relatie lichten-ongevallen

*The author of this computation has been verified*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Mon, 23 Nov 2009 08:31:21 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/23/t1258990363rm4w5q7wvoc0yqo.htm/, Retrieved Mon, 23 Nov 2009 16:32:55 +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/23/t1258990363rm4w5q7wvoc0yqo.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:

0=lichten niet aan 1= lichten aan

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

105,29 0 101,23 0 102,33 0 100,26 0 104,13 0 103,54 0 100,02 0 98,66 0 108,64 0 105,67 0 102,66 0 100,3 0 95,13 0 93,2 0 102,84 0 101,36 0 102,55 0 103,12 0 96,3 0 99,13 0 102,23 0 104,3 0 99,58 0 98,45 0 96,23 0 97,62 0 102,32 0 105,23 0 100,05 0 102,66 0 100,98 0 99,2 0 98,36 0 102,56 0 97,33 0 96,22 0 99,22 0 102,32 0 104,22 0 100,06 0 107,23 0 99,62 0 98,32 1 101,23 1 102,33 1 100,6 1 95,63 1 94,63 1 95,66 1 100,78 1 90,36 1 95,45 1 103,65 1 99,89 1 97,68 1 99,62 1 98,33 1 96,23 1 102,65 1 99,35 1 92,65 1 100,6 1 97,67 1

