*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: Mon, 23 Nov 2009 05:47:54 -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/23/t12589805276kzp4d7aqkdo758.htm/, Retrieved Mon, 23 Nov 2009 13:49:00 +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/23/t12589805276kzp4d7aqkdo758.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 «

5560 543 3922 594 3759 611 4138 613 4634 611 3996 594 4308 595 4143 591 4429 589 5219 584 4929 573 5755 567 5592 569 4163 621 4962 629 5208 628 4755 612 4491 595 5732 597 5731 593 5040 590 6102 580 4904 574 5369 573 5578 573 4619 620 4731 626 5011 620 5299 588 4146 566 4625 557 4736 561 4219 549 5116 532 4205 526 4121 511 5103 499 4300 555 4578 565 3809 542 5526 527 4247 510 3830 514 4394 517 4826 508 4409 493 4569 490 4106 469 4794 478 3914 528 3793 534 4405 518 4022 506 4100 502 4788 516 3163 528 3585 533 3903 536 4178 537 3863 524 4187 536

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 Y[t] = + 2311.02615457282 + 4.07602600150082X[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) 2311.02615457282 1048.194859 2.2048 0.031382 0.015691 X 4.07602600150082 1.874176 2.1748 0.033663 0.016832 Multiple Linear Regression - Regression Statistics Multiple R 0.272429968325699 R-squared 0.0742180876419413 Adjusted R-squared 0.0585268687884148 F-TEST (value) 4.72991220980017 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 0.0336631195452499 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 614.560300724891 Sum Squared Residuals 22283377.4303971 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 5560 4524.30827338777 1035.69172661223 2 3922 4732.18559946431 -810.185599464307 3 3759 4801.47804148982 -1042.47804148982 4 4138 4809.63009349282 -671.630093492824 5 4634 4801.47804148982 -167.478041489822 6 3996 4732.18559946431 -736.185599464308 7 4308 4736.26162546581 -428.261625465809 8 4143 4719.9575214598 -576.957521459806 9 4429 4711.80546945680 -282.805469456804 10 5219 4691.4253394493 527.5746605507 11 4929 4646.58905343279 282.410946567209 12 5755 4622.13289742379 1132.86710257621 13 5592 4630.28494942679 961.715050573212 14 4163 4842.23830150483 -679.23830150483 15 4962 4874.84650951684 87.1534904831631 16 5208 4870.77048351534 337.229516484664 17 4755 4805.55406749132 -50.554067491323 18 4491 4736.26162546581 -245.261625465809 19 5732 4744.41367746881 987.58632253119 20 5731 4728.10957346281 1002.89042653719 21 5040 4715.88149545830 324.118504541695 22 6102 4675.1212354433 1426.87876455670 23 4904 4650.66507943429 253.334920565708 24 5369 4646.58905343279 722.410946567209 25 5578 4646.58905343279 931.410946567209 26 4619 4838.16227550333 -219.162275503330 27 4731 4862.61843151233 -131.618431512334 28 5011 4838.16227550333 172.837724496670 29 5299 4707.7294434553 591.270556544697 30 4146 4618.05687142229 -472.056871422285 31 4625 4581.37263740878 43.627362591222 32 4736 4597.67674141478 138.323258585219 33 4219 4548.76442939677 -329.764429396771 34 5116 4479.47198737126 636.528012628742 35 4205 4455.01583136225 -250.015831362253 36 4121 4393.87544133974 -272.875441339740 37 5103 4344.96312932173 758.03687067827 38 4300 4573.22058540578 -273.220585405776 39 4578 4613.98084542078 -35.9808454207845 40 3809 4520.23224738627 -711.232247386266 41 5526 4459.09185736375 1066.90814263625 42 4247 4389.79941533824 -142.799415338239 43 3830 4406.10351934424 -576.103519344243 44 4394 4418.33159734875 -24.3315973487452 45 4826 4381.64736333524 444.352636664762 46 4409 4320.50697331273 88.4930266872744 47 4569 4308.27889530822 260.721104691777 48 4106 4222.68234927671 -116.682349276706 49 4794 4259.36658329021 534.633416709787 50 3914 4463.16788336525 -549.167883365254 51 3793 4487.62403937426 -694.624039374259 52 4405 4422.40762335025 -17.4076233502460 53 4022 4373.49531133224 -351.495311332236 54 4100 4357.19120732623 -257.191207326233 55 4788 4414.25557134724 373.744428652756 56 3163 4463.16788336525 -1300.16788336525 57 3585 4483.54801337276 -898.548013372758 58 3903 4495.77609137726 -592.776091377261 59 4178 4499.85211737876 -321.852117378762 60 3863 4446.86377935925 -583.863779359251 61 4187 4495.77609137726 -308.776091377261 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.417261129172957 0.834522258345914 0.582738870827043 6 0.34094065540566 0.68188131081132 0.65905934459434 7 0.214370086359202 0.428740172718404 0.785629913640798 8 0.149512605242996 0.299025210485993 0.850487394757004 9 0.085642882881663 0.171285765763326 0.914357117118337 10 0.138029510438516 0.276059020877032 0.861970489561484 11 0.0844171487387433 0.168834297477487 0.915582851261257 12 