*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 00:35:08 +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/t1293496792f56kcpmli49beog.htm/, Retrieved Tue, 28 Dec 2010 01:39:55 +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/t1293496792f56kcpmli49beog.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 «

99 94.6 106.3 95.9 128.9 104.7 111.1 102.8 102.9 98.1 130 113.9 87 80.9 87.5 95.7 117.6 113.2 103.4 105.9 110.8 108.8 112.6 102.3 102.5 99 112.4 100.7 135.6 115.5 105.1 100.7 127.7 109.9 137 114.6 91 85.4 90.5 100.5 122.4 114.8 123.3 116.5 124.3 112.9 120 102 118.1 106 119 105.3 142.7 118.8 123.6 106.1 129.6 109.3 151.6 117.2 110.4 92.5 99.2 104.2 130.5 112.5 136.2 122.4 129.7 113.3 128 100 121.6 110.7 135.8 112.8 143.8 109.8 147.5 117.3 136.2 109.1 156.6 115.9 123.3 96 104.5 99.8 139.8 116.8 136.5 115.7 112.1 99.4 118.5 94.3 94.4 91 102.3 93.2 111.4 103.1 99.2 94.1 87.8 91.8 115.8 102.7 79.7 82.6 72.7 89.1 104.5 104.5 103 105.1 95.1 95.1 104.2 88.7 78.3 86.3

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 6 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation ipi[t] = -52.2182826720229 + 1.61621931142924`tip `[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) -52.2182826720229 13.736251 -3.8015 0.000343 0.000172 `tip ` 1.61621931142924 0.131945 12.2492 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.847208157154555 R-squared 0.717761661549217 Adjusted R-squared 0.712977960897509 F-TEST (value) 150.043180752315 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 10.1828542822533 Sum Squared Residuals 6117.74075868272 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 99 100.676064189184 -1.6760641891836 2 106.3 102.777149294042 3.52285070595836 3 128.9 116.999879234619 11.900120765381 4 111.1 113.929062542903 -2.82906254290342 5 102.9 106.332831779186 -3.43283177918595 6 130 131.869096899768 -1.86909689976804 7 87 78.533859622603 8.46614037739703 8 87.5 102.453905431756 -14.9539054317558 9 117.6 130.737743381768 -13.1377433817676 10 103.4 118.939342408334 -15.5393424083341 11 110.8 123.626378411479 -12.8263784114789 12 112.6 113.120952887189 -0.520952887188796 13 102.5 107.787429159472 -5.28742915947229 14 112.4 110.535001988902 1.864998011098 15 135.6 134.455047798055 1.14495220194517 16 105.1 110.535001988902 -5.43500198890201 17 127.7 125.404219654051 2.29578034594894 18 137 133.000450417768 3.9995495822315 19 91 85.8068465240346 5.19315347596543 20 90.5 110.211758126616 -19.7117581266162 21 122.4 133.323694280054 -10.9236942800543 22 123.3 136.071267109484 -12.7712671094841 23 124.3 130.252877588339 -5.9528775883388 24 120 112.63608709376 7.36391290623998 25 118.1 119.100964339477 -1.00096433947701 26 119 117.969610821477 1.03038917852347 27 142.7 139.788571525771 2.91142847422866 28 123.6 119.26258627062 4.33741372938008 29 129.6 124.434488067193 5.16551193280649 30 151.6 137.202620627485 14.3973793725154 31 110.4 97.2820036351822 13.1179963648178 32 99.2 116.191769578904 -16.9917695789044 33 130.5 129.606389863767 0.893610136232906 34 136.2 145.606961046917 -9.40696104691664 35 129.7 130.89936531291 -1.1993653129105 36 128 109.403648470902 18.5963515290985 37 121.6 126.697195103194 -5.09719510319446 38 135.8 130.091255657196 5.70874434280415 39 143.8 125.242597722908 18.5574022770919 40 147.5 137.364242558627 10.1357574413725 41 136.2 124.111244204908 12.0887557950923 42 156.6 135.101535522627 21.4984644773735 43 123.3 102.938771225185 20.3612287748154 44 104.5 109.080404608616 -4.58040460861568 45 139.8 136.556132902913 3.24386709708717 46 136.5 134.778291660341 1.72170833965932 47 112.1 108.433916884044 3.666083115956 48 118.5 100.191198395755 18.3088016042452 49 94.4 94.8576746680383 -0.457674668038324 50 102.3 98.4133571531827 3.88664284681733 51 111.4 114.413928336332 -3.01392833633218 52 99.2 99.867954533469 -0.667954533468976 53 87.8 96.1506501171817 -8.35065011718173 54 115.8 113.76744061176 2.0325593882395 55 79.7 81.2814324520327 -1.58143245203266 56 72.7 91.7868579763228 -19.0868579763228 57 104.5 116.676635372333 -12.1766353723331 58 103 117.646366959191 -14.6463669591907 59 95.1 101.484173844898 -6.38417384489823 60 104.2 91.1403702517511 13.0596297482489 61 78.3 87.2614439043209 -8.96144390432088 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.233263058219805 0.46652611643961 0.766736941780195 6 0.184131309046911 0.368262618093821 0.81586869095309 7 0.123871964078668 0.247743928157336 0.876128035921332 8 0.351161450582471 0.702322901164942 0.648838549417529 9 0.346509112632392 0.693018225264783 0.653490887367609 10 0.396575108343172 0.793150216686344 0.603424891656828 11 0.352909740369222 0.705819480738444 