0.869306011516604 0.261387976966792 0.130693988483396 23 0.836506550341969 0.326986899316062 0.163493449658031 24 0.837978797290195 0.324042405419609 0.162021202709805 25 0.865682267110032 0.268635465779936 0.134317732889968 26 0.803130680604841 0.393738638790317 0.196869319395159 27 0.732383223893782 0.535233552212437 0.267616776106218 28 0.641446653584763 0.717106692830474 0.358553346415237 29 0.686316331498407 0.627367337003187 0.313683668501593 30 0.597039816084148 0.805920367831705 0.402960183915852 31 0.504763420889132 0.990473158221737 0.495236579110868 32 0.567006083068184 0.865987833863633 0.432993916931816 33 0.505186996789149 0.989626006421702 0.494813003210851 34 0.576688171797499 0.846623656405002 0.423311828202501 35 0.516552495715054 0.966895008569893 0.483447504284946 36 0.89541026630472 0.209179467390560 0.104589733695280 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 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/20/t126128965073gdx3s0vm9kmei/1059u01261289316.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126128965073gdx3s0vm9kmei/1059u01261289316.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126128965073gdx3s0vm9kmei/1enz11261289316.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126128965073gdx3s0vm9kmei/1enz11261289316.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126128965073gdx3s0vm9kmei/267801261289316.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126128965073gdx3s0vm9kmei/267801261289316.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126128965073gdx3s0vm9kmei/3ib0b1261289316.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126128965073gdx3s0vm9kmei/3ib0b1261289316.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126128965073gdx3s0vm9kmei/4eycy1261289316.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126128965073gdx3s0vm9kmei/4eycy1261289316.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126128965073gdx3s0vm9kmei/5crj01261289316.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126128965073gdx3s0vm9kmei/5crj01261289316.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126128965073gdx3s0vm9kmei/68sye1261289316.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126128965073gdx3s0vm9kmei/68sye1261289316.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126128965073gdx3s0vm9kmei/7g5ve1261289316.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126128965073gdx3s0vm9kmei/7g5ve1261289316.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126128965073gdx3s0vm9kmei/80e0i1261289316.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126128965073gdx3s0vm9kmei/80e0i1261289316.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126128965073gdx3s0vm9kmei/96u6f1261289316.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126128965073gdx3s0vm9kmei/96u6f1261289316.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