ws7

*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: Tue, 17 Nov 2009 11:20:14 -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/17/t12584823866qkyz9l3mk3e3es.htm/, Retrieved Tue, 17 Nov 2009 19:26:38 +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/17/t12584823866qkyz9l3mk3e3es.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:

dummies geen lineaire trend

Dataseries X:

» Textbox « » Textfile « » CSV «

325412 285351 326011 286602 328282 283042 317480 276687 317539 277915 313737 277128 312276 277103 309391 275037 302950 270150 300316 267140 304035 264993 333476 287259 337698 291186 335932 292300 323931 288186 313927 281477 314485 282656 313218 280190 309664 280408 302963 276836 298989 275216 298423 274352 310631 271311 329765 289802 335083 290726 327616 292300 309119 278506 295916 269826 291413 265861 291542 269034 284678 264176 276475 255198 272566 253353 264981 246057 263290 235372 296806 258556 303598 260993 286994 254663 276427 250643 266424 243422 267153 247105 268381 248541 262522 245039 255542 237080 253158 237085 243803 225554 250741 226839 280445 247934 285257 248333 270976 246969 261076 245098 255603 246263 260376 255765 263903 264319 264291 268347 263276 273046 262572 273963 256167 267430 264221 271993 293860 292710 300713 295881 287224 293299

