*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, 07 Dec 2010 12:10:30 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/07/t1291723968xuppmljhnfo4hqu.htm/, Retrieved Tue, 07 Dec 2010 13:12:59 +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/2010/Dec/07/t1291723968xuppmljhnfo4hqu.htm/}, year = {2010}, } @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 = {2010}, 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 «

112.3 0 117.3 0 111.1 1 102.2 1 104.3 1 122.9 1 107.6 1 121.3 1 131.5 1 89 1 104.4 1 128.9 1 135.9 1 133.3 1 121.3 1 120.5 0 120.4 0 137.9 0 126.1 0 133.2 0 151.1 0 105 0 119 0 140.4 0 156.6 0 137.1 0 122.7 0 125.8 0 139.3 0 134.9 0 149.2 0 132.3 0 149 0 117.2 0 119.6 0 152 0 149.4 0 127.3 0 114.1 0 102.1 0 107.7 0 104.4 0 102.1 0 96 1 109.3 0 90 1 83.9 1 112 1 114.3 1 103.6 1 91.7 1 80.8 1 87.2 1 109.2 1 102.7 1 95.1 1 117.5 1 85.1 1 92.1 1 113.5 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 7 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Promet[t] = + 126.945161290323 -20.4486095661847Dummy[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) 126.945161290323 2.88491 44.0032 0 0 Dummy -20.4486095661847 4.149626 -4.9278 7e-06 4e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.543248724887231 R-squared 0.295119177091602 Adjusted R-squared 0.282966059455251 F-TEST (value) 24.2834131884694 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 7.29177659353208e-06 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 16.0624999285474 Sum Squared Residuals 14964.2264293660 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 112.3 126.945161290323 -14.6451612903225 2 117.3 126.945161290323 -9.64516129032257 3 111.1 106.496551724138 4.60344827586206 4 102.2 106.496551724138 -4.29655172413793 5 104.3 106.496551724138 -2.19655172413793 6 122.9 106.496551724138 16.4034482758621 7 107.6 106.496551724138 1.10344827586206 8 121.3 106.496551724138 14.8034482758621 9 131.5 106.496551724138 25.0034482758621 10 89 106.496551724138 -17.4965517241379 11 104.4 106.496551724138 -2.09655172413792 12 128.9 106.496551724138 22.4034482758621 13 135.9 106.496551724138 29.4034482758621 14 133.3 106.496551724138 26.8034482758621 15 121.3 106.496551724138 14.8034482758621 16 120.5 126.945161290323 -6.44516129032259 17 120.4 126.945161290323 -6.54516129032258 18 137.9 126.945161290323 10.9548387096774 19 126.1 126.945161290323 -0.84516129032259 20 133.2 126.945161290323 6.2548387096774 21 151.1 126.945161290323 24.1548387096774 22 105 126.945161290323 -21.9451612903226 23 119 126.945161290323 -7.94516129032259 24 140.4 126.945161290323 13.4548387096774 25 156.6 126.945161290323 29.6548387096774 26 137.1 126.945161290323 10.1548387096774 27 122.7 126.945161290323 -4.24516129032258 28 125.8 126.945161290323 -1.14516129032259 29 139.3 126.945161290323 12.3548387096774 30 134.9 126.945161290323 7.95483870967742 31 149.2 126.945161290323 22.2548387096774 32 132.3 126.945161290323 5.35483870967743 33 149 126.945161290323 22.0548387096774 34 117.2 126.945161290323 -9.74516129032258 35 119.6 126.945161290323 -7.34516129032259 36 152 126.945161290323 25.0548387096774 37 149.4 126.945161290323 22.4548387096774 38 127.3 126.945161290323 0.354838709677412 39 114.1 126.945161290323 -12.8451612903226 40 102.1 126.945161290323 -24.8451612903226 41 107.7 126.945161290323 -19.2451612903226 42 104.4 126.945161290323 -22.5451612903226 43 102.1 126.945161290323 -24.8451612903226 44 96 106.496551724138 -10.4965517241379 45 109.3 126.945161290323 -17.6451612903226 46 90 106.496551724138 -16.4965517241379 47 83.9 106.496551724138 -22.5965517241379 48 112 106.496551724138 5.50344827586207 49 114.3 106.496551724138 7.80344827586207 50 103.6 106.496551724138 -2.89655172413794 51 91.7 106.496551724138 -14.7965517241379 52 80.8 106.496551724138 -25.6965517241379 53 87.2 106.496551724138 -19.2965517241379 54 109.2 106.496551724138 2.70344827586207 55 102.7 106.496551724138 -3.79655172413793 56 95.1 106.496551724138 -11.3965517241379 57 117.5 106.496551724138 11.0034482758621 58 85.1 106.496551724138 -21.3965517241379 59 92.1 106.496551724138 -14.3965517241379 60 113.5 106.496551724138 7.00344827586207 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0230807756484898 0.0461615512969796 0.97691922435151 6 0.0905806267377687 0.181161253475537 0.909419373262231 7 0.0383608436546544 0.0767216873093089 0.961639156345346 8 0.0359614548621042 0.0719229097242083 0.964038545137896 9 0.0930248926986203 0.186049785397241 0.90697510730138 10 0.222341035474555 0.444682070949110 0.777658964525445 11 0.157610888174344 0.315221776348688 0.842389111825656 12 0.199439281456319 