Model 1

*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: Thu, 19 Nov 2009 13:32:12 -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/19/t1258662899a6dlsjc9dugmhjf.htm/, Retrieved Thu, 19 Nov 2009 21:35:11 +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/19/t1258662899a6dlsjc9dugmhjf.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 «

3 101.2 3.21 101.1 3.37 100.7 3.51 100.1 3.75 99.9 4.11 99.7 4.25 99.5 4.25 99.2 4.5 99 4.7 99 4.75 99.3 4.75 99.5 4.75 99.7 4.75 100 4.75 100.4 4.75 100.6 4.58 100.7 4.5 100.7 4.5 100.6 4.49 100.5 4.03 100.6 3.75 100.5 3.39 100.4 3.25 100.3 3.25 100.4 3.25 100.4 3.25 100.4 3.25 100.4 3.25 100.4 3.25 100.5 3.25 100.6 3.25 100.6 3.25 100.5 3.25 100.5 3.25 100.7 2.85 101.1 2.75 101.5 2.75 101.9 2.55 102.1 2.5 102.1 2.5 102.1 2.1 102.4 2 102.8 2 103.1 2 103.1 2 102.9 2 102.4 2 101.9 2 101.3 2 100.7 2 100.6 2 101 2 101.5 2 101.9 2 102.1 2 102.3 2 102.5 2 102.9 2 103.6 2 104.3

