*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 11:44:20 -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/t1258656332ir6oys4xx5rlvos.htm/, Retrieved Thu, 19 Nov 2009 19:45:44 +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/t1258656332ir6oys4xx5rlvos.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 «

2.06 0 2.08 2.05 2.09 2.11 2.06 0 2.06 2.08 2.05 2.09 2.08 0 2.06 2.06 2.08 2.05 2.07 0 2.08 2.06 2.06 2.08 2.06 0 2.07 2.08 2.06 2.06 2.07 0 2.06 2.07 2.08 2.06 2.06 0 2.07 2.06 2.07 2.08 2.09 0 2.06 2.07 2.06 2.07 2.07 0 2.09 2.06 2.07 2.06 2.09 0 2.07 2.09 2.06 2.07 2.28 0 2.09 2.07 2.09 2.06 2.33 0 2.28 2.09 2.07 2.09 2.35 0 2.33 2.28 2.09 2.07 2.52 0 2.35 2.33 2.28 2.09 2.63 0 2.52 2.35 2.33 2.28 2.58 0 2.63 2.52 2.35 2.33 2.70 0 2.58 2.63 2.52 2.35 2.81 0 2.70 2.58 2.63 2.52 2.97 0 2.81 2.70 2.58 2.63 3.04 0 2.97 2.81 2.70 2.58 3.28 0 3.04 2.97 2.81 2.70 3.33 0 3.28 3.04 2.97 2.81 3.50 0 3.33 3.28 3.04 2.97 3.56 0 3.50 3.33 3.28 3.04 3.57 0 3.56 3.50 3.33 3.28 3.69 0 3.57 3.56 3.50 3.33 3.82 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 Y[t] = + 0.29971047287334 -0.663822504572186X[t] + 1.08044916584715Y1[t] -0.0879835056614519Y2[t] -0.188245761093748Y3[t] + 0.0560742837520075Y4[t] + 0.00722853285851178t + 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) 0.29971047287334 0.072599 4.1283 0.000142 7.1e-05 X -0.663822504572186 0.155341 -4.2733 8.8e-05 4.4e-05 Y1 1.08044916584715 0.176589 6.1184 0 0 Y2 -0.0879835056614519 0.238677 -0.3686 0.713991 0.356996 Y3 -0.188245761093748 0.242498 -0.7763 0.441316 0.220658 Y4 0.0560742837520075 0.135941 0.4125 0.681779 0.34089 t 0.00722853285851178 0.002356 3.0688 0.003498 0.001749 Multiple Linear Regression - Regression Statistics Multiple R 0.995432595103778 R-squared 0.990886051395043 Adjusted R-squared 0.989770057688313 F-TEST (value) 887.895734017145 F-TEST (DF numerator) 6 F-TEST (DF denominator) 49 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.117666128724429 Sum Squared Residuals 0.678420574600706 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2.06 2.09879018211876 -0.0387901821187631 2 2.06 2.08817857125920 -0.028178571259196 3 2.08 2.08927643004804 -0.00927643004804446 4 2.07 2.12356108995793 -0.0535610899579341 5 2.06 2.11710397536970 -0.0571039753697043 6 2.07 2.11064293640448 -0.0406429364044849 7 2.06 2.13255973926406 -0.0725597392640597 8 2.09 2.12942566018090 -0.0394256601809034 9 2.07 2.16750430262299 -0.0975043026229865 10 2.09 2.15292754744317 -0.0629275474431691 11 2.28 2.17731661806152 0.102683381938479 12 2.33 2.39351796605220 -0.0635179660521976 13 2.35 2.43316569023048 -0.0831656902304765 14 2.52 2.42295882219009 0.0970411778099131 15 2.63 2.61334586898758 0.0166541310124201 16 2.58 2.72350541309256 -0.143505413092557 17 2.7 2.63615300832505 0.0638469916749454 18 2.81 2.76626021088583 0.0437397891141733 19 2.97 2.89736059057556 0.0726394094244408 20 3.04 3.04238959882801 -0.00238959882800619 21 3.28 3.09719409271991 0.182805907280085 22 3.33 3.33362042942316 -0.00362042942316313 23 3.5 3.36955006133904 0.130449938660957 24 3.56 3.51480199430864 0.0451980056913601 25 3.57 3.57594582120133 -0.00594582120132879 26 3.69 3.55950177018029 0.130498229819712 27 3.82 3.69374225045606 0.126257749543940 28 3.79 3.83235315360951 -0.0423531536095107 29 3.96 3.77370160726289 0.186298392737110 30 4.06 3.94950296859332 0.110497031406685 31 4.05 4.06275625179467 -0.0127562517946685 32 4.03 4.01669793453007 0.0133020654699339 33 3.94 3.99390537125672 -0.0539053712567166 34 4.02 3.91314303528835 0.106856964711648 35 3.88 4.01793018930852 -0.137930189308521 36 4.02 3.88267779131891 0.137322208681087 37 4.03 4.03338055176345 -0.00338055176344775 38 4.09 4.06993623474111 0.0200637652588859 39 3.99 4.10690707621543 -0.116907076215434 40 4.01 4.00677962426389 0.00322037573611233 41 4.01 4.03368148817738 -0.0236814881773826 42 4.19 4.06133938405716 0.12866061594284 43 4.3 4.25367642317109 0.0463235768289148 44 4.27 4.36503881892876 -0.0950388189287623 45 3.82 4.29629145419222 -0.476291454192224 46 3.15 3.14552120037222 0.00447879962777656 47 2.49 2.48025691370633 0.00974308629367192 48 1.81 1.91636630987852 -0.106366309878517 49 1.26 1.34784975594193 -0.0878497559419297 50 1.06 0.907332463642322 0.152667536357678 51 0.84 0.837860181712625 0.00213981828737486 52 0.78 0.690391254867249 0.089608745132751 53 0.7 0.658957505175597 0.0410424948244032 54 0.36 0.615228325796246 -0.255228325796246 55 0.35 0.261101225959824 0.0888987740401757 56 0.36 0.299134862947138 0.0608651370528618 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.00259481648899120 0.00518963297798239 0.99740518351101 11 0.0867610024730611 0.173522004946122 0.913238997526939 12 0.0399106217978815 0.079821243595763 0.960089378202118 13 0.0298578833609049 0.0597157667218098 0.970142116639095 14 0.0137399987231031 0.0274799974462061 0.986260001276897 15 0.00550503648949869 0.0110100729789974 0.994494963510501 16 0.00531061806158874 0.0106212361231775 0.994689381938411 17 0.00221413424692345 0.0044282684938469 0.997785865753076 18 0.000894917185395584 0.00178983437079117 0.999105082814604 19 0.00532102022337969 0.0106420404467594 0.99467897977662 20 0.00323652729014484 0.00647305458028969 0.996763472709855 21 0.00485079229163622 0.00970158458327244 0.995149207708364 22 0.00449469133516393 0.00898938267032787 0.995505308664836 23 0.00215993835543989 0.00431987671087977 0.99784006164456 24 0.00210345550151814 0.00420691100303627 0.997896544498482 25 0.0039239861705777 0.0078479723411554 0.996076013829422 26 0.00196169696979057 0.00392339393958114 0.99803830303021 27 0.000943696442017871 0.00188739288403574 0.999056303557982 28 0.00137176950101815 0.00274353900203629 0.998628230498982 29 0.000765050882974664 0.00153010176594933 0.999234949117025 30 0.000361972503139535 0.00072394500627907 0.99963802749686 31 0.000235211628896379 0.000470423257792759 0.999764788371104 32 0.000472915844283051 0.000945831688566102 0.999527084155717 33 0.00119184172626076 0.00238368345252151 0.998808158273739 34 0.000637731488203203 0.00127546297640641 0.999362268511797 35 0.00222762991629438 0.00445525983258877 0.997772370083706 36 0.00169644746846205 0.0033928949369241 0.998303552531538 37 0.000994162315617526 0.00198832463123505 0.999005837684382 38 0.000464625112479599 0.000929250224959199 0.99953537488752 39 0.00333831634734398 0.00667663269468795 0.996661683652656 40 0.00273047919558176 0.00546095839116353 0.997269520804418 41 0.0148574088047133 0.0297148176094266 0.985142591195287 42 0.0124231240985120 0.0248462481970241 0.987576875901488 43 0.00610574588243238 0.0122114917648648 0.993894254117568 44 0.0466559393182693 0.0933118786365385 0.95334406068173 45 0.189303419417153 0.378606838834307 0.810696580582847 46 0.230691054831366 0.461382109662732 0.769308945168634 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 24 0.648648648648649 NOK 5% type I error level 31 0.837837837837838 NOK 10% type I error level 34 0.918918918918919 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656332ir6oys4xx5rlvos/10dw611258656255.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656332ir6oys4xx5rlvos/10dw611258656255.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656332ir6oys4xx5rlvos/1ogsa1258656255.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656332ir6oys4xx5rlvos/1ogsa1258656255.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656332ir6oys4xx5rlvos/2hoy91258656255.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656332ir6oys4xx5rlvos/2hoy91258656255.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656332ir6oys4xx5rlvos/348wz1258656255.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656332ir6oys4xx5rlvos/348wz1258656255.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656332ir6oys4xx5rlvos/4hni81258656255.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656332ir6oys4xx5rlvos/4hni81258656255.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656332ir6oys4xx5rlvos/5tntd1258656255.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656332ir6oys4xx5rlvos/5tntd1258656255.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656332ir6oys4xx5rlvos/6trc61258656255.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656332ir6oys4xx5rlvos/6trc61258656255.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656332ir6oys4xx5rlvos/7n9zj1258656255.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656332ir6oys4xx5rlvos/7n9zj1258656255.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656332ir6oys4xx5rlvos/8182p1258656255.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656332ir6oys4xx5rlvos/8182p1258656255.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656332ir6oys4xx5rlvos/9t2t11258656255.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258656332ir6oys4xx5rlvos/9t2t11258656255.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