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 99.8161370716511 -1.16314641744547X[t] -0.720998442367575M1[t] + 1.25069262720664M2[t] + 1.95905036344756M3[t] + 2.10250882658359M4[t] + 5.1958665628245M5[t] + 3.48322429906542M6[t] + 0.65321131879543M7[t] + 1.60456905503635M8[t] + 4.05792679127726M9[t] + 3.99528452751817M10[t] + 1.73664226375908M11[t] -0.0433577362409143t + 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) 99.8161370716511 1.695379 58.8754 0 0 X -1.16314641744547 1.479181 -0.7863 0.435452 0.217726 M1 -0.720998442367575 1.905165 -0.3784 0.706735 0.353368 M2 1.25069262720664 1.903351 0.6571 0.514191 0.257096 M3 1.95905036344756 1.902297 1.0298 0.308145 0.154072 M4 2.10250882658359 1.993964 1.0544 0.296855 0.148427 M5 5.1958665628245 1.993129 2.6069 0.012072 0.006036 M6 3.48322429906542 1.993022 1.7477 0.086779 0.043389 M7 0.65321131879543 1.994798 0.3275 0.744717 0.372358 M8 1.60456905503635 1.991524 0.8057 0.424309 0.212155 M9 4.05792679127726 1.988974 2.0402 0.046738 0.023369 M10 3.99528452751817 1.98715 2.0106 0.049892 0.024946 M11 1.73664226375908 1.986055 0.8744 0.386156 0.193078 t -0.0433577362409143 0.03808 -1.1386 0.260414 0.130207 Multiple Linear Regression - Regression Statistics Multiple R 0.616314324614588 R-squared 0.379843346725136 Adjusted R-squared 0.215311989733846 F-TEST (value) 2.30863802299548 F-TEST (DF numerator) 13 F-TEST (DF denominator) 49 p-value 0.0176730361287295 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.13965149329566 Sum Squared Residuals 483.01316346833 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 105.29 99.0517808930424 6.23821910695756 2 101.23 100.980114226376 0.249885773624083 3 102.33 101.645114226376 0.68488577362408 4 100.26 101.745214953271 -1.48521495327104 5 104.13 104.795214953271 -0.665214953271052 6 103.54 103.039214953271 0.500785046728965 7 100.02 100.165844236760 -0.145844236760132 8 98.66 101.073844236760 -2.41384423676013 9 108.64 103.483844236760 5.15615576323987 10 105.67 103.377844236760 2.29215576323986 11 102.66 101.075844236760 1.58415576323986 12 100.3 99.2958442367601 1.00415576323987 13 95.13 98.5314880581516 -3.40148805815165 14 93.2 100.459821391485 -7.25982139148495 15 102.84 101.124821391485 1.71517860851506 16 101.36 101.22492211838 0.135077881619931 17 102.55 104.27492211838 -1.72492211838007 18 103.12 102.51892211838 0.601077881619936 19 96.3 99.6455514018692 -3.34555140186916 20 99.13 100.553551401869 -1.42355140186916 21 102.23 102.963551401869 -0.733551401869156 22 104.3 102.857551401869 1.44244859813084 23 99.58 100.555551401869 -0.97555140186916 24 98.45 98.7755514018692 -0.325551401869159 25 96.23 98.0111952232607 -1.78119522326067 26 97.62 99.939528556594 -2.31952855659397 27 102.32 100.604528556594 1.71547144340602 28 105.23 100.704629283489 4.52537071651091 29 100.05 103.754629283489 -3.70462928348909 30 102.66 101.998629283489 0.6613707165109 31 100.98 99.1252585669782 1.85474143302182 32 99.2 100.033258566978 -0.833258566978186 33 98.36 102.443258566978 -4.08325856697819 34 102.56 102.337258566978 0.222741433021815 35 97.33 100.035258566978 -2.70525856697819 36 96.22 98.2552585669782 -2.03525856697819 37 99.22 97.4909023883697 1.7290976116303 38 102.32 99.419235721703 2.90076427829699 39 104.22 100.084235721703 4.135764278297 40 100.06 100.184336448598 -0.124336448598122 41 107.23 103.234336448598 3.99566355140189 42 99.62 101.478336448598 -1.85833644859812 43 98.32 97.4418193146417 0.878180685358247 44 101.23 98.3498193146417 2.88018068535826 45 102.33 100.759819314642 1.57018068535825 46 100.6 100.653819314642 -0.0538193146417504 47 95.63 98.3518193146417 -2.72181931464175 48 94.63 96.5718193146417 -1.94181931464175 49 95.66 95.8074631360333 -0.147463136033261 50 100.78 97.7357964693666 3.04420353063344 51 90.36 98.4007964693666 -8.04079646936656 52 95.45 98.5008971962617 -3.05089719626168 53 103.65 101.550897196262 2.09910280373833 54 99.89 99.7948971962617 0.0951028037383176 55 97.68 96.9215264797508 0.758473520249232 56 99.62 97.8295264797508 1.79047352024923 57 98.33 100.239526479751 -1.90952647975078 58 96.23 100.133526479751 -3.90352647975077 59 102.65 97.8315264797508 4.81847352024923 60 99.35 96.0515264797508 3.29847352024922 61 92.65 95.2871703011423 -2.63717030114228 62 100.6 97.2155036344756 3.38449636552440 63 97.67 97.8805036344756 -0.210503634475587 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.85481076170617 0.290378476587659 0.145189238293829 18 0.782941095816412 0.434117808367177 0.217058904183588 19 0.683200509314376 0.633598981371248 0.316799490685624 20 0.620578760852334 0.758842478295332 0.379421239147666 21 0.555973204074385 0.888053591851229 0.444026795925615 22 0.468746825121946 0.937493650243893 0.531253174878054 23 0.354976815969053 0.709953631938105 0.645023184030947 24 0.260971443166907 0.521942886333814 0.739028556833093 25 0.180020701393697 0.360041402787394 0.819979298606303 26 0.219954414720282 0.439908829440564 0.780045585279718 27 0.201605211083524 0.403210422167048 0.798394788916476 28 0.418651356887086 0.837302713774173 0.581348643112914 29 0.421673628564609 0.843347257129218 0.578326371435391 30 0.350312242069452 0.700624484138905 0.649687757930548 31 0.345486664819384 0.690973329638768 0.654513335180616 32 0.302126106109771 0.604252212219542 0.697873893890229 33 0.374599278695154 0.749198557390308 0.625400721304846 34 0.292051448304320 0.584102896608641 0.70794855169568 35 0.292736000671649 0.585472001343299 0.707263999328351 36 0.285915957190497 0.571831914380994 0.714084042809503 37 0.233020360254323 0.466040720508646 0.766979639745677 38 0.307855757040834 0.615711514081667 0.692144242959166 39 0.427141340433714 0.854282680867429 0.572858659566286 40 0.346489901798661 0.692979803597321 0.653510098201339 41 0.35432978892375 0.7086595778475 0.64567021107625 42 0.261280543164749 0.522561086329499 0.738719456835251 43 0.175910000784693 0.351820001569385 0.824089999215307 44 0.121208181950737 0.242416363901475 0.878791818049263 45 0.118267606168703 0.236535212337406 0.881732393831297 46 0.185158199670356 0.370316399340712 0.814841800329644 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 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258990363rm4w5q7wvoc0yqo/10y2651258990276.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258990363rm4w5q7wvoc0yqo/10y2651258990276.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258990363rm4w5q7wvoc0yqo/183rg1258990276.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258990363rm4w5q7wvoc0yqo/183rg1258990276.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258990363rm4w5q7wvoc0yqo/2xmjj1258990276.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258990363rm4w5q7wvoc0yqo/2xmjj1258990276.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258990363rm4w5q7wvoc0yqo/3nu5l1258990276.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258990363rm4w5q7wvoc0yqo/3nu5l1258990276.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258990363rm4w5q7wvoc0yqo/4lp2d1258990276.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258990363rm4w5q7wvoc0yqo/4lp2d1258990276.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258990363rm4w5q7wvoc0yqo/5f5ht1258990276.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258990363rm4w5q7wvoc0yqo/5f5ht1258990276.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258990363rm4w5q7wvoc0yqo/6v8m01258990276.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258990363rm4w5q7wvoc0yqo/6v8m01258990276.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258990363rm4w5q7wvoc0yqo/7sm4f1258990276.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258990363rm4w5q7wvoc0yqo/7sm4f1258990276.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258990363rm4w5q7wvoc0yqo/80qe01258990276.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258990363rm4w5q7wvoc0yqo/80qe01258990276.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258990363rm4w5q7wvoc0yqo/9l6g61258990276.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258990363rm4w5q7wvoc0yqo/9l6g61258990276.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