*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 00:41:25 -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/t1258702963azcaalt7gwjjguj.htm/, Retrieved Fri, 20 Nov 2009 08:42:55 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Nov/20/t1258702963azcaalt7gwjjguj.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 «

100.03 2 100.25 1.8 99.6 2.7 100.16 2.3 100.49 1.9 99.72 2 100.14 2.3 98.48 2.8 100.38 2.4 101.45 2.3 98.42 2.7 98.6 2.7 100.06 2.9 98.62 3 100.84 2.2 100.02 2.3 97.95 2.8 98.32 2.8 98.27 2.8 97.22 2.2 99.28 2.6 100.38 2.8 99.02 2.5 100.32 2.4 99.81 2.3 100.6 1.9 101.19 1.7 100.47 2 101.77 2.1 102.32 1.7 102.39 1.8 101.16 1.8 100.63 1.8 101.48 1.3 101.44 1.3 100.09 1.3 100.7 1.2 100.78 1.4 99.81 2.2 98.45 2.9 98.49 3.1 97.48 3.5 97.91 3.6 96.94 4.4 98.53 4.1 96.82 5.1 95.76 5.8 95.27 5.9 97.32 5.4 96.68 5.5 97.87 4.8 97.42 3.2 97.94 2.7 99.52 2.1 100.99 1.9 99.92 0.6 101.97 0.7 101.58 -0.2 99.54 -1 100.83 -1.7

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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 100.937861512697 -0.903708260706107X[t] + 1.14037328685191M1[t] + 0.906224956423668M2[t] + 1.38222495642367M3[t] + 0.661557469496567M4[t] + 0.667483304282443M5[t] + 0.721112478211832M6[t] + 1.2433349738542M7[t] -0.0611100174305318M8[t] + 1.31674165214122M9[t] + 1.44651915649886M10[t] -0.0594808435011405M11[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) 100.937861512697 0.503342 200.5355 0 0 X -0.903708260706107 0.09529 -9.4837 0 0 M1 1.14037328685191 0.654832 1.7415 0.088143 0.044071 M2 0.906224956423668 0.654488 1.3846 0.172703 0.086352 M3 1.38222495642367 0.654488 2.1119 0.040037 0.020018 M4 0.661557469496567 0.653213 1.0128 0.316353 0.158177 M5 0.667483304282443 0.653099 1.022 0.312001 0.156001 M6 0.721112478211832 0.652612 1.105 0.2748 0.1374 M7 1.2433349738542 0.652887 1.9044 0.062994 0.031497 M8 -0.0611100174305318 0.652387 -0.0937 0.925768 0.462884 M9 1.31674165214122 0.652264 2.0187 0.049242 0.024621 M10 1.44651915649886 0.652122 2.2182 0.031412 0.015706 M11 -0.0594808435011405 0.652122 -0.0912 0.927712 0.463856 Multiple Linear Regression - Regression Statistics Multiple R 0.828776724306222 R-squared 0.68687085875175 Adjusted R-squared 0.606922992901134 F-TEST (value) 8.5914846061704 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 2.56587797675678e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.03087971742847 Sum Squared Residuals 49.9475106148534 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 100.03 100.270818278137 -0.24081827813664 2 100.25 100.217411599850 0.0325884001503753 3 99.6 99.880074165214 -0.280074165214128 4 100.16 99.5208899825695 0.639110017430531 5 100.49 99.8882991216378 0.60170087836221 6 99.72 99.8515574694966 -0.131557469496564 7 100.14 100.102667486927 0.0373325130729031 8 98.48 98.3463683652893 0.133631634710691 9 100.38 100.085703339144 0.294296660856486 10 101.45 100.305851669572 1.14414833042825 11 98.42 98.4383683652893 -0.0183683652893126 12 98.6 98.4978492087905 0.102150791209539 13 100.06 99.4574808435011 0.602519156498856 14 98.62 99.1329616870023 -0.512961687002286 15 100.84 100.331928295567 0.508071704432827 16 100.02 99.5208899825695 0.499110017430531 17 97.95 99.0749616870023 -1.12496168700228 18 98.32 99.1285908609317 -0.808590860931683 19 98.27 99.650813356574 -1.38081335657405 20 97.22 98.888593321713 -1.66859332171298 21 99.28 99.9049616870023 -0.624961687002287 22 100.38 99.8539975392187 0.526002460781295 23 99.02 98.6191100174305 0.40088998256946 24 100.32 98.7689616870023 1.55103831299771 25 99.81 99.9997057999248 -0.189705799924808 26 100.6 100.127040773779 0.472959226220986 27 101.19 100.783782425920 0.406217574079767 28 100.47 99.7920024607813 0.677997539218702 29 101.77 99.7075574694966 2.06244253050343 30 102.32 100.122669947708 2.1973300522916 31 102.39 100.554521617280 1.83547838271985 32 101.16 99.2500766259954 1.90992337400458 33 100.63 100.627928295567 0.00207170443282080 34 101.48 101.209559930278 0.270440069722142 35 101.44 99.7035599302779 1.73644006972213 36 100.09 99.763040773779 0.326959226220998 37 100.7 100.993784886702 -0.293784886701526 38 100.78 100.578894904132 0.201105095867939 39 99.81 100.331928295567 -0.521928295567174 40 98.45 98.9786650261458 -0.528665026145798 41 98.49 98.8038492087905 -0.31384920879046 42 97.48 98.4959950784374 -1.01599507843740 43 97.91 98.9278467480092 -1.01784674800916 44 96.94 96.9004351481595 0.0395648518404578 45 98.53 98.5493992959431 -0.0193992959431265 46 96.82 97.7754685395947 -0.95546853959466 47 95.76 95.6368727571004 0.123127242899625 48 95.27 95.605982774531 -0.335982774530914 49 97.32 97.1982101917359 0.121789808264117 50 96.68 96.873691035237 -0.193691035237014 51 97.87 97.9822868177313 -0.112286817731292 52 97.42 98.707552547934 -1.28755254793397 53 97.94 99.1653325130729 -1.2253325130729 54 99.52 99.761186643426 -0.241186643425955 55 100.99 100.464150791210 0.525849208790454 56 99.92 100.334526538843 -0.414526538842747 57 101.97 101.622007382344 0.347992617656106 58 101.58 102.565122321337 -0.985122321337025 59 99.54 101.782088929902 -2.24208892990191 60 100.83 102.474165555897 -1.64416555589733 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.093377256290533 0.186754512581066 0.906622743709467 17 0.128343192461834 0.256686384923668 0.871656807538166 18 0.0609806336679589 0.121961267335918 0.93901936633204 19 0.05667594053722 0.11335188107444 0.94332405946278 20 0.167555224161334 0.335110448322668 0.832444775838666 21 0.119938374781386 0.239876749562772 0.880061625218614 22 0.0757757315049425 0.151551463009885 0.924224268495057 23 0.0432512796556601 0.0865025593113203 0.95674872034434 24 0.0527618225758892 0.105523645151778 0.94723817742411 25 0.0312898219827941 0.0625796439655881 0.968710178017206 26 0.0178183108396721 0.0356366216793442 0.982181689160328 27 0.00945218629562 0.01890437259124 0.99054781370438 28 0.00591539844874431 0.0118307968974886 0.994084601551256 29 0.0471966389344575 0.094393277868915 0.952803361065543 30 0.181854678661941 0.363709357323881 0.81814532133806 31 0.301158267732361 0.602316535464722 0.698841732267639 32 0.473421265777375 0.94684253155475 0.526578734222624 33 0.39652107770793 0.79304215541586 0.60347892229207 34 0.493648509761575 0.98729701952315 0.506351490238425 35 0.831659113601575 0.33668177279685 0.168340886398425 36 0.899017117404902 0.201965765190196 0.100982882595098 37 0.861632025541726 0.276735948916549 0.138367974458274 38 0.830357879625222 0.339284240749556 0.169642120374778 39 0.751528170692735 0.49694365861453 0.248471829307265 40 0.691369985331837 0.617260029336326 0.308630014668163 41 0.620353882613647 0.759292234772705 0.379646117386353 42 0.559848203396805 0.88030359320639 0.440151796603195 43 0.732549255768553 0.534901488462894 0.267450744231447 44 0.591623034770257 0.816753930459486 0.408376965229743 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 3 0.103448275862069 NOK 10% type I error level 6 0.206896551724138 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258702963azcaalt7gwjjguj/10rqku1258702879.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258702963azcaalt7gwjjguj/10rqku1258702879.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258702963azcaalt7gwjjguj/19tbj1258702879.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258702963azcaalt7gwjjguj/19tbj1258702879.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258702963azcaalt7gwjjguj/2e3am1258702879.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258702963azcaalt7gwjjguj/2e3am1258702879.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258702963azcaalt7gwjjguj/3e37h1258702879.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258702963azcaalt7gwjjguj/3e37h1258702879.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258702963azcaalt7gwjjguj/4oox71258702879.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258702963azcaalt7gwjjguj/4oox71258702879.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258702963azcaalt7gwjjguj/5f3jk1258702879.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258702963azcaalt7gwjjguj/5f3jk1258702879.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258702963azcaalt7gwjjguj/6xg061258702879.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258702963azcaalt7gwjjguj/6xg061258702879.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258702963azcaalt7gwjjguj/70ovh1258702879.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258702963azcaalt7gwjjguj/70ovh1258702879.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258702963azcaalt7gwjjguj/8yerv1258702879.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258702963azcaalt7gwjjguj/8yerv1258702879.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258702963azcaalt7gwjjguj/9hpn31258702879.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258702963azcaalt7gwjjguj/9hpn31258702879.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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