*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: Wed, 18 Nov 2009 07:51:34 -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/18/t1258555936451d7bqx7qp18ca.htm/, Retrieved Wed, 18 Nov 2009 15:52:29 +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/18/t1258555936451d7bqx7qp18ca.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 95.05 8.8 96.84 8.3 96.92 7.5 97.44 7.2 97.78 7.4 97.69 8.8 96.67 9.3 98.29 9.3 98.2 8.7 98.71 8.2 98.54 8.3 98.2 8.5 96.92 8.6 99.06 8.5 99.65 8.2 99.82 8.1 99.99 7.9 100.33 8.6 99.31 8.7 101.1 8.7 101.1 8.5 100.93 8.4 100.85 8.5 100.93 8.7 99.6 8.7 101.88 8.6 101.81 8.5 102.38 8.3 102.74 8 102.82 8.2 101.72 8.1 103.47 8.1 102.98 8 102.68 7.9 102.9 7.9 103.03 8 101.29 8 103.69 7.9 103.68 8 104.2 7.7 104.08 7.2 104.16 7.5 103.05 7.3 104.66 7 104.46 7 104.95 7 105.85 7.2 106.23 7.3 104.86 7.1 107.44 6.8 108.23 6.4 108.45 6.1 109.39 6.5 110.15 7.7 109.13 7.9 110.28 7.5 110.17 6.9 109.99 6.6 109.26 6.9 109.11

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 Werkloosheidsgraad[t] = + 12.6232166151388 -0.0390651995205026Consumptieprijs[t] + 0.114912150907034M1[t] + 0.185116969233002M2[t] -0.00132413390026129M3[t] -0.262921152292983M4[t] -0.466940213055976M5[t] -0.515022054569101M6[t] + 0.226580126935367M7[t] + 0.41123630477492M8[t] + 0.287059601059348M9[t] + 0.0125710668248607M10[t] -0.163558205789488M11[t] -0.0227769017990774t + 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) 12.6232166151388 7.494561 1.6843 0.098895 0.049448 Consumptieprijs -0.0390651995205026 0.078488 -0.4977 0.621052 0.310526 M1 0.114912150907034 0.319037 0.3602 0.720358 0.360179 M2 0.185116969233002 0.299146 0.6188 0.539089 0.269544 M3 -0.00132413390026129 0.299335 -0.0044 0.99649 0.498245 M4 -0.262921152292983 0.301371 -0.8724 0.387512 0.193756 M5 -0.466940213055976 0.302935 -1.5414 0.130075 0.065037 M6 -0.515022054569101 0.302843 -1.7006 0.095766 0.047883 M7 0.226580126935367 0.296438 0.7643 0.448566 0.224283 M8 0.41123630477492 0.303954 1.353 0.182682 0.091341 M9 0.287059601059348 0.297591 0.9646 0.339784 0.169892 M10 0.0125710668248607 0.295961 0.0425 0.966304 0.483152 M11 -0.163558205789488 0.294662 -0.5551 0.581536 0.290768 t -0.0227769017990774 0.017935 -1.27 0.21047 0.105235 Multiple Linear Regression - Regression Statistics Multiple R 0.833448099496925 R-squared 0.694635734555036 Adjusted R-squared 0.60833713779885 F-TEST (value) 8.04921239354042 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 4.6478183413079e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.465180206988506 Sum Squared Residuals 9.95406074879798 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.9 9.00220464982302 -0.102204649823021 2 8.8 8.97970585920818 -0.179705859208180 3 8.3 8.7673626383142 -0.467362638314199 4 7.5 8.46267481437174 -0.962674814371738 5 7.2 8.2225966839727 -1.02259668397270 6 7.4 8.15525380861734 -0.755253808617341 7 8.8 8.91392559183364 -0.113925591833643 8 9.3 9.0125192446509 0.287480755349095 9 9.3 8.8690815070931 0.4309184929069 10 8.7 8.55189281930408 0.148107180695919 11 8.2 8.35962772880914 -0.159627728809140 12 8.3 8.51369120063652 -0.213691200636520 13 8.5 8.65582990513072 -0.155829905130721 14 8.6 8.61965829468374 -0.0196582946837365 15 8.5 8.3873918220343 0.112608177965701 16 8.2 8.09637681792401 0.103623182075985 17 8.1 7.86293977144346 0.237060228556541 18 7.9 7.77879886029429 0.121201139705715 19 8.6 8.53747064351059 0.0625293564894112 20 8.7 8.62942321240937 0.0705767875906342 21 8.7 8.48246960689472 0.217530393105284 22 8.5 8.19184525477964 0.308154745220364 23 8.4 7.99606429632785 0.40393570367215 24 8.5 8.13372038435662 0.366279615643380 25 8.7 8.27781234882685 0.422187651173153 26 8.7 8.23617161044699 0.463828389553009 27 8.6 8.02968816948109 0.570311830518915 28 8.5 7.7230470855626 0.7769529144374 29 8.3 7.48218765117315 0.817812348826852 30 8 7.4082036918993 0.591796308100694 31 8.2 8.17000069107725 0.0299993089227506 32 8.1 8.26351586795685 -0.163515867956846 33 8.1 8.13570421020724 -0.0357042102072422 34 8 7.85015833402983 0.149841665970172 35 7.9 7.64265781572189 0.257342184278109 36 7.9 7.77836064377464 0.121639356225364 37 8 7.93846934004827 0.0615306599517327 38 8 7.89214077772595 0.107859222274048 39 7.9 7.68331342478882 0.216686575211184 40 8 7.37862560084636 0.621374399153644 41 7.7 7.15651746222675 