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 Werkl_vrouwen[t] = + 8149.59315102303 + 1.08526219535748Werkl_mannen[t] + 3965.82953738877M1[t] -3722.61937394788M2[t] -421.223610744918M3[t] -4284.16580455731M4[t] -6484.6345136416M5[t] -8672.62418484014M6[t] -11244.2441395232M7[t] -12521.0147386811M8[t] -14390.7151163799M9[t] -13354.4041125638M10[t] -5332.85341087203M11[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) 8149.59315102303 34557.957251 0.2358 0.814552 0.407276 Werkl_mannen 1.08526219535748 0.122893 8.831 0 0 M1 3965.82953738877 9586.217705 0.4137 0.680898 0.340449 M2 -3722.61937394788 9581.283341 -0.3885 0.699307 0.349653 M3 -421.223610744918 10031.015512 -0.042 0.966676 0.483338 M4 -4284.16580455731 10105.552494 -0.4239 0.673464 0.336732 M5 -6484.6345136416 10068.805942 -0.644 0.522558 0.261279 M6 -8672.62418484014 10043.800362 -0.8635 0.39208 0.19604 M7 -11244.2441395232 10053.534111 -1.1184 0.268834 0.134417 M8 -12521.0147386811 10107.23325 -1.2388 0.221315 0.110657 M9 -14390.7151163799 10135.074207 -1.4199 0.16197 0.080985 M10 -13354.4041125638 10275.437993 -1.2996 0.199804 0.099902 M11 -5332.85341087203 10334.641822 -0.516 0.608163 0.304082 Multiple Linear Regression - Regression Statistics Multiple R 0.840940228821094 R-squared 0.707180468449673 Adjusted R-squared 0.635469562763879 F-TEST (value) 9.86154702254393 F-TEST (DF numerator) 12 F-TEST (DF denominator) 49 p-value 2.13789475012049e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 15815.239232929 Sum Squared Residuals 12255967807.7441 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 325412 321796.075395865 3615.92460413544 2 326011 315465.28949092 10545.7105090796 3 328282 314903.151838651 13378.8481613492 4 317480 304143.368393342 13336.6316066584 5 317539 303275.601660156 14263.3983398437 6 313737 300233.510641211 13503.4893587885 7 312276 297634.759131644 14641.2408683556 8 309391 294115.836836878 15275.1631631221 9 302950 286942.460110467 16007.5398895329 10 300316 284712.131906257 15603.8680937427 11 304035 290403.624674516 13631.3753254835 12 333476 319900.926127218 13575.0738727818 13 337698 328128.580305776 9569.41969422416 14 335932 321649.113480067 14282.8865199326 15 323931 320485.74057157 3445.2594284303 16 313927 309341.774309104 4585.22569089605 17 314485 308420.829728346 6064.17027165386 18 313218 303556.583483396 9661.41651660395 19 309664 301221.550687301 8442.44931269909 20 302963 296068.223526326 6894.77647367394 21 298989 292440.398392148 6548.60160785185 22 298423 292539.042859175 5883.95714082459 23 310631 297260.311224785 13370.6887752149 24 329765 322660.747890012 7104.2521099877 25 335083 327629.359695911 7453.6403040886 26 327616 321649.113480067 5966.88651993258 27 309119 309980.402520509 -861.40252050927 28 295916 296697.384470994 -781.384470993921 29 291413 290193.851157317 1219.14884268278 30 291542 291449.398431988 92.6015680120228 31 284678 283605.574732258 1072.42526774174 32 276475 272585.320143181 3889.67985681915 33 272566 268713.311015047 3852.68898495249 34 264981 261831.549041535 3149.45095846456 35 263290 258257.073185833 5032.9268141675 36 296806 288750.645333872 8055.3546661276 37 303598 295361.258841347 8236.74115865264 38 286994 280803.100233398 6190.89976660215 39 276427 279741.741971264 -3314.74197126373 40 266424 268042.121464775 -1618.12146477495 41 267153 269838.673421192 -2685.67342119227 42 268381 269209.120262527 -828.120262527082 43 262522 262836.912099702 -314.91209970211 44 255542 252922.539687694 2619.46031230602 45 253158 251058.265620972 2099.73437902802 46 243803 239580.418250121 4222.58174987902 47 250741 248996.530872847 1744.4691271529 48 280445 277222.990294785 3222.00970521478 49 285257 281621.839448122 3635.16055187836 50 270976 272453.092902317 -1477.09290231737 51 261076 273723.963098006 -12647.9630980065 52 255603 271125.351361786 -15522.3513617856 53 260376 279237.044032988 -18861.0440329881 54 263903 286332.387180877 -22429.3871808774 55 264291 288132.203349094 -23841.2033490943 56 263276 291955.079805921 -28679.0798059212 57 262572 291080.564861365 -28508.5648613652 58 256167 285026.857942911 -28859.8579429109 59 264221 298000.460042019 -33779.4600420189 60 293860 325816.690354112 -31956.6903541119 61 300713 333223.886312979 -32510.8863129792 62 287224 322733.290413230 -35509.2904132296 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0666004507556592 0.133200901511318 0.93339954924434 17 0.0283276871886031 0.0566553743772062 0.971672312811397 18 0.00967125850251656 0.0193425170050331 0.990328741497484 19 0.00365874557172398 0.00731749114344797 0.996341254428276 20 0.00197750705795190 0.00395501411590379 0.998022492942048 21 0.000885428649473363 0.00177085729894673 0.999114571350527 22 0.000355904838349836 0.000711809676699671 0.99964409516165 23 0.00029442700289344 0.00058885400578688 0.999705572997106 24 0.000176126552742078 0.000352253105484157 0.999823873447258 25 0.000117543029703317 0.000235086059406633 0.999882456970297 26 0.000170412260227209 0.000340824520454419 0.999829587739773 27 0.00240501015621946 0.00481002031243892 0.99759498984378 28 0.0116965642537798 0.0233931285075595 0.98830343574622 29 0.0250731366755310 0.0501462733510621 0.974926863324469 30 0.0574842245945893 0.114968449189179 0.94251577540541 31 0.0900255093082193 0.180051018616439 0.909974490691781 32 0.115123732953075 0.230247465906149 0.884876267046925 33 0.141485693734211 0.282971387468422 0.858514306265789 34 0.192878417028213 0.385756834056426 0.807121582971787 35 0.180795161649715 0.36159032329943 0.819204838350285 36 0.254647366870413 0.509294733740826 0.745352633129587 37 0.428223707639172 0.856447415278343 0.571776292360828 38 0.571023655522316 0.857952688955367 0.428976344477684 39 0.764046666645295 0.471906666709411 0.235953333354706 40 0.893718351496934 0.212563297006131 0.106281648503066 41 0.95558087370407 0.088838252591861 0.0444191262959305 42 0.995188058752483 0.00962388249503339 0.00481194124751669 43 0.998649403180442 0.00270119363911509 0.00135059681955755 44 0.99908009557725 0.00183980884549971 0.000919904422749853 45 0.998597988346004 0.00280402330799140 0.00140201165399570 46 0.993188076607125 0.0136238467857502 0.00681192339287512 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 13 0.419354838709677 NOK 5% type I error level 16 0.516129032258065 NOK 10% type I error level 19 0.612903225806452 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584823866qkyz9l3mk3e3es/106jk01258482009.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584823866qkyz9l3mk3e3es/106jk01258482009.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584823866qkyz9l3mk3e3es/172ay1258482009.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584823866qkyz9l3mk3e3es/172ay1258482009.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584823866qkyz9l3mk3e3es/2nmnl1258482009.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584823866qkyz9l3mk3e3es/2nmnl1258482009.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584823866qkyz9l3mk3e3es/346on1258482009.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584823866qkyz9l3mk3e3es/346on1258482009.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584823866qkyz9l3mk3e3es/4mv901258482009.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584823866qkyz9l3mk3e3es/4mv901258482009.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584823866qkyz9l3mk3e3es/52y491258482009.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584823866qkyz9l3mk3e3es/52y491258482009.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584823866qkyz9l3mk3e3es/6rycb1258482009.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584823866qkyz9l3mk3e3es/6rycb1258482009.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584823866qkyz9l3mk3e3es/7ha301258482009.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584823866qkyz9l3mk3e3es/7ha301258482009.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584823866qkyz9l3mk3e3es/8ew1c1258482009.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584823866qkyz9l3mk3e3es/8ew1c1258482009.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584823866qkyz9l3mk3e3es/9nhmj1258482009.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584823866qkyz9l3mk3e3es/9nhmj1258482009.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