0.152217479584981 0.304434959169963 0.847782520415019 13 0.162706476923038 0.325412953846076 0.837293523076962 14 0.1590622110275 0.318124422055 0.8409377889725 15 0.399329678157393 0.798659356314786 0.600670321842607 16 0.571419661786761 0.857160676426478 0.428580338213239 17 0.50782507445352 0.98434985109296 0.49217492554648 18 0.442173689691257 0.884347379382515 0.557826310308743 19 0.596431185316481 0.807137629367038 0.403568814683519 20 0.703225827547437 0.593548344905127 0.296774172452563 21 0.639604847898922 0.720790304202157 0.360395152101078 22 0.843140378421918 0.313719243156163 0.156859621578082 23 0.802985932876242 0.394028134247517 0.197014067123758 24 0.79946592097425 0.401068158051501 0.200534079025750 25 0.846717290557006 0.306565418885989 0.153282709442994 26 0.799104609691766 0.401790780616468 0.200895390308234 27 0.752844920144989 0.494310159710023 0.247155079855011 28 0.737207765728362 0.525584468543276 0.262792234271638 29 0.793069279038054 0.413861441923891 0.206930720961946 30 0.832158596975072 0.335682806049855 0.167841403024928 31 0.824321056235296 0.351357887529408 0.175678943764704 32 0.820133549734382 0.359732900531235 0.179866450265618 33 0.826135724695487 0.347728550609025 0.173864275304513 34 0.862304425705604 0.275391148588791 0.137695574294396 35 0.860298899729919 0.279402200540163 0.139701100270081 36 0.853080709410868 0.293838581178263 0.146919290589132 37 0.868244634418352 0.263510731163296 0.131755365581648 38 0.84209899714346 0.315802005713081 0.157901002856541 39 0.852436326913579 0.295127346172842 0.147563673086421 40 0.847528976495483 0.304942047009035 0.152471023504518 41 0.987153409994808 0.0256931800103839 0.0128465900051919 42 0.980134579071409 0.0397308418571822 0.0198654209285911 43 0.978509503449069 0.0429809931018629 0.0214904965509315 44 0.968574282814464 0.0628514343710726 0.0314257171855363 45 0.975806640990386 0.0483867180192289 0.0241933590096144 46 0.95916247293259 0.0816750541348217 0.0408375270674108 47 0.938782677473376 0.122434645053248 0.0612173225266239 48 0.941905041442583 0.116189917114835 0.0580949585574173 49 0.909647055574288 0.180705888851423 0.0903529444257117 50 0.866761024368822 0.266477951262357 0.133238975631178 51 0.814427893677233 0.371144212645534 0.185572106322767 52 0.761472219999035 0.47705556000193 0.238527780000965 53 0.664948138798079 0.670103722403842 0.335051861201921 54 0.561635091969288 0.876729816061424 0.438364908030712 55 0.82401093552117 0.351978128957659 0.175989064478829 56 0.908357151814315 0.183285696371371 0.0916428481856853 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 4 0.0769230769230769 NOK 10% type I error level 6 0.115384615384615 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/23/t12589805276kzp4d7aqkdo758/10gvzj1258980468.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589805276kzp4d7aqkdo758/10gvzj1258980468.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589805276kzp4d7aqkdo758/1vyyk1258980468.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589805276kzp4d7aqkdo758/1vyyk1258980468.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589805276kzp4d7aqkdo758/2it2t1258980468.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589805276kzp4d7aqkdo758/2it2t1258980468.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589805276kzp4d7aqkdo758/32qgc1258980468.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589805276kzp4d7aqkdo758/32qgc1258980468.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589805276kzp4d7aqkdo758/4au3e1258980468.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589805276kzp4d7aqkdo758/4au3e1258980468.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589805276kzp4d7aqkdo758/55hpe1258980468.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589805276kzp4d7aqkdo758/55hpe1258980468.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589805276kzp4d7aqkdo758/6t8tk1258980468.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589805276kzp4d7aqkdo758/6t8tk1258980468.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589805276kzp4d7aqkdo758/7uz3l1258980468.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589805276kzp4d7aqkdo758/7uz3l1258980468.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589805276kzp4d7aqkdo758/8822k1258980468.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589805276kzp4d7aqkdo758/8822k1258980468.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589805276kzp4d7aqkdo758/96z8d1258980468.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589805276kzp4d7aqkdo758/96z8d1258980468.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