workshop 7

*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 02:34:23 -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/t1258709778qv6l64snc4voiu3.htm/, Retrieved Fri, 20 Nov 2009 10:36:30 +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/t1258709778qv6l64snc4voiu3.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 «

8,9 8,6 8,9 8,5 8,9 8,3 8,9 7,8 9 7,8 9 8 9 8,6 9 8,9 9 8,9 9 8,6 9 8,3 9,1 8,3 9 8,3 9,1 8,4 9,1 8,5 9 8,4 9 8,6 9 8,5 9 8,5 8,9 8,4 8,9 8,5 8,9 8,5 8,9 8,5 8,8 8,5 8,8 8,5 8,7 8,5 8,7 8,5 8,5 8,5 8,5 8,6 8,4 8,4 8,2 8,1 8,2 8 8,1 8 8,1 8 8 8 7,9 7,9 7,8 7,8 7,7 7,8 7,6 7,9 7,5 8,1 7,5 8 7,5 7,6 7,5 7,3 7,5 7 7,4 6,8 7,4 7 7,3 7,1 7,3 7,2 7,3 7,1 7,2 6,9 7,2 6,7 7,3 6,7 7,4 6,6 7,4 6,9 7,5 7,3 7,6 7,5 7,7 7,3 7,9 7,1 8 6,9 8,2 7,1

