WS 7 4

*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, 14 Nov 2009 06:58:56 -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/14/t1258207210ohkxjykpseglkrp.htm/, Retrieved Sat, 14 Nov 2009 15:00:22 +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/Nov/14/t1258207210ohkxjykpseglkrp.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 «

103,91 100,30 103,88 103,77 103,66 103,64 103,63 103,91 98,50 103,91 103,88 103,77 103,66 103,64 103,92 95,10 103,91 103,91 103,88 103,77 103,66 104,05 93,10 103,92 103,91 103,91 103,88 103,77 104,23 92,20 104,05 103,92 103,91 103,91 103,88 104,30 89,00 104,23 104,05 103,92 103,91 103,91 104,31 86,40 104,30 104,23 104,05 103,92 103,91 104,31 84,50 104,31 104,30 104,23 104,05 103,92 104,34 82,70 104,31 104,31 104,30 104,23 104,05 104,55 80,80 104,34 104,31 104,31 104,30 104,23 104,65 81,80 104,55 104,34 104,31 104,31 104,30 104,73 81,80 104,65 104,55 104,34 104,31 104,31 104,75 82,90 104,73 104,65 104,55 104,34 104,31 104,75 83,80 104,75 104,73 104,65 104,55 104,34 104,76 86,20 104,75 104,75 104,73 104,65 104,55 104,94 86,10 104,76 104,75 104,75 104,73 104,65 105,29 86,20 104,94 104,76 104,75 104,75 104,73 105,38 88,80 105,29 104,94 104,76 104,75 104,75 105,43 89,60 105,38 105,29 104,94 104,76 104,75 105,43 87,80 105,43 105,38 105,29 104,94 104,76 105,42 88,30 105,43 105,43 105,38 105,29 10 etc...

