shw7: Multiple lineair regression software (5)

*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: Thu, 19 Nov 2009 11:39:22 -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/19/t1258656017ergj5q7ax7miho1.htm/, Retrieved Thu, 19 Nov 2009 19:40:29 +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/19/t1258656017ergj5q7ax7miho1.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:

Multiple lineair regression software (5)

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

0.7905 0.313 0.7744 0.779 0.7719 0.364 0.7905 0.7744 0.7811 0.363 0.7719 0.7905 0.7557 -0.155 0.7811 0.7719 0.7637 0.052 0.7557 0.7811 0.7595 0.568 0.7637 0.7557 0.7471 0.668 0.7595 0.7637 0.7615 1.378 0.7471 0.7595 0.7487 0.252 0.7615 0.7471 0.7389 -0.402 0.7487 0.7615 0.7337 -0.05 0.7389 0.7487 0.751 0.555 0.7337 0.7389 0.7382 0.05 0.751 0.7337 0.7159 0.15 0.7382 0.751 0.7542 0.45 0.7159 0.7382 0.7636 0.299 0.7542 0.7159 0.7433 0.199 0.7636 0.7542 0.7658 0.496 0.7433 0.7636 0.7627 0.444 0.7658 0.7433 0.748 -0.393 0.7627 0.7658 0.7692 -0.444 0.748 0.7627 0.785 0.198 0.7692 0.748 0.7913 0.494 0.785 0.7692 0.772 0.133 0.7913 0.785 0.788 0.388 0.772 0.7913 0.807 0.484 0.788 0.772 0.8268 0.278 0.807 0.788 0.8244 0.369 0.8268 0.807 0.8487 0.165 0.8244 0.8268 0.8572 0.155 0.8487 0.8244 0.8214 0.087 0.8572 0.8487 0.8827 0.414 0.8214 0.8572 0.9216 0.36 0.8827 0.8214 0.8865 0.975 0.9216 0.8827 0.8816 0.27 0.8865 0.9216 0.8884 0.359 0.8816 0.8865 0.9466 0.169 0.8884 0.8816 0.918 0. 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation USDOLLAR[t] = + 0.078110740623702 + 0.0139385911634161Amerikaanse_inflatie[t] + 1.12979723066344`Y[t-1]`[t] -0.264538499966401`Y[t-2]`[t] + 0.0327237110510389M1[t] + 0.0104818715294112M2[t] + 0.0407995808594032M3[t] + 0.0265051646608203M4[t] + 0.0222423674857073M5[t] + 0.0220179199741573M6[t] -0.00472949651591212M7[t] + 0.0249234761846346M8[t] + 0.00314997812565103M9[t] + 0.0173613481476963M10[t] + 0.0278331600231446M11[t] + 0.000283993595009668t + 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) 0.078110740623702 0.049263 1.5856 0.120907 0.060453 Amerikaanse_inflatie 0.0139385911634161 0.013959 0.9985 0.324179 0.162089 `Y[t-1]` 1.12979723066344 0.190769 5.9223 1e-06 0 `Y[t-2]` -0.264538499966401 0.177375 -1.4914 0.1439 0.07195 M1 0.0327237110510389 0.021919 1.493 0.143492 0.071746 M2 0.0104818715294112 0.021916 0.4783 0.635127 0.317563 M3 0.0407995808594032 0.0218 1.8715 0.068788 0.034394 M4 0.0265051646608203 0.022263 1.1905 0.24103 0.120515 M5 0.0222423674857073 0.021876 1.0167 0.315539 0.15777 M6 0.0220179199741573 0.02193 1.004 0.321562 0.160781 M7 -0.00472949651591212 0.022003 -0.215 0.830925 0.415462 M8 0.0249234761846346 0.024081 1.035 0.307048 0.153524 M9 0.00314997812565103 0.02402 0.1311 0.896339 0.448169 M10 0.0173613481476963 0.023899 0.7264 0.471911 0.235956 M11 0.0278331600231446 0.023248 1.1972 0.238445 0.119222 t 0.000283993595009668 0.000298 0.9524 0.346741 0.17337 Multiple Linear Regression - Regression Statistics Multiple R 0.939006435347887 R-squared 0.881733085624745 Adjusted R-squared 0.836245810865032 F-TEST (value) 19.3841704143083 F-TEST (DF numerator) 15 F-TEST (DF denominator) 39 p-value 1.52988732793347e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.0323693408415906 Sum Squared Residuals 0.0408631948342435 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 0.7905 0.784320708255845 0.00617929174415532 2 0.7719 0.782480342992086 -0.0105803429920862 3 0.7811 0.787794808986126 -0.00669480898612576 4 0.7557 0.781878746781381 -0.0261787467813814 5 0.7637 0.749654627713563 0.0140453722864371 6 0.7595 0.7726641425818 -0.0131641425817995 7 0.7471 0.740733122434564 0.00636687756543637 8 0.7615 0.767668064495778 -0.00616806449577773 9 0.7487 0.750033063902934 -0.00133306390293407 10 0.7389 0.737141829947107 0.00175817005289325 11 0.7337 0.745118099446155 -0.0114180994461553 12 0.751 0.722719312372108 0.0282806876278919 13 0.7382 0.769609120770934 -0.0314091207709344 14 0.7159 0.730007213358747 -0.0141072133587470 15 0.7542 0.742982108188549 0.0112178918114513 16 0.7636 0.77603740080296 -0.0124374008029603 17 0.7433 0.771153007526039 -0.0278530075260385 18 0.7658 0.74993076950288 0.0158692304971194 19 0.7627 0.753533109106569 0.00916689089343125 20 0.748 0.762348986934045 -0.0143489869340451 21 0.7692 0.72436066437988 0.0448393356201198 22 0.785 0.77564502076342 0.00935497923658063 23 0.7913 0.802769229263443 -0.0114692292634433 24 0.772 0.773126245679026 -0.00112624567902559 25 0.788 0.786216611970153 0.00178338802984743 26 0.807 0.788779219535189 