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Tprod[t] = + 104.057458254309 -0.954366625790823Rente[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) 104.057458254309 0.308823 336.9486 0 0 Rente -0.954366625790823 0.093353 -10.2232 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.801939374471285 R-squared 0.643106760327396 Adjusted R-squared 0.636953428608903 F-TEST (value) 104.513585444225 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 1.35447209004269e-14 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.715892288571204 Sum Squared Residuals 29.7251025924716 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 101.2 101.194358376937 0.00564162306336613 2 101.1 100.993941385520 0.106058614479800 3 100.7 100.841242725394 -0.14124272539365 4 100.1 100.707631397783 -0.607631397782944 5 99.9 100.478583407593 -0.578583407593135 6 99.7 100.135011422308 -0.435011422308442 7 99.5 100.001400094698 -0.50140009469773 8 99.2 100.001400094698 -0.801400094697727 9 99 99.76280843825 -0.762808438250024 10 99 99.5719351130919 -0.571935113091859 11 99.3 99.5242167818023 -0.224216781802321 12 99.5 99.5242167818023 -0.0242167818023186 13 99.7 99.5242167818023 0.175783218197684 14 100 99.5242167818023 0.475783218197681 15 100.4 99.5242167818023 0.875783218197687 16 100.6 99.5242167818023 1.07578321819768 17 100.7 99.6864591081868 1.01354089181324 18 100.7 99.76280843825 0.937191561749979 19 100.6 99.76280843825 0.83719156174997 20 100.5 99.772352104508 0.727647895492068 21 100.6 100.211360752372 0.388639247628284 22 100.5 100.478583407593 0.0214165924068594 23 100.4 100.822155392878 -0.422155392877831 24 100.3 100.955766720489 -0.655766720488555 25 100.4 100.955766720489 -0.555766720488546 26 100.4 100.955766720489 -0.555766720488546 27 100.4 100.955766720489 -0.555766720488546 28 100.4 100.955766720489 -0.555766720488546 29 100.4 100.955766720489 -0.555766720488546 30 100.5 100.955766720489 -0.455766720488552 31 100.6 100.955766720489 -0.355766720488557 32 100.6 100.955766720489 -0.355766720488557 33 100.5 100.955766720489 -0.455766720488552 34 100.5 100.955766720489 -0.455766720488552 35 100.7 100.955766720489 -0.255766720488549 36 101.1 101.337513370805 -0.237513370804886 37 101.5 101.432950033384 0.0670499666160373 38 101.9 101.432950033384 0.467049966616043 39 102.1 101.623823358542 0.476176641457867 40 102.1 101.671541689832 0.428458310168326 41 102.1 101.671541689832 0.428458310168326 42 102.4 102.053288340148 0.346711659852009 43 102.8 102.148725002727 0.651274997272918 44 103.1 102.148725002727 0.951274997272915 45 103.1 102.148725002727 0.951274997272915 46 102.9 102.148725002727 0.751274997272926 47 102.4 102.148725002727 0.251274997272926 48 101.9 102.148725002727 -0.248725002727074 49 101.3 102.148725002727 -0.848725002727082 50 100.7 102.148725002727 -1.44872500272708 51 100.6 102.148725002727 -1.54872500272709 52 101 102.148725002727 -1.14872500272708 53 101.5 102.148725002727 -0.648725002727079 54 101.9 102.148725002727 -0.248725002727074 55 102.1 102.148725002727 -0.048725002727085 56 102.3 102.148725002727 0.151274997272918 57 102.5 102.148725002727 0.351274997272921 58 102.9 102.148725002727 0.751274997272926 59 103.6 102.148725002727 1.45127499727291 60 104.3 102.148725002727 2.15127499727292 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0238414848985247 0.0476829697970493 0.976158515101475 6 0.0175742657329251 0.0351485314658503 0.982425734267075 7 0.00588102145143733 0.0117620429028747 0.994118978548563 8 0.00212059348618305 0.0042411869723661 0.997879406513817 9 0.000587320787481028 0.00117464157496206 0.999412679212519 10 0.000355771251568655 0.00071154250313731 0.999644228748431 11 0.000916860634170218 0.00183372126834044 0.99908313936583 12 0.00188229342758154 0.00376458685516307 0.998117706572418 13 0.00370584581143945 0.0074116916228789 0.99629415418856 14 0.0101284476909174 0.0202568953818349 0.989871552309083 15 0.0396372483011431 0.0792744966022861 0.960362751698857 16 0.101478894352433 0.202957788704866 0.898521105647567 17 0.158906136109628 0.317812272219257 0.841093863890372 18 0.202557896956443 0.405115793912886 0.797442103043557 19 0.229450237180475 0.45890047436095 0.770549762819525 20 0.251077598223512 0.502155196447023 0.748922401776488 21 0.236879486477723 0.473758972955446 0.763120513522277 22 0.191609557800884 0.383219115601767 0.808390442199116 23 0.143768948443547 0.287537896887095 0.856231051556453 24 0.111371536587479 0.222743073174958 0.888628463412521 25 0.0804102619006545 0.160820523801309 0.919589738099346 26 0.0564017844102695 0.112803568820539 0.94359821558973 27 0.0384875308259434 0.0769750616518868 0.961512469174057 28 0.0255948325572355 0.0511896651144709 0.974405167442765 29 0.0166304111824254 0.0332608223648507 0.983369588817575 30 0.0102533731673888 0.0205067463347776 0.989746626832611 31 0.00610286334121223 0.0122057266824245 0.993897136658788 32 0.0035329438724069 0.0070658877448138 0.996467056127593 33 0.00204770988652225 0.0040954197730445 0.997952290113478 34 0.00120545321976379 0.00241090643952759 0.998794546780236 35 0.000709218934285636 0.00141843786857127 0.999290781065714 36 0.00049634629441276 0.00099269258882552 0.999503653705587 37 0.000449264742047960 0.000898529484095919 0.999550735257952 38 0.000635528068441109 0.00127105613688222 0.999364471931559 39 0.000780352109471887 0.00156070421894377 0.999219647890528 40 0.000730067843889776 0.00146013568777955 0.99926993215611 41 0.000598886702834567 0.00119777340566913 0.999401113297165 42 0.000426411742353679 0.000852823484707357 0.999573588257646 43 0.000450595353112242 0.000901190706224483 0.999549404646888 44 0.000759443295558633 0.00151888659111727 0.999240556704441 45 0.00109893598646372 0.00219787197292743 0.998901064013536 46 0.00102634543867735 0.00205269087735470 0.998973654561323 47 0.00053025011919278 0.00106050023838556 0.999469749880807 48 0.000260090374649883 0.000520180749299765 0.99973990962535 49 0.000274141591900503 0.000548283183801007 0.9997258584081 50 0.00147642595810291 0.00295285191620582 0.998523574041897 51 0.0118934802413496 0.0237869604826992 0.98810651975865 52 0.0409522125086278 0.0819044250172556 0.959047787491372 53 0.068976932881847 0.137953865763694 0.931023067118153 54 0.0798764811657025 0.159752962331405 0.920123518834298 55 0.0897685256990436 0.179537051398087 0.910231474300956 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 25 0.490196078431373 NOK 5% type I error level 33 0.647058823529412 NOK 10% type I error level 37 0.725490196078431 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662899a6dlsjc9dugmhjf/10rtq21258662728.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662899a6dlsjc9dugmhjf/10rtq21258662728.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662899a6dlsjc9dugmhjf/1vx851258662728.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662899a6dlsjc9dugmhjf/1vx851258662728.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662899a6dlsjc9dugmhjf/2gtw91258662728.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662899a6dlsjc9dugmhjf/2gtw91258662728.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662899a6dlsjc9dugmhjf/376jd1258662728.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662899a6dlsjc9dugmhjf/376jd1258662728.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662899a6dlsjc9dugmhjf/4ci5b1258662728.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662899a6dlsjc9dugmhjf/4ci5b1258662728.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662899a6dlsjc9dugmhjf/5oqe71258662728.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662899a6dlsjc9dugmhjf/5oqe71258662728.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662899a6dlsjc9dugmhjf/6kqai1258662728.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662899a6dlsjc9dugmhjf/6kqai1258662728.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662899a6dlsjc9dugmhjf/7utce1258662728.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662899a6dlsjc9dugmhjf/7utce1258662728.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662899a6dlsjc9dugmhjf/8lo3s1258662728.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662899a6dlsjc9dugmhjf/8lo3s1258662728.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662899a6dlsjc9dugmhjf/9g0kc1258662728.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662899a6dlsjc9dugmhjf/9g0kc1258662728.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; 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