*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: Fri, 20 Nov 2009 00:55:13 -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/20/t1258706956axd0lmntberhas7.htm/, Retrieved Fri, 20 Nov 2009 09:49:28 +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/20/t1258706956axd0lmntberhas7.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 «

8.2 267722 8 266003 7.9 262971 7.6 265521 7.6 264676 8.3 270223 8.4 269508 8.4 268457 8.4 265814 8.4 266680 8.6 263018 8.9 269285 8.8 269829 8.3 270911 7.5 266844 7.2 271244 7.4 269907 8.8 271296 9.3 270157 9.3 271322 8.7 267179 8.2 264101 8.3 265518 8.5 269419 8.6 268714 8.5 272482 8.2 268351 8.1 268175 7.9 270674 8.6 272764 8.7 272599 8.7 270333 8.5 270846 8.4 270491 8.5 269160 8.7 274027 8.7 273784 8.6 276663 8.5 274525 8.3 271344 8 271115 8.2 270798 8.1 273911 8.1 273985 8 271917 7.9 273338 7.9 270601 8 273547 8 275363 7.9 281229 8 277793 7.7 279913 7.2 282500 7.5 280041 7.3 282166 7 290304 7 283519 7 287816 7.2 285226 7.3 287595

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 wkh[t] = + 29.0258358269593 -7.66040871202718e-05los[t] -0.0102476863359301M1[t] -0.0367149244378123M2[t] -0.542583206261581M3[t] -0.703472642147917M4[t] -0.83090672136853M5[t] -0.083568878298148M6[t] + 0.0373315671599241M7[t] + 0.0617584549197351M8[t] -0.35840149526647M9[t] -0.458542865393233M10[t] -0.483361368749547M11[t] + 0.00841726582995854t + 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) 29.0258358269593 4.492625 6.4608 0 0 los -7.66040871202718e-05 1.7e-05 -4.5049 4.5e-05 2.3e-05 M1 -0.0102476863359301 0.260733 -0.0393 0.968819 0.484409 M2 -0.0367149244378123 0.261875 -0.1402 0.889114 0.444557 M3 -0.542583206261581 0.262041 -2.0706 0.044035 0.022017 M4 -0.703472642147917 0.260294 -2.7026 0.009605 0.004803 M5 -0.83090672136853 0.259756 -3.1988 0.0025 0.00125 M6 -0.083568878298148 0.259079 -0.3226 0.748489 0.374245 M7 0.0373315671599241 0.258963 0.1442 0.886005 0.443003 M8 0.0617584549197351 0.259667 0.2378 0.813063 0.406532 M9 -0.35840149526647 0.26083 -1.3741 0.176075 0.088037 M10 -0.458542865393233 0.260063 -1.7632 0.084509 0.042255 M11 -0.483361368749547 0.266248 -1.8155 0.075976 0.037988 t 0.00841726582995854 0.006004 1.402 0.167622 0.083811 Multiple Linear Regression - Regression Statistics Multiple R 0.759797768866946 R-squared 0.577292649575189 Adjusted R-squared 0.457831876629046 F-TEST (value) 4.83248714484256 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 3.19679927507366e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.408573917739366 Sum Squared Residuals 7.67890172781713 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.2 8.51540599443998 -0.315405994439982 2 8 8.62903844792777 -0.629038447927774 3 7.9 8.36385102408263 -0.463851024082628 4 7.6 8.01603843186956 -0.416038431869558 5 7.6 7.96175207209553 -0.361752072095532 6 8.3 8.29258430973972 0.00741569026027663 7 8.4 8.47667394331875 -0.0766739433187496 8 8.4 8.59002899247193 -0.190028992471925 9 8.4 8.38075091037456 0.0192490896254434 10 8.4 8.2226876666316 0.177312333368403 11 8.6 8.48681059613968 0.113189403860323 12 8.9 8.49851141673644 0.401488583263561 13 8.8 8.45500837283704 0.344991627162961 14 8.3 8.35407277830098 -0.054072778300981 15 7.5 8.16817058462532 -0.668170584625318 16 7.2 7.67864043123974 -0.478640431239744 17 7.4 7.6620432823289 -0.262043282328893 18 8.8 8.31139531421918 0.488604685780825 19 9.3 8.5279650807372 0.772034919262805 20 9.3 8.47156547283185 0.828434527168152 21 8.7 8.37719352141489 0.322806478585111 22 8.2 8.52125679727428 -0.321256797274281 23 8.3 8.3963075682985 -0.0963075682984987 24 8.5 8.58925365902182 -0.0892536590218252 25 8.6 8.64142911993565 -0.0414291199356455 26 8.5 8.33473494739454 0.165265052605463 27 8.2 8.15373541529457 0.0462645847054288 28 8.1 8.01474556457136 0.0852544354286385 29 7.9 7.70429513746715 0.195704862532853 30 8.6 8.29994770428612 0.300052295713881 31 8.7 8.441905089949 0.258094910051005 32 8.7 8.6483341049533 0.0516658950466994 33 8.5 8.19729352390435 0.302706476095646 34 8.4 8.13276387053525 0.267236129464754 35 8.5 8.21832267296597 0.281677327034028 36 8.7 8.33726921553112 0.362730784468884 37 8.7 8.35405358819537 0.34594641180463 38 8.6 8.11546044910418 0.484539550895816 39 8.5 7.78178897137351 0.718211028626485 40 8.3 7.87299440244672 0.427005597553279 41 8 7.77151992500661 0.22848007499339 42 8.2 8.55155852952408 -0.351558529524077 43 8.1 8.4424077176067 -0.342407717606701 44 8.1 8.46958316874957 -0.36958316874957 45 8 8.21625773655805 -0.216257736558046 46 7.9 8.01567922446334 -0.115679224463335 47 7.9 8.20894337338516 -0.308943373385162 48 8 8.47504636730835 -0.475046367308348 49 8 8.33410292459196 -0.334102924591963 50 7.9 7.86669337727252 0.0333066227274757 51 8 7.63245400462397 0.367545995376031 52 7.7 7.31758116987262 0.382418830127385 53 7.2 7.00038958310182 0.199610416898182 54 7.5 7.9445141422309 -0.444514142230906 55 7.3 7.91104816838836 -0.611048168388359 56 7 7.32048826099336 -0.320488260993356 57 7 7.42850430774815 -0.428504307748154 58 7 7.00761244109554 -0.0076124410955417 59 7.2 7.18961578921069 0.0103842107893105 60 7.3 7.49991934140227 -0.199919341402272 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.535953250582728 0.928093498834545 0.464046749417272 18 0.368908566955646 0.737817133911293 0.631091433044354 19 0.292830064071253 0.585660128142507 0.707169935928747 20 0.495473230647017 0.990946461294035 0.504526769352983 21 0.456645332108215 0.913290664216429 0.543354667891786 22 0.874621336623186 0.250757326753629 0.125378663376814 23 0.888920157560451 0.222159684879098 0.111079842439549 24 0.918339677498719 0.163320645002563 0.0816603225012813 25 0.902324611386943 0.195350777226115 0.0976753886130575 26 0.897480126143323 0.205039747713354 0.102519873856677 27 0.979926712359793 0.040146575280413 0.0200732876402065 28 0.999430352367863 0.00113929526427312 0.000569647632136559 29 0.99999917387441 1.65225118084142e-06 8.26125590420712e-07 30 0.999998714872176 2.57025564715519e-06 1.28512782357759e-06 31 0.99999702570357 5.94859286175864e-06 2.97429643087932e-06 32 0.999993723054774 1.25538904512686e-05 6.27694522563431e-06 33 0.999979009538195 4.19809236100496e-05 2.09904618050248e-05 34 0.99994361581892 0.000112768362162255 5.63841810811277e-05 35 0.999845008571163 0.000309982857674576 0.000154991428837288 36 0.999547488348759 0.000905023302481746 0.000452511651240873 37 0.99906068033051 0.00187863933898051 0.000939319669490256 38 0.997805744449884 0.00438851110023102 0.00219425555011551 39 0.994826715486595 0.0103465690268092 0.00517328451340461 40 0.990359610388914 0.0192807792221729 0.00964038961108646 41 0.972078992796215 0.0558420144075693 0.0279210072037847 42 0.947368204699394 0.105263590601212 0.0526317953006059 43 0.897077415142676 0.205845169714648 0.102922584857324 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 11 0.407407407407407 NOK 5% type I error level 14 0.518518518518518 NOK 10% type I error level 15 0.555555555555556 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706956axd0lmntberhas7/10wp9i1258703708.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706956axd0lmntberhas7/10wp9i1258703708.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706956axd0lmntberhas7/1xxlm1258703708.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706956axd0lmntberhas7/1xxlm1258703708.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706956axd0lmntberhas7/2dp0y1258703708.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706956axd0lmntberhas7/2dp0y1258703708.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706956axd0lmntberhas7/38h1p1258703708.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706956axd0lmntberhas7/38h1p1258703708.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706956axd0lmntberhas7/4fexg1258703708.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706956axd0lmntberhas7/4fexg1258703708.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706956axd0lmntberhas7/535521258703708.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706956axd0lmntberhas7/535521258703708.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706956axd0lmntberhas7/6rh3o1258703708.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706956axd0lmntberhas7/6rh3o1258703708.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706956axd0lmntberhas7/7klf41258703708.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706956axd0lmntberhas7/7klf41258703708.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706956axd0lmntberhas7/8oq331258703708.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706956axd0lmntberhas7/8oq331258703708.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706956axd0lmntberhas7/97zkn1258703708.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258706956axd0lmntberhas7/97zkn1258703708.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