seizoenaliteit

*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, 18 Dec 2010 11:46:43 +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/18/t1292672692hnmof3pavaci6vl.htm/, Retrieved Sat, 18 Dec 2010 12:44:54 +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/18/t1292672692hnmof3pavaci6vl.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 «

94.6 95.9 104.7 102.8 98.1 113.9 80.9 95.7 113.2 105.9 108.8 102.3 99 100.7 115.5 100.7 109.9 114.6 85.4 100.5 114.8 116.5 112.9 102 106 105.3 118.8 106.1 109.3 117.2 92.5 104.2 112.5 122.4 113.3 100 110.7 112.8 109.8 117.3 109.1 115.9 96 99.8 116.8 115.7 99.4 94.3 91 93.2 103.1 94.1 91.8 102.7 82.6 89.1 104.5 105.1 95.1 88.7 86.3 91.8 111.5 99.7 97.5 111.7 86.2 95.4

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 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation productie[t] = + 97.46 + 0.473333333333328M1[t] + 2.49M2[t] + 13.1066666666667M3[t] + 5.99M4[t] + 5.15666666666667M5[t] + 15.2066666666667M6[t] -10.1933333333333M7[t] -0.00999999999999586M8[t] + 14.9M9[t] + 15.66M10[t] + 8.44M11[t] + e[t] Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STAT H0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 97.46 3.107249 31.3654 0 0 M1 0.473333333333328 4.207235 0.1125 0.910826 0.455413 M2 2.49 4.207235 0.5918 0.556341 0.278171 M3 13.1066666666667 4.207235 3.1153 0.002897 0.001448 M4 5.99 4.207235 1.4237 0.160071 0.080035 M5 5.15666666666667 4.207235 1.2257 0.225454 0.112727 M6 15.2066666666667 4.207235 3.6144 0.000646 0.000323 M7 -10.1933333333333 4.207235 -2.4228 0.01866 0.00933 M8 -0.00999999999999586 4.207235 -0.0024 0.998112 0.499056 M9 14.9 4.394313 3.3907 0.001284 0.000642 M10 15.66 4.394313 3.5637 0.000756 0.000378 M11 8.44 4.394313 1.9207 0.059871 0.029936 Multiple Linear Regression - Regression Statistics Multiple R 0.76970485989946 R-squared 0.592445571352847 Adjusted R-squared 0.512390237154299 F-TEST (value) 7.40045091665601 F-TEST (DF numerator) 11 F-TEST (DF denominator) 56 p-value 1.21475681513772e-07 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6.94801958765778 Sum Squared Residuals 2703.39866666667 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 94.6 97.9333333333334 -3.33333333333337 2 95.9 99.95 -4.04999999999999 3 104.7 110.566666666667 -5.86666666666666 4 102.8 103.45 -0.650000000000001 5 98.1 102.616666666667 -4.51666666666667 6 113.9 112.666666666667 1.23333333333334 7 80.9 87.2666666666667 -6.36666666666665 8 95.7 97.45 -1.74999999999999 9 113.2 112.36 0.84 10 105.9 113.12 -7.22 11 108.8 105.9 2.9 12 102.3 97.46 4.84 13 99 97.9333333333333 1.06666666666667 14 100.7 99.95 0.750000000000001 15 115.5 110.566666666667 4.93333333333333 16 100.7 103.45 -2.75 17 109.9 102.616666666667 7.28333333333334 18 114.6 112.666666666667 1.93333333333332 19 85.4 87.2666666666667 -1.86666666666667 20 100.5 97.45 3.05 21 114.8 112.36 2.44 22 116.5 113.12 3.38 23 112.9 105.9 7 24 102 97.46 4.54 25 106 97.9333333333333 8.06666666666667 26 105.3 99.95 5.35 27 118.8 110.566666666667 8.23333333333333 28 106.1 103.45 2.65 29 109.3 102.616666666667 6.68333333333333 30 117.2 112.666666666667 4.53333333333333 31 92.5 87.2666666666667 5.23333333333333 32 104.2 97.45 6.75 33 112.5 112.36 0.14 34 122.4 113.12 9.28 35 113.3 105.9 7.4 36 100 97.46 2.54 37 110.7 97.9333333333333 12.7666666666667 38 112.8 99.95 12.85 39 109.8 110.566666666667 -0.766666666666669 40 117.3 103.45 13.85 41 109.1 102.616666666667 6.48333333333333 42 115.9 112.666666666667 3.23333333333333 43 96 87.2666666666667 8.73333333333333 44 99.8 97.45 2.34999999999999 45 116.8 112.36 4.44 46 115.7 113.12 2.58 47 99.4 105.9 -6.5 48 94.3 97.46 -3.16 49 91 97.9333333333333 -6.93333333333333 50 93.2 99.95 -6.75 51 103.1 110.566666666667 -7.46666666666667 52 94.1 103.45 -9.35 53 91.8 102.616666666667 -10.8166666666667 54 102.7 112.666666666667 -9.96666666666667 55 82.6 87.2666666666667 -4.66666666666668 56 89.1 97.45 -8.35000000000001 57 104.5 112.36 -7.86 58 105.1 113.12 -8.02 59 95.1 105.9 -10.8 60 88.7 97.46 -8.76 61 86.3 97.9333333333333 -11.6333333333333 62 91.8 99.95 -8.15 63 111.5 110.566666666667 0.933333333333333 64 99.7 103.45 -3.75 65 97.5 102.616666666667 -5.11666666666667 66 111.7 112.666666666667 -0.966666666666668 67 86.2 87.2666666666667 -1.06666666666667 68 95.4 97.45 -2.04999999999999 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 15 0.279903945828856 0.559807891657713 0.720096054171144 16 0.149490446041479 0.298980892082957 0.850509553958521 17 0.225069076485577 0.450138152971154 0.774930923514423 18 0.130239130852882 0.260478261705765 0.869760869147118 19 0.0828617936707712 0.165723587341542 0.917138206329229 20 0.0527294448431985 0.105458889686397 0.947270555156801 21 0.0274548426769131 0.0549096853538262 0.972545157323087 22 0.0357619702752245 0.0715239405504491 