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 R Framework error message Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values. Multiple Linear Regression - Estimated Regression Equation Y[t] = -2.83103972160239 -0.00497969791234237X[t] + 0.932713845855733Y1[t] -0.0849261973825332Y2[t] -0.295020473860288Y3[t] + 0.179390247778269Y4[t] + 0.299672934182992`Y5 `[t] + 0.066291966934211M1[t] + 0.070275643501231M2[t] + 0.0678099255876927M3[t] + 0.155510564164373M4[t] + 0.181984794020036M5[t] + 0.128406954400737M6[t] + 0.123356529101140M7[t] + 0.144114792469188M8[t] + 0.102750256297642M9[t] + 0.080114755808093M10[t] + 0.0357874509842374M11[t] -0.000149240410139286t + 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) -2.83103972160239 4.34225 -0.652 0.518558 0.259279 X -0.00497969791234237 0.001643 -3.0301 0.004506 0.002253 Y1 0.932713845855733 0.157745 5.9128 1e-06 0 Y2 -0.0849261973825332 0.224609 -0.3781 0.707572 0.353786 Y3 -0.295020473860288 0.243782 -1.2102 0.234096 0.117048 Y4 0.179390247778269 0.239424 0.7493 0.458569 0.229285 `Y5 ` 0.299672934182992 0.185883 1.6122 0.115661 0.057831 M1 0.066291966934211 0.049712 1.3335 0.190735 0.095367 M2 0.070275643501231 0.051292 1.3701 0.179141 0.089571 M3 0.0678099255876927 0.050229 1.35 0.185442 0.092721 M4 0.155510564164373 0.048629 3.1979 0.002883 0.001442 M5 0.181984794020036 0.051174 3.5562 0.001076 0.000538 M6 0.128406954400737 0.053728 2.3899 0.022209 0.011104 M7 0.123356529101140 0.055083 2.2395 0.031396 0.015698 M8 0.144114792469188 0.065772 2.1911 0.034997 0.017499 M9 0.102750256297642 0.061791 1.6629 0.105024 0.052512 M10 0.080114755808093 0.051751 1.5481 0.130347 0.065174 M11 0.0357874509842374 0.050554 0.7079 0.483566 0.241783 t -0.000149240410139286 0.003603 -0.0414 0.967193 0.483596 Multiple Linear Regression - Regression Statistics Multiple R 0.997724269607044 R-squared 0.99545371816291 Adjusted R-squared 0.993180577244363 F-TEST (value) 437.919932742148 F-TEST (DF numerator) 18 F-TEST (DF denominator) 36 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.0701871577428226 Sum Squared Residuals 0.177344536032571 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 103.91 103.878451238188 0.0315487618122744 2 103.91 103.884020946423 0.0259790535770017 3 103.92 103.889063308894 0.0309366911055615 4 104.05 104.039747577144 0.0102524228558393 5 104.23 104.229303562892 0.000696437108322132 6 104.3 104.354399586063 -0.0543995860629370 7 104.31 104.375591629482 -0.0655916294822722 8 104.31 104.382258159374 -0.0722581593735686 9 104.34 104.399454869934 -0.0594548699339316 10 104.55 104.477661211202 0.0723387887978133 11 104.65 104.644298097635 0.00570190236534622 12 104.73 104.677944404502 0.0520555954984488 13 104.75 104.748161359175 0.00183864082505709 14 104.75 104.776534341010 -0.0265343410101180 15 104.76 104.817538286797 -0.0575382867965953 16 104.94 104.953332896976 -0.0133328969762984 17 105.29 105.173760786601 0.116239213399018 18 105.38 105.421292876465 -0.0412928764651777 19 105.43 105.415019746652 0.0149802533483907 20 105.43 105.415614368471 0.0143856315290806 21 105.42 105.457540305292 -0.0375403052918494 22 105.52 105.530214142131 -0.0102141421309762 23 105.69 105.603847044932 0.086152955067867 24 105.72 105.733423090087 -0.0134230900865648 25 105.74 105.762393006709 -0.0223930067093367 26 105.74 105.730689745631 0.00931025436913267 27 105.74 105.772013647190 -0.0320136471895232 28 105.95 105.916464349409 0.033535650590646 29 106.17 106.142771753015 0.0272282469853371 30 106.34 106.282898646098 0.0571013539019569 31 106.37 106.354626331666 0.0153736683335060 32 106.37 106.356567066317 0.0134329336831226 33 106.36 106.356781677287 0.00321832271262681 34 106.44 106.381867414091 0.0581325859094093 35 106.29 106.457730039545 -0.167730039544940 36 106.23 106.273587383882 -0.0435873838823813 37 106.23 106.232766994951 -0.00276699495084360 38 106.23 106.268422316619 -0.0384223166185613 39 106.23 106.269120579096 -0.0391205790960142 40 106.34 106.310419048302 0.0295809516981365 41 106.44 106.443770825346 -0.00377082534608349 42 106.44 106.471483399234 -0.0314833992339631 43 106.48 106.408407888759 0.0715921112406273 44 106.5 106.455560405839 0.0444395941613654 45 106.57 106.476223147487 0.0937768525131542 46 106.4 106.520257232576 -0.120257232576246 47 106.37 106.294124817888 0.0758751821117265 48 106.25 106.245045121530 0.00495487847049753 49 106.21 106.218227400977 -0.00822740097715119 50 106.21 106.180332650317 0.0296673496825448 51 106.24 106.142264178023 0.0977358219765712 52 106.19 106.250036128168 -0.0600361281683234 53 106.08 106.220393072147 -0.140393072146593 54 106.13 106.059925492140 0.0700745078601208 55 106.09 106.126354403440 -0.0363544034402519 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 22 0.171047795996972 0.342095591993943 0.828952204003028 23 0.0790724928354266 0.158144985670853 0.920927507164573 24 0.0553682055472368 0.110736411094474 0.944631794452763 25 0.0903657768176242 0.180731553635248 0.909634223182376 26 0.204462626340326 0.408925252680652 0.795537373659674 27 0.233771117010724 0.467542234021448 0.766228882989276 28 0.146088471827494 0.292176943654988 0.853911528172506 29 0.158435154434980 0.316870308869960 0.84156484556502 30 0.159355091175182 0.318710182350364 0.840644908824818 31 0.08838058442943 0.17676116885886 0.91161941557057 32 0.306315355309148 0.612630710618297 0.693684644690852 33 0.339060398319434 0.678120796638869 0.660939601680566 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/Nov/14/t1258207210ohkxjykpseglkrp/106c3j1258207131.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258207210ohkxjykpseglkrp/106c3j1258207131.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258207210ohkxjykpseglkrp/1orbs1258207131.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258207210ohkxjykpseglkrp/1orbs1258207131.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258207210ohkxjykpseglkrp/2zjom1258207131.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258207210ohkxjykpseglkrp/2zjom1258207131.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258207210ohkxjykpseglkrp/33v301258207131.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258207210ohkxjykpseglkrp/33v301258207131.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258207210ohkxjykpseglkrp/49pa81258207131.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258207210ohkxjykpseglkrp/49pa81258207131.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258207210ohkxjykpseglkrp/536j11258207131.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258207210ohkxjykpseglkrp/536j11258207131.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258207210ohkxjykpseglkrp/6cvsi1258207131.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258207210ohkxjykpseglkrp/6cvsi1258207131.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258207210ohkxjykpseglkrp/7z7k21258207131.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258207210ohkxjykpseglkrp/7z7k21258207131.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258207210ohkxjykpseglkrp/89qqy1258207131.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258207210ohkxjykpseglkrp/89qqy1258207131.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258207210ohkxjykpseglkrp/9krzm1258207131.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t1258207210ohkxjykpseglkrp/9krzm1258207131.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