0.543482537773254 42 7.2 7.0825335029529 0.117466497047096 43 7.5 7.84472115412605 -0.344721154126052 44 7.3 7.94370545893852 -0.643705458938519 45 7 7.80456489332797 -0.80456489332797 46 7 7.48815750952936 -0.488157509529358 47 7 7.25409265554748 -0.254092655547481 48 7.2 7.3800291837201 -0.180029183720100 49 7.3 7.52568375617115 -0.225683756171145 50 7.1 7.47232345793514 -0.372323457935139 51 6.8 7.2322439453816 -0.432243945381602 52 6.4 6.93927568129529 -0.539275681295291 53 6.1 6.67575843118395 -0.575758431183949 54 6.5 6.57521013623616 -0.0752101362361645 55 7.7 7.33388191945247 0.366118080547532 56 7.9 7.45083621604437 0.449163783955635 57 7.5 7.30817978247697 0.191820217523029 58 6.9 7.0179460823571 -0.117946082357097 59 6.6 6.84755750359364 -0.247557503593638 60 6.9 6.99419858751212 -0.0941985875121235 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.714312903041102 0.571374193917797 0.285687096958898 18 0.584827691730643 0.830344616538714 0.415172308269357 19 0.61445199008419 0.77109601983162 0.38554800991581 20 0.720992467334776 0.558015065330448 0.279007532665224 21 0.697246814080346 0.605506371839308 0.302753185919654 22 0.61706931162798 0.76586137674404 0.38293068837202 23 0.511318799038498 0.977362401923003 0.488681200961502 24 0.426293568036181 0.852587136072362 0.573706431963819 25 0.324622382049554 0.649244764099107 0.675377617950446 26 0.233417914490706 0.466835828981413 0.766582085509294 27 0.165212182513271 0.330424365026541 0.83478781748673 28 0.175197894056662 0.350395788113324 0.824802105943338 29 0.179248371313647 0.358496742627293 0.820751628686353 30 0.128311552319515 0.25662310463903 0.871688447680485 31 0.152559594065685 0.305119188131369 0.847440405934315 32 0.278540125052031 0.557080250104061 0.72145987494797 33 0.332785250564278 0.665570501128557 0.667214749435722 34 0.259061598776888 0.518123197553776 0.740938401223112 35 0.184319662741843 0.368639325483686 0.815680337258157 36 0.141743948806126 0.283487897612251 0.858256051193874 37 0.0926363001978155 0.185272600395631 0.907363699802185 38 0.0609360328968898 0.121872065793780 0.93906396710311 39 0.0452379560814498 0.0904759121628997 0.95476204391855 40 0.0936875764533178 0.187375152906636 0.906312423546682 41 0.533843564422528 0.932312871154944 0.466156435577472 42 0.912058116301963 0.175883767396075 0.0879418836980374 43 0.876510066395119 0.246979867209763 0.123489933604881 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 1 0.0370370370370370 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555936451d7bqx7qp18ca/10acpg1258555889.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555936451d7bqx7qp18ca/10acpg1258555889.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555936451d7bqx7qp18ca/1zor21258555889.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555936451d7bqx7qp18ca/1zor21258555889.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555936451d7bqx7qp18ca/2mz461258555889.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555936451d7bqx7qp18ca/2mz461258555889.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555936451d7bqx7qp18ca/395n51258555889.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555936451d7bqx7qp18ca/395n51258555889.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555936451d7bqx7qp18ca/4zv6g1258555889.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555936451d7bqx7qp18ca/4zv6g1258555889.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555936451d7bqx7qp18ca/596xg1258555889.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555936451d7bqx7qp18ca/596xg1258555889.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555936451d7bqx7qp18ca/6hspz1258555889.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555936451d7bqx7qp18ca/6hspz1258555889.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555936451d7bqx7qp18ca/7sucn1258555889.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555936451d7bqx7qp18ca/7sucn1258555889.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555936451d7bqx7qp18ca/8azsd1258555889.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555936451d7bqx7qp18ca/8azsd1258555889.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555936451d7bqx7qp18ca/9qlw91258555889.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555936451d7bqx7qp18ca/9qlw91258555889.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