0.964238029724775 23 0.0240213879853113 0.0480427759706225 0.975978612014689 24 0.0131441852574014 0.0262883705148029 0.986855814742599 25 0.0191180789004362 0.0382361578008724 0.980881921099564 26 0.0169712804313833 0.0339425608627665 0.983028719568617 27 0.0212144982024582 0.0424289964049163 0.978785501797542 28 0.0136701112946298 0.0273402225892595 0.98632988870537 29 0.0111916156927583 0.0223832313855166 0.988808384307242 30 0.00709002570717608 0.0141800514143522 0.992909974292824 31 0.00808429519131566 0.0161685903826313 0.991915704808684 32 0.00707581526946462 0.0141516305389292 0.992924184730535 33 0.00378434604527411 0.00756869209054821 0.996215653954726 34 0.00717099732908169 0.0143419946581634 0.992829002670918 35 0.00764912622196707 0.0152982524439341 0.992350873778033 36 0.00517164828103617 0.0103432965620723 0.994828351718964 37 0.0273586236409393 0.0547172472818786 0.97264137635906 38 0.119511095467622 0.239022190935243 0.880488904532378 39 0.0863183948972432 0.172636789794486 0.913681605102757 40 0.364523704669585 0.72904740933917 0.635476295330415 41 0.504444226455508 0.991111547088984 0.495555773544492 42 0.508005534775095 0.98398893044981 0.491994465224905 43 0.663396262257293 0.673207475485413 0.336603737742707 44 0.660297260785231 0.679405478429538 0.339702739214769 45 0.765777349466891 0.468445301066218 0.234222650533109 46 0.833076188296082 0.333847623407837 0.166923811703918 47 0.829276754594327 0.341446490811346 0.170723245405673 48 0.807723102940108 0.384553794119783 0.192276897059892 49 0.789564116601246 0.420871766797508 0.210435883398754 50 0.71724540631697 0.565509187366059 0.282754593683029 51 0.750608491150512 0.498783017698976 0.249391508849488 52 0.716205540279308 0.567588919441383 0.283794459720692 53 0.669717308363064 0.660565383273872 0.330282691636936 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0256410256410256 NOK 5% type I error level 14 0.358974358974359 NOK 10% type I error level 17 0.435897435897436 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292672692hnmof3pavaci6vl/10fxpl1292672794.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292672692hnmof3pavaci6vl/10fxpl1292672794.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292672692hnmof3pavaci6vl/1qea91292672794.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292672692hnmof3pavaci6vl/1qea91292672794.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292672692hnmof3pavaci6vl/2qea91292672794.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292672692hnmof3pavaci6vl/2qea91292672794.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292672692hnmof3pavaci6vl/316rc1292672794.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292672692hnmof3pavaci6vl/316rc1292672794.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292672692hnmof3pavaci6vl/416rc1292672794.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292672692hnmof3pavaci6vl/416rc1292672794.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292672692hnmof3pavaci6vl/516rc1292672794.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292672692hnmof3pavaci6vl/516rc1292672794.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292672692hnmof3pavaci6vl/6ufqf1292672794.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292672692hnmof3pavaci6vl/6ufqf1292672794.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292672692hnmof3pavaci6vl/74o801292672794.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292672692hnmof3pavaci6vl/74o801292672794.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292672692hnmof3pavaci6vl/84o801292672794.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292672692hnmof3pavaci6vl/84o801292672794.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292672692hnmof3pavaci6vl/94o801292672794.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292672692hnmof3pavaci6vl/94o801292672794.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

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

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

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

Copyright

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

Software written by Ed van Stee & Patrick Wessa

Disclaimer

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

Privacy Policy

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

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

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

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

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

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

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

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

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

FreeStatistics.org is powered by