*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: Sun, 20 Dec 2009 02:23:55 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/20/t1261301211sc9u39o20qobquu.htm/, Retrieved Sun, 20 Dec 2009 10:27:03 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Dec/20/t1261301211sc9u39o20qobquu.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 «

102.9 127.5 112.7 97 95.1 97.4 134.6 102.9 112.7 97 111.4 131.8 97.4 102.9 112.7 87.4 135.9 111.4 97.4 102.9 96.8 142.7 87.4 111.4 97.4 114.1 141.7 96.8 87.4 111.4 110.3 153.4 114.1 96.8 87.4 103.9 145 110.3 114.1 96.8 101.6 137.7 103.9 110.3 114.1 94.6 148.3 101.6 103.9 110.3 95.9 152.2 94.6 101.6 103.9 104.7 169.4 95.9 94.6 101.6 102.8 168.6 104.7 95.9 94.6 98.1 161.1 102.8 104.7 95.9 113.9 174.1 98.1 102.8 104.7 80.9 179 113.9 98.1 102.8 95.7 190.6 80.9 113.9 98.1 113.2 190 95.7 80.9 113.9 105.9 181.6 113.2 95.7 80.9 108.8 174.8 105.9 113.2 95.7 102.3 180.5 108.8 105.9 113.2 99 196.8 102.3 108.8 105.9 100.7 193.8 99 102.3 108.8 115.5 197 100.7 99 102.3 100.7 216.3 115.5 100.7 99 109.9 221.4 100.7 115.5 100.7 114.6 217.9 109.9 100.7 115.5 85.4 229.7 114.6 109.9 100.7 100.5 227.4 85.4 114.6 109.9 114.8 204.2 100.5 85.4 114.6 116.5 196.6 114.8 100.5 85.4 112.9 198.8 116.5 114.8 100.5 102 207.5 112.9 116.5 114.8 106 190.7 102 112.9 116.5 105.3 201.6 106 102 112.9 118.8 210.5 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 Multiple Linear Regression - Estimated Regression Equation tot.ind.prod.index[t] = + 8.93968106591842 + 0.0278761866410181prijsindex.grondst.incl.energie[t] + 0.0209391269251189`y(t-1)`[t] + 0.327716059508625`y(t-2)`[t] + 0.645907122351861`y(t-3)`[t] -6.5190957391177M1[t] -11.7428285168030M2[t] -6.29787050803359M3[t] -28.2519629104457M4[t] -18.8007596663826M5[t] -1.50640187826471M6[t] + 11.9119058393715M7[t] -6.17371142112734M8[t] -22.9232796894441M9[t] -20.3332537513160M10[t] -13.2633906802557M11[t] -0.147205196270872t + 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) 8.93968106591842 26.166019 0.3417 0.734358 0.367179 prijsindex.grondst.incl.energie 0.0278761866410181 0.015012 1.857 0.070511 0.035256 `y(t-1)` 0.0209391269251189 0.139073 0.1506 0.881059 0.44053 `y(t-2)` 0.327716059508625 0.146297 2.2401 0.030569 0.015284 `y(t-3)` 0.645907122351861 0.159049 4.0611 0.000215 0.000107 M1 -6.5190957391177 2.959739 -2.2026 0.033303 0.016651 M2 -11.7428285168030 3.114127 -3.7708 0.000514 0.000257 M3 -6.29787050803359 3.308044 -1.9038 0.063971 0.031985 M4 -28.2519629104457 3.155708 -8.9527 0 0 M5 -18.8007596663826 3.857053 -4.8744 1.7e-05 8e-06 M6 -1.50640187826471 3.817827 -0.3946 0.695205 0.347602 M7 11.9119058393715 3.812479 3.1245 0.003266 0.001633 M8 -6.17371142112734 3.68833 -1.6739 0.101774 0.050887 M9 -22.9232796894441 4.121535 -5.5618 2e-06 1e-06 M10 -20.3332537513160 3.579621 -5.6803 1e-06 1e-06 M11 -13.2633906802557 3.119782 -4.2514 0.00012 6e-05 t -0.147205196270872 0.060013 -2.4529 0.018514 0.009257 Multiple Linear Regression - Regression Statistics Multiple R 0.929546668821959 R-squared 0.864057009518 Adjusted R-squared 0.811006086403074 F-TEST (value) 16.2873133733443 F-TEST (DF numerator) 16 F-TEST (DF denominator) 41 p-value 6.66466881682481e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.97108867293895 Sum Squared Residuals 646.551355182106 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 102.9 101.401658639719 1.49834136028072 2 97.4 102.395803813802 -4.99580381380193 3 111.4 114.429462543357 -3.02946254335714 4 87.4 84.6032769605083 2.79672303949167 5 96.8 94.6298296914422 2.17017030855783 6 114.1 113.123448174463 0.976551825536647 7 110.3 114.661708998270 -4.36170899826953 8 103.9 107.856172671007 -3.95617267100652 9 101.6 100.550764822173 1.04923517782681 10 94.6 98.6890833047051 -4.08908330470515 11 95.9 100.686331898997 -4.78633189899693 12 104.7 110.529609860240 -5.82960986024025 13 102.8 99.9299533133781 2.87004668662187 14 98.1 98.0337401811899 0.0662598188101135 15 113.9 108.656791687104 5.24320831289639 16 80.9 84.2554365962194 -3.35543659621941 17 95.7 95.333957485701 0.36604251429894 18 113.2 112.16498601343 1.03501398656999 19 105.9 109.103625931317 -3.20362593131658 20 108.8 105.822846231043 2.97715376895690 21 102.3 98.0567379051368 4.24326209486323 22 99 97.0530907436357 1.94690925636427 23 100.7 103.565997207664 -2.86599720766351 24 115.5 111.527123713007 3.97287628699327 25 100.7 104.134356055685 -3.43435605568510 26 109.9 104.543927343832 5.35607265616787 27 