0.0182207804648110 27 0.8268 0.83374310406367 -0.00694310406367003 28 0.8244 0.838344846923742 -0.0139448469237421 29 0.8487 0.823573195093375 0.025126804906625 30 0.8572 0.851582320370241 0.00561767962975856 31 0.8214 0.827346064187525 -0.00594606418752503 32 0.8827 0.819145631686053 0.0635543683139471 33 0.9216 0.87563049183772 0.0459695081622792 34 0.8865 0.926430991245144 -0.0399309912451441 35 0.8816 0.877413659500414 0.00418634049958623 36 0.8884 0.854854322604393 0.0335456773956071 37 0.9466 0.894192554747739 0.052407445252261 38 0.918 0.939145027172606 -0.0211450271726060 39 0.9337 0.918874328408493 0.0148256715915067 40 0.9559 0.93479513569163 0.0211048643083707 41 0.9626 0.957863617703522 0.00473638229647842 42 0.9434 0.956874148073979 -0.0134741480739792 43 0.8639 0.896603775757152 -0.0327037757571517 44 0.7996 0.842637316884124 -0.0430373168841243 45 0.668 0.757475779879465 -0.089475779879465 46 0.6572 0.62838215804433 0.0288178419556702 47 0.6928 0.674099011789988 0.0187009882100123 48 0.6438 0.704500119344473 -0.0607001193444734 49 0.6454 0.674361004255329 -0.0289610042553293 50 0.6873 0.659688196941372 0.0276118030586283 51 0.7265 0.738905650353162 -0.0124056503531622 52 0.7912 0.759743869800287 0.0314561301997131 53 0.8114 0.827455551963502 -0.0160555519635019 54 0.8281 0.822948619471099 0.0051513805289008 55 0.8393 0.81618392851419 0.0231160714858091 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.126501971539676 0.253003943079351 0.873498028460324 20 0.0849417189256796 0.169883437851359 0.91505828107432 21 0.0540909131146054 0.108181826229211 0.945909086885395 22 0.0220417854745803 0.0440835709491606 0.97795821452542 23 0.0092702140958118 0.0185404281916236 0.990729785904188 24 0.00669211979408875 0.0133842395881775 0.993307880205911 25 0.00275813405331584 0.00551626810663169 0.997241865946684 26 0.00198498362302671 0.00396996724605342 0.998015016376973 27 0.000688113100343025 0.00137622620068605 0.999311886899657 28 0.000375555719633507 0.000751111439267015 0.999624444280367 29 0.000196507053347839 0.000393014106695677 0.999803492946652 30 8.023256810656e-05 0.00016046513621312 0.999919767431893 31 0.000298580571521405 0.00059716114304281 0.999701419428479 32 0.000432307511843042 0.000864615023686085 0.999567692488157 33 0.00472423048433067 0.00944846096866135 0.99527576951567 34 0.0103989454442546 0.0207978908885092 0.989601054555745 35 0.00423961857140106 0.00847923714280212 0.9957603814286 36 0.0111835978162174 0.0223671956324348 0.988816402183783 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 10 0.555555555555556 NOK 5% type I error level 15 0.833333333333333 NOK 10% type I error level 15 0.833333333333333 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656017ergj5q7ax7miho1/10rwgd1258655957.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656017ergj5q7ax7miho1/10rwgd1258655957.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656017ergj5q7ax7miho1/19mh81258655957.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656017ergj5q7ax7miho1/19mh81258655957.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656017ergj5q7ax7miho1/24hmm1258655957.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656017ergj5q7ax7miho1/24hmm1258655957.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656017ergj5q7ax7miho1/3tbyw1258655957.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656017ergj5q7ax7miho1/3tbyw1258655957.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656017ergj5q7ax7miho1/4ebcx1258655957.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656017ergj5q7ax7miho1/4ebcx1258655957.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656017ergj5q7ax7miho1/503de1258655957.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656017ergj5q7ax7miho1/503de1258655957.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656017ergj5q7ax7miho1/6o9j11258655957.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656017ergj5q7ax7miho1/6o9j11258655957.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656017ergj5q7ax7miho1/7f8hu1258655957.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656017ergj5q7ax7miho1/7f8hu1258655957.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656017ergj5q7ax7miho1/8spz91258655957.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656017ergj5q7ax7miho1/8spz91258655957.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656017ergj5q7ax7miho1/9hw7w1258655957.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656017ergj5q7ax7miho1/9hw7w1258655957.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