multiple regression

*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: Tue, 28 Dec 2010 20:33:13 +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/28/t1293568298933hdiqdvmoopjd.htm/, Retrieved Tue, 28 Dec 2010 21:31:38 +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/28/t1293568298933hdiqdvmoopjd.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 «

-2 3 16 0 6 0 8 17 2 6 -2 3 23 3 7 -4 3 24 1 4 -4 7 27 1 3 -7 4 31 0 0 -9 -4 40 1 6 -13 -6 47 -1 3 -8 8 43 2 1 -13 2 60 2 6 -15 -1 64 0 5 -15 -2 65 1 7 -15 0 65 1 4 -10 10 55 3 3 -12 3 57 3 6 -11 6 57 1 6 -11 7 57 1 5 -17 -4 65 -2 2 -18 -5 69 1 3 -19 -7 70 1 -2 -22 -10 71 -1 -4 -24 -21 71 -4 0 -24 -22 73 -2 1 -20 -16 68 -1 4 -25 -25 65 -5 -3 -22 -22 57 -4 -3 -17 -22 41 -5 0 -9 -19 21 0 6 -11 -21 21 -2 -1 -13 -31 17 -4 0 -11 -28 9 -6 -1 -9 -23 11 -2 1 -7 -17 6 -2 -4 -3 -12 -2 -2 -1 -3 -14 0 1 -1 -6 -18 5 -2 0 -4 -16 3 0 3 -8 -22 7 -1 0 -1 -9 4 2 8 -2 -10 8 3 8 -2 -10 9 2 8 -1 0 14 3 8 1 3 12 4 11 2 2 12 5 13 2 4 7 5 5 -1 -3 15 4 12 1 0 14 5 13 -1 -1 19 6 9 -8 -7 39 4 11 1 2 12 6 7 2 3 11 6 12 -2 -3 17 3 11 -2 -5 16 5 10 -2 0 25 5 13 -2 -3 24 5 14 -6 -7 28 3 10 -4 -7 25 5 13 -5 -7 31 5 12 -2 -4 24 6 13 -1 -3 24 6 17

