Paper TSA Multiple Regression Model 4

*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: Sat, 18 Dec 2010 17:15:14 +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/18/t12926928896ltif501wuq1w0b.htm/, Retrieved Sat, 18 Dec 2010 18:21:32 +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/18/t12926928896ltif501wuq1w0b.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 «

80900 0 35600 36700 174000 0 80900 35600 169422 0 174000 80900 153452 0 169422 174000 173570 0 153452 169422 193036 0 173570 153452 174652 0 193036 173570 105367 0 174652 193036 95963 0 105367 174652 82896 0 95963 105367 121747 0 82896 95963 120196 0 121747 82896 103983 0 120196 121747 81103 0 103983 120196 70944 0 81103 103983 57248 0 70944 81103 47830 0 57248 70944 60095 0 47830 57248 60931 0 60095 47830 82955 0 60931 60095 99559 0 82955 60931 77911 0 99559 82955 70753 0 77911 99559 69287 0 70753 77911 88426 0 69287 70753 91756 1 88426 69287 96933 1 91756 88426 174484 1 96933 91756 232595 1 174484 96933 266197 1 232595 174484 290435 1 266197 232595 304296 1 290435 266197 322310 1 304296 290435 415555 1 322310 304296 490042 1 415555 322310 545109 1 490042 415555 545720 1 545109 490042 505944 1 545720 545109 477930 1 505944 545720 466106 1 477930 505944 424476 1 466106 477930 383018 1 424476 466106 364696 1 383018 424476 391116 1 364696 383018 435721 1 391116 364696 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 5 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation Werklozen[t] = + 7083.61427550935 + 27769.2857221576Oliecrisis[t] + 1.4167263043587Y1[t] -0.534958190946436Y2[t] + 496.549472685872t + 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) 7083.61427550935 8192.613703 0.8646 0.391211 0.195605 Oliecrisis 27769.2857221576 15843.559631 1.7527 0.085545 0.042772 Y1 1.4167263043587 0.117032 12.1054 0 0 Y2 -0.534958190946436 0.113884 -4.6974 2e-05 1e-05 t 496.549472685872 543.794121 0.9131 0.365394 0.182697 Multiple Linear Regression - Regression Statistics Multiple R 0.98987994432335 R-squared 0.979862304173597 Adjusted R-squared 0.978313250648489 F-TEST (value) 632.555485198821 F-TEST (DF numerator) 4 F-TEST (DF denominator) 52 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 27974.9745735618 Sum Squared Residuals 40695158524.3543 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 80900 38382.6545756303 42517.3454243697 2 174000 103645.359645806 70354.6403541936 3 169422 211805.522004414 -42383.5220044136 4 153452 156011.690878632 -2559.69087863203 5 173570 136332.159868862 37237.8401311377 6 193036 173873.691442051 19162.3085579491 7 174652 191185.946269923 -16533.9462699228 8 105367 155223.903218315 -49856.9032183151 9 95963 67397.242075868 28565.757924132 10 82896 91635.4756420885 -8739.47564208845 11 121747 78650.4093233795 43096.5906766205 12 120196 141178.491127802 -20982.4911278022 13 103983 118694.037425968 -14711.0374259677 14 81103 97050.923480244 -15947.923480244 15 70944 73806.0522590175 -2862.05225901745 16 57248 72149.9226145778 -14901.9226145778 17 47830 58677.6288845918 -10847.6288845918 18 60095 53158.2374060298 6936.76259397016 19 60931 76069.1712440086 -15138.1712440086 20 82955 71188.8416951804 11766.1583048196 21 99559 102440.146247431 -2881.14624743093 22 77911 114678.100080284 -36767.1000802843 23 70753 75622.9127137385 -4869.9127137385 24 69287 77559.3102174333 -8272.31021743326 25 88426 79808.1696587239 8617.83034127613 26 91756 135972.978300616 -44216.9783006159 27 96933 130948.661550292 -34015.6615502924 28 174484 136998.192324792 37485.8076752084 29 232595 244593.804872269 -11998.8048722691 30 266197 285931.193951456 -19734.1939514561 31 290435 302945.625269115 -12510.6252691146 32 304296 319805.121774664 -15509.1217746644 33 322310 326972.597919906 -4662.59791990642 34 415555 345574.999554601 69980.0004453987 35 490042 468537.456425505 21504.5435744953 36 545109 524679.521616156 20429.4783838436 37 545720 563343.507721936 -17623.5077219355 38 505944 535247.134265737 -29303.1342657372 39 477930 479065.118801583 -1135.11880158323 40 466106 461151.99458705 4954.00541294999 41 424476 459883.490998172 -35407.4909981721 42 383018 407727.070070156 -24709.0700701561 43 364696 371759.289905839 -7063.28990583926 44 391116 368476.876710322 22639.1232896775 45 435721 416204.839118686 19516.1608813144 46 511435 465760.869992486 45674.1300075137 47 553997 549661.624766221 4335.37523377918 48 555252 569953.054735703 -14701.0547357031 49 544897 549458.705197297 -4561.70519729692 50 540562 534613.681258711 5948.31874128929 51 505282 534508.214269252 -29226.214269252 52 507626 487341.703481916 20284.2965180842 53 474427 510032.384388609 -35605.3843886088 