0.398878562912638 0.800560718543681 13 0.336052073986973 0.672104147973946 0.663947926013027 14 0.427709223928365 0.85541844785673 0.572290776071635 15 0.385004046968793 0.770008093937586 0.614995953031207 16 0.311385440157566 0.622770880315133 0.688614559842434 17 0.243410787969288 0.486821575938576 0.756589212030712 18 0.266829653412278 0.533659306824556 0.733170346587722 19 0.204623831729636 0.409247663459272 0.795376168270364 20 0.170095883916747 0.340191767833495 0.829904116083253 21 0.289091557784065 0.57818311556813 0.710908442215935 22 0.367323929648787 0.734647859297573 0.632676070351213 23 0.309397081160769 0.618794162321538 0.690602918839231 24 0.305082153502036 0.610164307004071 0.694917846497964 25 0.519998041566075 0.96000391686785 0.480001958433925 26 0.474491957530377 0.948983915060754 0.525508042469623 27 0.406419407919283 0.812838815838566 0.593580592080717 28 0.334907484828507 0.669814969657013 0.665092515171493 29 0.311389956257161 0.622779912514321 0.68861004374284 30 0.265298690366661 0.530597380733322 0.734701309633339 31 0.353734057273741 0.707468114547482 0.646265942726259 32 0.302373615137733 0.604747230275467 0.697626384862267 33 0.427609212603286 0.855218425206572 0.572390787396714 34 0.384700599303244 0.769401198606489 0.615299400696756 35 0.330745477617996 0.661490955235991 0.669254522382004 36 0.590877778353696 0.818244443292609 0.409122221646304 37 0.880925836241047 0.238148327517906 0.119074163758953 38 0.908800211932195 0.182399576135611 0.0911997880678054 39 0.901226997101382 0.197546005797236 0.098773002898618 40 0.906648416699392 0.186703166601215 0.0933515833006077 41 0.891334113045205 0.217331773909591 0.108665886954795 42 0.87644982390424 0.24710035219152 0.12355017609576 43 0.865254266147261 0.269491467705477 0.134745733852739 44 0.8312464825277 0.337507034944602 0.168753517472301 45 0.784100456087555 0.43179908782489 0.215899543912445 46 0.765569849506269 0.468860300987462 0.234430150493731 47 0.803018769922026 0.393962460155948 0.196981230077974 48 0.771328082949654 0.457343834100691 0.228671917050346 49 0.770957896951003 0.458084206097994 0.229042103048997 50 0.692941824489683 0.614116351020634 0.307058175510317 51 0.616327676760239 0.767344646479522 0.383672323239761 52 0.689881373928263 0.620237252143474 0.310118626071737 53 0.680308998087868 0.639382003824263 0.319691001912132 54 0.566080043143866 0.867839913712268 0.433919956856134 55 0.397654283207018 0.795308566414036 0.602345716792982 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 1 0.0196078431372549 OK 10% type I error level 3 0.0588235294117647 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/07/t1291723968xuppmljhnfo4hqu/10a6651291723822.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t1291723968xuppmljhnfo4hqu/10a6651291723822.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t1291723968xuppmljhnfo4hqu/1lnrt1291723822.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t1291723968xuppmljhnfo4hqu/1lnrt1291723822.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t1291723968xuppmljhnfo4hqu/2wwqe1291723822.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t1291723968xuppmljhnfo4hqu/2wwqe1291723822.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t1291723968xuppmljhnfo4hqu/3wwqe1291723822.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t1291723968xuppmljhnfo4hqu/3wwqe1291723822.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t1291723968xuppmljhnfo4hqu/4wwqe1291723822.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t1291723968xuppmljhnfo4hqu/4wwqe1291723822.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t1291723968xuppmljhnfo4hqu/5wwqe1291723822.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t1291723968xuppmljhnfo4hqu/5wwqe1291723822.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t1291723968xuppmljhnfo4hqu/67n7h1291723822.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t1291723968xuppmljhnfo4hqu/67n7h1291723822.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t1291723968xuppmljhnfo4hqu/7hx6k1291723822.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t1291723968xuppmljhnfo4hqu/7hx6k1291723822.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t1291723968xuppmljhnfo4hqu/8hx6k1291723822.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t1291723968xuppmljhnfo4hqu/8hx6k1291723822.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t1291723968xuppmljhnfo4hqu/9hx6k1291723822.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t1291723968xuppmljhnfo4hqu/9hx6k1291723822.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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