0.647090259630778 12 0.269888297196818 0.539776594393637 0.730111702803182 13 0.199708281593313 0.399416563186626 0.800291718406687 14 0.152104954247203 0.304209908494405 0.847895045752797 15 0.143291778243611 0.286583556487222 0.85670822175639 16 0.102118317740118 0.204236635480236 0.897881682259882 17 0.08422020331362 0.16844040662724 0.91577979668638 18 0.0773784965732228 0.154756993146446 0.922621503426777 19 0.0552923811178863 0.110584762235773 0.944707618882114 20 0.154996590673079 0.309993181346159 0.84500340932692 21 0.138600803981079 0.277201607962158 0.861399196018921 22 0.138035826355402 0.276071652710803 0.861964173644598 23 0.107583997029549 0.215167994059097 0.892416002970452 24 0.107977463742366 0.215954927484732 0.892022536257634 25 0.0786331231686774 0.157266246337355 0.921366876831323 26 0.057542848147071 0.115085696294142 0.942457151852929 27 0.0516591620372754 0.103318324074551 0.948340837962725 28 0.0411334188413259 0.0822668376826518 0.958866581158674 29 0.0343950942575223 0.0687901885150445 0.965604905742478 30 0.0676114657346181 0.135222931469236 0.932388534265382 31 0.0867773113742927 0.173554622748585 0.913222688625707 32 0.155802356107687 0.311604712215375 0.844197643892313 33 0.118871035622943 0.237742071245885 0.881128964377057 34 0.130379821663218 0.260759643326436 0.869620178336782 35 0.103212176228542 0.206424352457083 0.896787823771458 36 0.201872633628875 0.403745267257749 0.798127366371125 37 0.181603513585824 0.363207027171648 0.818396486414176 38 0.149584229487472 0.299168458974943 0.850415770512528 39 0.240015282546624 0.480030565093248 0.759984717453376 40 0.218744996378279 0.437489992756558 0.781255003621721 41 0.218378071550886 0.436756143101773 0.781621928449113 42 0.411830216536242 0.823660433072484 0.588169783463758 43 0.667974343656194 0.664051312687612 0.332025656343806 44 0.597589847561456 0.804820304877087 0.402410152438544 45 0.529523006769836 0.940953986460329 0.470476993230164 46 0.464220027071545 0.92844005414309 0.535779972928455 47 0.408048950717125 0.81609790143425 0.591951049282875 48 0.713489549784749 0.573020900430501 0.286510450215251 49 0.631630834377686 0.736738331244627 0.368369165622314 50 0.597361939648125 0.80527612070375 0.402638060351875 51 0.511368237135366 0.977263525729267 0.488631762864634 52 0.425426724398597 0.850853448797194 0.574573275601403 53 0.33241365068466 0.66482730136932 0.66758634931534 54 0.350055590912318 0.700111181824636 0.649944409087682 55 0.230488385153196 0.460976770306391 0.769511614846804 56 0.414457077682764 0.828914155365527 0.585542922317236 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 2 0.0384615384615385 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/28/t1293496792f56kcpmli49beog/10dbzg1293496497.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293496792f56kcpmli49beog/10dbzg1293496497.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293496792f56kcpmli49beog/1oa2m1293496497.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293496792f56kcpmli49beog/1oa2m1293496497.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293496792f56kcpmli49beog/2z11p1293496497.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293496792f56kcpmli49beog/2z11p1293496497.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293496792f56kcpmli49beog/3z11p1293496497.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293496792f56kcpmli49beog/3z11p1293496497.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293496792f56kcpmli49beog/4z11p1293496497.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293496792f56kcpmli49beog/4z11p1293496497.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293496792f56kcpmli49beog/5aa0a1293496497.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293496792f56kcpmli49beog/5aa0a1293496497.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293496792f56kcpmli49beog/6aa0a1293496497.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293496792f56kcpmli49beog/6aa0a1293496497.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293496792f56kcpmli49beog/72j0v1293496497.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293496792f56kcpmli49beog/72j0v1293496497.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293496792f56kcpmli49beog/82j0v1293496497.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293496792f56kcpmli49beog/82j0v1293496497.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293496792f56kcpmli49beog/92j0v1293496497.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293496792f56kcpmli49beog/92j0v1293496497.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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