114.6 114.645981200878 -0.0459812008781376 28 85.4 86.427598837779 -1.02759883777902 29 100.5 102.538670155411 -2.03867015541112 30 114.8 112.821730571158 1.97826942884230 31 116.5 112.268428114986 4.23157188501364 32 112.9 108.572066983086 4.32793301691394 33 102 101.636024636141 0.363975363858816 34 106 103.300553252713 2.69944674728739 35 105.3 104.713447380479 0.586552619521113 36 118.8 112.333550141120 6.46644985887962 37 106.1 108.666545093306 -2.56654509330558 38 109.3 107.010074881113 2.28992511888722 39 117.2 117.138868876906 0.0611311230941532 40 92.5 88.4054765064953 4.09452349350471 41 104.2 101.588889245120 2.61111075488027 42 112.5 116.421190111643 -3.92119011164308 43 122.4 118.178540253583 4.22145974641741 44 113.3 111.040660266248 2.25933973375173 45 100 102.438891248742 -2.43889124874154 46 110.7 108.352787830606 2.34721216939407 47 112.8 105.734223512861 7.06577648713933 48 109.8 114.409716285633 -4.60971628563265 49 117.3 115.667486897912 1.63251310208809 50 109.1 111.816453780063 -2.71645378006328 51 115.9 118.128895691755 -2.22889569175527 52 96 98.508211098998 -2.50821109899796 53 99.8 102.908653422326 -3.10865342232592 54 116.8 116.868645129306 -0.068645129305859 55 115.7 116.587696701845 -0.887696701844936 56 99.4 105.008253848616 -5.60825384861605 57 94.3 97.5175813878073 -3.21758138780731 58 91 93.9044848683406 -2.90448486834056 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 20 0.477011089876752 0.954022179753505 0.522988910123248 21 0.391852030106857 0.783704060213714 0.608147969893143 22 0.403529733471464 0.80705946694293 0.596470266528536 23 0.529361960994027 0.941276078011945 0.470638039005973 24 0.493239014134886 0.986478028269773 0.506760985865114 25 0.550895480505404 0.898209038989192 0.449104519494596 26 0.662803395840522 0.674393208318956 0.337196604159478 27 0.551917527646938 0.896164944706124 0.448082472353062 28 0.534424508801414 0.931150982397173 0.465575491198586 29 0.641793178816782 0.716413642366436 0.358206821183218 30 0.566883170184653 0.866233659630695 0.433116829815347 31 0.488791013906447 0.977582027812893 0.511208986093553 32 0.384157120656949 0.768314241313899 0.615842879343051 33 0.336507634533565 0.67301526906713 0.663492365466435 34 0.230738459258926 0.461476918517851 0.769261540741074 35 0.525969182863191 0.948061634273618 0.474030817136809 36 0.479927943045405 0.95985588609081 0.520072056954595 37 0.454302885873803 0.908605771747605 0.545697114126197 38 0.306101719851925 0.612203439703850 0.693898280148075 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/20/t1261301211sc9u39o20qobquu/10ozc61261301029.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261301211sc9u39o20qobquu/10ozc61261301029.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261301211sc9u39o20qobquu/1m7el1261301029.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261301211sc9u39o20qobquu/1m7el1261301029.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261301211sc9u39o20qobquu/2zpnp1261301029.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261301211sc9u39o20qobquu/2zpnp1261301029.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261301211sc9u39o20qobquu/3ddx21261301029.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261301211sc9u39o20qobquu/3ddx21261301029.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261301211sc9u39o20qobquu/4ygr41261301029.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261301211sc9u39o20qobquu/4ygr41261301029.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261301211sc9u39o20qobquu/5ziac1261301029.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261301211sc9u39o20qobquu/5ziac1261301029.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261301211sc9u39o20qobquu/6jc251261301029.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261301211sc9u39o20qobquu/6jc251261301029.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261301211sc9u39o20qobquu/7wqtq1261301029.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261301211sc9u39o20qobquu/7wqtq1261301029.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261301211sc9u39o20qobquu/8vxwl1261301029.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261301211sc9u39o20qobquu/8vxwl1261301029.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261301211sc9u39o20qobquu/9org31261301029.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t1261301211sc9u39o20qobquu/9org31261301029.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