54 469740 462241.095283312 7498.90471668822 55 491480 473857.525548699 17622.4744513008 56 538974 507661.053919109 31312.946080891 57 576612 563813.611419831 12798.3885801686 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.967434744052645 0.0651305118947108 0.0325652559473554 9 0.944716954249692 0.110566091500615 0.0552830457503077 10 0.902835789201305 0.194328421597389 0.0971642107986946 11 0.940339767679382 0.119320464641236 0.059660232320618 12 0.901793301277447 0.196413397445107 0.0982066987225533 13 0.846833094253594 0.306333811492813 0.153166905746406 14 0.783974329212371 0.432051341575258 0.216025670787629 15 0.70721587986908 0.58556824026184 0.29278412013092 16 0.624741344727038 0.750517310545923 0.375258655272962 17 0.531702664624254 0.93659467075149 0.468297335375746 18 0.469373220514758 0.938746441029516 0.530626779485242 19 0.380838642421468 0.761677284842936 0.619161357578532 20 0.396456733048555 0.79291346609711 0.603543266951445 21 0.371997118934601 0.743994237869203 0.628002881065398 22 0.328490266421923 0.656980532843846 0.671509733578077 23 0.274293169788899 0.548586339577799 0.7257068302111 24 0.222181152098503 0.444362304197007 0.777818847901497 25 0.218514837573294 0.437029675146588 0.781485162426706 26 0.226075100262203 0.452150200524406 0.773924899737797 27 0.244119967042079 0.488239934084158 0.755880032957921 28 0.523651113170127 0.952697773659745 0.476348886829873 29 0.581747452133156 0.836505095733689 0.418252547866844 30 0.655575879700743 0.688848240598514 0.344424120299257 31 0.703025724575458 0.593948550849083 0.296974275424542 32 0.77257276420426 0.454854471591481 0.22742723579574 33 0.831958322872523 0.336083354254954 0.168041677127477 34 0.97092508323519 0.058149833529621 0.0290749167648105 35 0.96073327906353 0.0785334418729417 0.0392667209364709 36 0.95232411653756 0.0953517669248803 0.0476758834624402 37 0.93090423124333 0.138191537513338 0.069095768756669 38 0.907537486815976 0.184925026368048 0.092462513184024 39 0.902247423366494 0.195505153267013 0.0977525766335064 40 0.916994477754744 0.166011044490512 0.0830055222452562 41 0.898717531550639 0.202564936898722 0.101282468449361 42 0.86975484517215 0.2604903096557 0.13024515482785 43 0.83980804953527 0.320383900929461 0.16019195046473 44 0.78144444859969 0.437111102800621 0.21855555140031 45 0.753285540732256 0.493428918535488 0.246714459267744 46 0.727729750252766 0.544540499494468 0.272270249747234 47 0.61771412913857 0.76457174172286 0.38228587086143 48 0.481885241540889 0.963770483081778 0.518114758459111 49 0.377650007925931 0.755300015851862 0.622349992074069 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 4 0.0952380952380952 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/18/t12926928896ltif501wuq1w0b/10evc11292692506.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926928896ltif501wuq1w0b/10evc11292692506.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926928896ltif501wuq1w0b/17ufq1292692506.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926928896ltif501wuq1w0b/17ufq1292692506.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926928896ltif501wuq1w0b/27ufq1292692506.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926928896ltif501wuq1w0b/27ufq1292692506.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926928896ltif501wuq1w0b/3imea1292692506.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926928896ltif501wuq1w0b/3imea1292692506.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926928896ltif501wuq1w0b/4imea1292692506.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926928896ltif501wuq1w0b/4imea1292692506.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926928896ltif501wuq1w0b/5imea1292692506.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926928896ltif501wuq1w0b/5imea1292692506.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926928896ltif501wuq1w0b/6tvvd1292692506.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926928896ltif501wuq1w0b/6tvvd1292692506.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926928896ltif501wuq1w0b/73mcy1292692506.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926928896ltif501wuq1w0b/73mcy1292692506.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926928896ltif501wuq1w0b/83mcy1292692506.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926928896ltif501wuq1w0b/83mcy1292692506.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926928896ltif501wuq1w0b/93mcy1292692506.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926928896ltif501wuq1w0b/93mcy1292692506.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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