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 5 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation werkloosheid[t] = + 0.595725392460535 -3.94502235891269indicator[t] + 0.9968486769921economie[t] + 1.06507538235466finaciën[t] + 0.880345457337176spaarvermogen[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) 0.595725392460535 0.456224 1.3058 0.197065 0.098532 indicator -3.94502235891269 0.030602 -128.9139 0 0 economie 0.9968486769921 0.022312 44.6775 0 0 finaciën 1.06507538235466 0.12733 8.3647 0 0 spaarvermogen 0.880345457337176 0.059472 14.8027 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.998682644366743 R-squared 0.99736702415935 Adjusted R-squared 0.997175535007303 F-TEST (value) 5208.47793985419 F-TEST (DF numerator) 4 F-TEST (DF denominator) 55 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.23256411359052 Sum Squared Residuals 83.5567861761156 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 16 16.7583888852853 -0.758388885285256 2 17 15.9827383171297 1.01726168287032 3 23 20.8339604896864 2.16603951031357 4 24 23.9528180707909 0.0471819292090656 5 27 27.0598673214222 -0.0598673214221609 6 31 32.1982766128177 -1.19827661281773 7 40 38.460680041084 1.53931995891598 8 47 47.4758849860297 -0.475884986029705 9 43 43.1411899017453 -0.141189901745319 10 60 61.286936921042 -1.28693692104202 11 64 63.1759393858446 0.82406061415541 12 65 65.0048570058815 -0.00485700588150748 13 65 64.3575179878542 0.642482012145821 14 55 55.8506982705839 -0.850698270583903 15 57 59.4038386214761 -2.40383862147611 16 57 56.3192115288304 0.68078847116961 17 57 56.4357147484853 0.564285251514685 18 65 63.3042509359728 1.6957490640272 19 69 70.3279962222946 -1.32799622229456 20 70 67.8775939405372 2.12240605946283 21 71 72.8312733069152 -1.83127330691524 22 71 70.0821382601122 0.917861739887782 23 73 72.0957858051666 0.904214194833377 24 68 66.0029001858347 1.99709981416533 25 65 66.3336541566903 -1.33365415669031 26 57 58.5542084932832 -1.55420849328322 27 41 40.4050576883767 0.59494231162334 28 21 22.4428745038479 -1.44287450384786 29 21 20.0466529016195 0.953347098380532 30 17 16.7184055421517 0.281594457848317 31 9 8.80841063325611 0.191589366743887 32 11 11.9236017444842 -0.923601744484248 33 6 5.6129218019256 0.387078198074401 34 -2 -2.54188787675311 0.541887876753113 35 0 -1.34035908367332 1.34035908367332 36 5 4.19243259536952 0.807567404630483 37 3 3.0672723682492 -0.0672723682492024 38 7 9.16015798758115 -2.16015798758115 39 4 4.74202408185106 -0.742024081851058 40 8 8.7552731461263 -0.755273146126308 41 9 7.69019776377164 1.30980223622836 42 14 14.7787375571346 -0.778737557134618 43 12 13.5853506246517 -1.58535062465174 44 12 11.4692458857760 0.530754114224026 45 7 6.42017958106277 0.579820418937234 46 15 16.3746487378617 -1.37464873786169 47 14 13.4205708907045 0.57942910929554 48 19 17.8574604845437 1.14253951545631 49 39 39.1220650849449 -0.122065084944909 50 12 11.1972708830203 0.80272911697973 51 11 12.6508244877856 -1.65082448778556 52 17 18.3742502570825 -1.37425025708253 53 16 17.6303582104705 -1.63035821047049 54 25 25.2556379674425 -0.255637967442515 55 24 23.1454373938034 0.854562606196608 56 28 29.2865995274277 -1.28659952742770 57 25 26.1677419463232 -1.16774194632318 58 31 29.2324188478987 1.76758115210131 59 24 22.3333186418288 1.66668135817122 60 24 22.9065267892569 1.09347321074310 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.00139581978660349 0.00279163957320698 0.998604180213396 9 0.0110730367183871 0.0221460734367741 0.988926963281613 10 0.0428564707246801 0.0857129414493603 0.95714352927532 11 0.298110046580889 0.596220093161778 0.701889953419111 12 0.196650813946822 0.393301627893644 0.803349186053178 13 0.156252385408922 0.312504770817844 0.843747614591078 14 0.111256292522322 0.222512585044645 0.888743707477678 15 0.467775068305252 0.935550136610504 0.532224931694748 16 0.444820369783769 0.889640739567538 0.555179630216231 17 0.385648076041868 0.771296152083736 0.614351923958132 18 0.478667531613038 0.957335063226075 0.521332468386962 19 0.440307615047062 0.880615230094123 0.559692384952938 20 0.650862167753017 0.698275664493967 0.349137832246983 21 0.748599495786615 0.502801008426771 0.251400504213385 22 0.691811395609513 0.616377208780974 0.308188604390487 23 0.62657405863983 0.74685188272034 0.37342594136017 24 0.712169023883211 0.575661952233578 0.287830976116789 25 0.742381158222035 0.515237683555931 0.257618841777965 26 0.76499330764948 0.470013384701041 0.235006692350520 27 0.724842981927196 0.550314036145608 0.275157018072804 28 0.794232070796532 0.411535858406935 0.205767929203468 29 0.777824547332806 0.444350905334387 0.222175452667194 30 0.718140799187505 0.563718401624991 0.281859200812495 31 0.684678079836791 0.630643840326418 0.315321920163209 32 0.638944749803539 0.722110500392923 0.361055250196461 33 0.58421899021932 0.83156201956136 0.41578100978068 34 0.569725935398702 0.860548129202596 0.430274064601298 35 0.559757808493422 0.880484383013156 0.440242191506578 36 0.659910976105242 0.680178047789515 0.340089023894758 37 0.63260605322822 0.73478789354356 0.36739394677178 38 0.674208427691286 0.651583144617428 0.325791572308714 39 0.609183498950496 0.781633002099007 0.390816501049504 40 0.588802260780142 0.822395478439716 0.411197739219858 41 0.711572882272585 0.576854235454829 0.288427117727415 42 0.667785589697017 0.664428820605967 0.332214410302983 43 0.633160447748524 0.733679104502953 0.366839552251476 44 0.585321344727907 0.829357310544187 0.414678655272093 45 0.5689056415272 0.8621887169456 0.4310943584728 46 0.485782593986519 0.971565187973039 0.514217406013481 47 0.473899680505432 0.947799361010863 0.526100319494568 48 0.405367680376904 0.810735360753809 0.594632319623096 49 0.337056668196685 0.67411333639337 0.662943331803315 50 0.43261282132077 0.86522564264154 0.56738717867923 51 0.361542924754275 0.72308584950855 0.638457075245725 52 0.349680075158760 0.699360150317519 0.65031992484124 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0222222222222222 NOK 5% type I error level 2 0.0444444444444444 OK 10% type I error level 3 0.0666666666666667 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/28/t1293568298933hdiqdvmoopjd/10ke571293568385.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293568298933hdiqdvmoopjd/10ke571293568385.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293568298933hdiqdvmoopjd/12kn01293568384.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293568298933hdiqdvmoopjd/12kn01293568384.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293568298933hdiqdvmoopjd/2547y1293568385.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293568298933hdiqdvmoopjd/2547y1293568385.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293568298933hdiqdvmoopjd/3547y1293568385.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293568298933hdiqdvmoopjd/3547y1293568385.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293568298933hdiqdvmoopjd/4547y1293568385.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293568298933hdiqdvmoopjd/4547y1293568385.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293568298933hdiqdvmoopjd/5547y1293568385.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293568298933hdiqdvmoopjd/5547y1293568385.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293568298933hdiqdvmoopjd/6gdoj1293568385.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293568298933hdiqdvmoopjd/6gdoj1293568385.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293568298933hdiqdvmoopjd/7r46m1293568385.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293568298933hdiqdvmoopjd/7r46m1293568385.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293568298933hdiqdvmoopjd/8r46m1293568385.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293568298933hdiqdvmoopjd/8r46m1293568385.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293568298933hdiqdvmoopjd/9r46m1293568385.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293568298933hdiqdvmoopjd/9r46m1293568385.ps (open in new window)

Parameters (Session):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 3 ; par2 = Do not include Seasonal 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