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] = + 6.31542137271935 + 0.362417773364777X[t] -0.263814178147375M1[t] -0.264809689297091M2[t] -0.245805200446816M3[t] -0.252304000661949M4[t] -0.195044578213561M5[t] -0.176040089363286M6[t] -0.200525733316784M7[t] -0.176017955401100M8[t] -0.149765111083529M9[t] -0.0635122667659586M10[t] -0.0300110669810927M11[t] -0.024507777915684t + 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) 6.31542137271935 0.969361 6.515 0 0 X 0.362417773364777 0.107132 3.3829 0.001474 0.000737 M1 -0.263814178147375 0.194209 -1.3584 0.18096 0.09048 M2 -0.264809689297091 0.193965 -1.3652 0.178816 0.089408 M3 -0.245805200446816 0.193753 -1.2687 0.210947 0.105474 M4 -0.252304000661949 0.193881 -1.3013 0.199624 0.099812 M5 -0.195044578213561 0.193296 -1.009 0.31823 0.159115 M6 -0.176040089363286 0.193169 -0.9113 0.366875 0.183437 M7 -0.200525733316784 0.192673 -1.0408 0.303429 0.151714 M8 -0.176017955401100 0.192584 -0.914 0.365494 0.182747 M9 -0.149765111083529 0.192452 -0.7782 0.440438 0.220219 M10 -0.0635122667659586 0.192396 -0.3301 0.742814 0.371407 M11 -0.0300110669810927 0.192491 -0.1559 0.876787 0.438393 t -0.024507777915684 0.004069 -6.0225 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.919685918816145 R-squared 0.845822189268697 Adjusted R-squared 0.802250199279415 F-TEST (value) 19.4120624161707 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 1.93178806284777e-14 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.30409309579165 Sum Squared Residuals 4.25374010177488 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.9 9.14389226759343 -0.243892267593432 2 8.9 9.0821472011915 -0.182147201191508 3 8.9 9.00416035745315 -0.104160357453144 4 8.9 8.79194489263994 0.108055107360063 5 9 8.82469653717264 0.175303462827358 6 9 8.89167680278019 0.108323197219811 7 9 9.06013404492987 -0.0601340449298727 8 9 9.1688593769393 -0.168859376939306 9 9 9.1706044433412 -0.170604443341193 10 9 9.12362417773365 -0.123624177733646 11 9 9.0238922675934 -0.0238922675933955 12 9.1 9.0293955566588 0.0706044433411955 13 9 8.74107360059575 0.258926399404254 14 9.1 8.75181208886682 0.348187911133177 15 9.1 8.7825505771379 0.317449422862108 16 9 8.7153022216706 0.284697778329403 17 9 8.82053742087626 0.179462579123744 18 9 8.77879235447437 0.22120764552563 19 9 8.72979893260519 0.270201067394813 20 8.9 8.69355715526871 0.206442844731291 21 8.9 8.73154399900707 0.168456000992926 22 8.9 8.79328906540896 0.106710934591039 23 8.9 8.80228248727814 0.0977175127218574 24 8.8 8.80778577634355 -0.00778577634355094 25 8.8 8.5194638202805 0.280536179719508 26 8.7 8.4939605312151 0.206039468784906 27 8.7 8.48845724214968 0.211542757850315 28 8.5 8.45745066401887 0.0425493359811334 29 8.5 8.52644408588805 -0.0264440858880486 30 8.4 8.44845724214968 -0.0484572421496844 31 8.2 8.29073848827107 -0.0907384882710694 32 8.2 8.2544967109346 -0.0544967109345916 33 8.1 8.25624177733648 -0.156241777336478 34 8.1 8.31798684373836 -0.217986843738365 35 8 8.32698026560755 -0.326980265607547 36 7.9 8.29624177733648 -0.396241777336477 37 7.8 7.97167804393694 -0.171678043936941 38 7.7 7.94617475487154 -0.246174754871541 39 7.6 7.97691324314261 -0.37691324314261 40 7.5 8.01839021968475 -0.518390219684748 41 7.5 8.01490008688097 -0.514900086880975 42 7.5 7.86442968846965 -0.364429688469655 43 7.5 7.70671093459104 -0.206710934591039 44 7.5 7.5979856025816 -0.0979856025816057 45 7.4 7.52724711431054 -0.127247114310537 46 7.4 7.66147573538538 -0.261475735385379 47 7.3 7.70671093459104 -0.406710934591039 48 7.3 7.74845600099293 -0.448456000992926 49 7.3 7.42389226759339 -0.123892267593389 50 7.2 7.32590542385503 -0.125905423855034 51 7.2 7.24791858011667 -0.0479185801166692 52 7.3 7.21691200198585 0.0830879980141479 53 7.4 7.21342186918208 0.186578130817922 54 7.4 7.3166439121261 0.0833560878738974 55 7.5 7.41261759960283 0.0873824003971687 56 7.6 7.48510115427579 0.114898845724213 57 7.7 7.41436266600472 0.285637333995282 58 7.9 7.40362417773365 0.496375822266351 59 8 7.34013404492988 0.659865955070124 60 8.2 7.41812088866824 0.78187911133176 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00565460448496671 0.0113092089699334 0.994345395515033 18 0.00145592059501245 0.00291184119002491 0.998544079404988 19 0.000833493231213192 0.00166698646242638 0.999166506768787 20 0.000976725365942209 0.00195345073188442 0.999023274634058 21 0.000440009225778258 0.000880018451556515 0.999559990774222 22 0.000185747453728755 0.000371494907457511 0.999814252546271 23 8.73473722526964e-05 0.000174694744505393 0.999912652627747 24 0.000282244111715783 0.000564488223431566 0.999717755888284 25 0.00018982728945381 0.00037965457890762 0.999810172710546 26 0.000357756886603298 0.000715513773206595 0.999642243113397 27 0.000585736337716173 0.00117147267543235 0.999414263662284 28 0.0022549326805241 0.0045098653610482 0.997745067319476 29 0.00523385313879767 0.0104677062775953 0.994766146861202 30 0.0170639590296748 0.0341279180593495 0.982936040970325 31 0.069330552562558 0.138661105125116 0.930669447437442 32 0.109968379496179 0.219936758992358 0.890031620503821 33 0.131691913266340 0.263383826532680 0.86830808673366 34 0.140534285982337 0.281068571964674 0.859465714017663 35 0.160277270778364 0.320554541556729 0.839722729221636 36 0.167107229679937 0.334214459359873 0.832892770320063 37 0.19554857406917 0.39109714813834 0.80445142593083 38 0.23994984950541 0.47989969901082 0.76005015049459 39 0.266112843511326 0.532225687022653 0.733887156488674 40 0.278705616048358 0.557411232096716 0.721294383951642 41 0.245722523997096 0.491445047994192 0.754277476002904 42 0.501761727022931 0.996476545954138 0.498238272977069 43 0.89729295927418 0.205414081451641 0.102707040725820 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 11 0.407407407407407 NOK 5% type I error level 14 0.518518518518518 NOK 10% type I error level 14 0.518518518518518 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258709778qv6l64snc4voiu3/10wtkl1258709658.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258709778qv6l64snc4voiu3/10wtkl1258709658.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258709778qv6l64snc4voiu3/1makf1258709658.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258709778qv6l64snc4voiu3/1makf1258709658.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258709778qv6l64snc4voiu3/2cp3d1258709658.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258709778qv6l64snc4voiu3/2cp3d1258709658.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258709778qv6l64snc4voiu3/3x6fr1258709658.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258709778qv6l64snc4voiu3/3x6fr1258709658.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258709778qv6l64snc4voiu3/4x09g1258709658.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258709778qv6l64snc4voiu3/4x09g1258709658.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258709778qv6l64snc4voiu3/5ln021258709658.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258709778qv6l64snc4voiu3/5ln021258709658.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258709778qv6l64snc4voiu3/6xhw91258709658.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258709778qv6l64snc4voiu3/6xhw91258709658.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258709778qv6l64snc4voiu3/7o7zl1258709658.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258709778qv6l64snc4voiu3/7o7zl1258709658.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258709778qv6l64snc4voiu3/86vm81258709658.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258709778qv6l64snc4voiu3/86vm81258709658.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258709778qv6l64snc4voiu3/9y5a11258709658.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258709778qv6l64snc4voiu3/9y5a11258709658.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