monthly dummies

*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: Thu, 19 Nov 2009 15:00:07 -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/19/t12586680957gz823xab7bmv65.htm/, Retrieved Thu, 19 Nov 2009 23:01:46 +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/19/t12586680957gz823xab7bmv65.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 «

106.1 97.89 106 98.69 105.9 99.01 105.8 99.18 105.7 98.45 105.6 98.13 105.4 98.29 105.4 99.1 105.5 99.26 105.6 98.85 105.7 98.05 105.9 98.53 106.1 99.34 106 100.14 105.8 100.3 105.8 100.22 105.7 99.9 105.5 99.58 105.3 99.9 105.2 100.78 105.2 100.78 105 100.46 105.1 100.06 105.1 100.28 105.2 100.78 104.9 101.58 104.8 102.06 104.5 102.02 104.5 101.68 104.4 101.32 104.4 101.81 104.2 102.3 104.1 102.12 103.9 102.1 103.8 101.75 103.9 101.5 104.2 102.16 104.1 103.47 103.8 104.05 103.6 104.09 103.7 103.55 103.5 102.77 103.4 102.89 103.1 103.6 103.1 103.76 103.1 103.92 103.2 103.35 103.3 103.32 103.5 104.2 103.6 105.44 103.5 105.81 103.3 106.25 103.2 105.94 103.1 105.82 103.2 105.96 103 106.49 103 106.32 103.1 105.88 103.4 105.07

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Werkl[t] = + 143.973715807234 -0.390691631516334Infl[t] + 0.456911830344265M1[t] + 0.743696545545371M2[t] + 0.732940748784612M3[t] + 0.614354061725341M4[t] + 0.399324210806029M5[t] + 0.110861390829816M6[t] + 0.126971532182841M7[t] + 0.234204608140009M8[t] + 0.231860458350911M9[t] + 0.111377982258543M10[t] -0.0175673138100251M11[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) 143.973715807234 1.852528 77.7174 0 0 Infl -0.390691631516334 0.018279 -21.3736 0 0 M1 0.456911830344265 0.23101 1.9779 0.053949 0.026975 M2 0.743696545545371 0.23167 3.2102 0.00242 0.00121 M3 0.732940748784612 0.232301 3.1551 0.002827 0.001414 M4 0.614354061725341 0.232513 2.6422 0.011219 0.005609 M5 0.399324210806029 0.231726 1.7233 0.091558 0.045779 M6 0.110861390829816 0.231284 0.4793 0.633974 0.316987 M7 0.126971532182841 0.231546 0.5484 0.586094 0.293047 M8 0.234204608140009 0.232732 1.0063 0.319523 0.159761 M9 0.231860458350911 0.232719 0.9963 0.324311 0.162155 M10 0.111377982258543 0.232293 0.4795 0.633875 0.316937 M11 -0.0175673138100251 0.231414 -0.0759 0.939817 0.469909 Multiple Linear Regression - Regression Statistics Multiple R 0.95674117805331 R-squared 0.915353681782835 Adjusted R-squared 0.89327203355227 F-TEST (value) 41.4531411887921 F-TEST (DF numerator) 12 F-TEST (DF denominator) 46 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.344367616333890 Sum Squared Residuals 5.45509653825634 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 106.1 106.185823828444 -0.0858238284444918 2 106 106.160055238433 -0.160055238432842 3 105.9 106.024278119587 -0.124278119586847 4 105.8 105.839273855170 -0.0392738551698082 5 105.7 105.909448895257 -0.209448895257416 6 105.6 105.746007397366 -0.146007397366441 7 105.4 105.699606877677 -0.299606877676837 8 105.4 105.490379732106 -0.0903797321057792 9 105.5 105.425524921274 0.0744750787259314 10 105.6 105.465226014103 0.134773985896592 11 105.7 105.648834023248 0.051165976752103 12 105.9 105.47886935393 0.421130646069923 13 106.1 105.619320962746 0.480679037253878 14 106 105.593552372734 0.406447627265843 15 105.8 105.520285914931 0.279714085069211 16 105.8 105.432954558393 0.367045441607176 17 105.7 105.342946029559 0.357053970441269 18 105.5 105.179504531668 0.32049546833225 19 105.3 105.070593350936 0.229406649064451 20 105.2 104.834017791158 0.365982208841661 21 105.2 104.831673641369 0.36832635863076 22 105 104.836212487362 0.163787512637895 23 105.1 104.8635438439 0.236456156099927 24 105.1 104.795158998777 0.304841001223495 25 105.2 105.056725013363 0.143274986637406 26 104.9 105.030956423351 -0.130956423350632 27 104.8 104.832668643462 -0.0326686434620398 28 104.5 104.729709621663 -0.229709621663422 29 104.5 104.647514925460 -0.147514925459659 30 104.4 104.499701092829 -0.099701092829326 31 104.4 104.324372334739 0.075627665260656 32 104.2 104.240166511254 -0.0401665112535133 33 104.1 104.308146855137 -0.208146855137360 34 103.9 104.195478211675 -0.295478211675312 35 103.8 104.203274986637 -0.403274986637467 36 103.9 104.318515208327 -0.418515208326567 37 104.2 104.51757056187 -0.317570561870056 38 104.1 104.292549239785 -0.192549239784773 39 103.8 104.055192296745 -0.255192296744538 40 103.6 103.920977944425 -0.320977944424614 41 103.7 103.916921574524 -0.216921574524116 42 103.5 103.933198227131 -0.433198227130647 43 103.4 103.902425372702 -0.502425372701704 44 103.1 103.732267390282 -0.63226739028229 45 103.1 103.667412579451 -0.567412579450573 46 103.1 103.484419442316 -0.384419442315593 47 103.2 103.578168376211 -0.37816837621133 48 103.3 103.607456438967 -0.307456438966851 49 103.5 103.720559633577 -0.220559633576736 50 103.6 103.522886725698 0.0771132743024041 51 103.5 103.367575025276 0.132424974724214 52 103.3 103.077084020349 0.222915979650668 53 103.2 102.9831685752 0.216831424799922 54 103.1 102.741588751006 0.358411248994164 55 103.2 102.703002063947 0.496997936053434 56 103 102.6031685752 0.396831424799921 57 103 102.667242002769 0.332757997231242 58 103.1 102.718663844544 0.381336155456418 59 103.4 102.906178770003 0.493821229996766 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.00122413091995305 0.0024482618399061 0.998775869080047 17 0.000116250145224882 0.000232500290449764 0.999883749854775 18 3.10913699466135e-05 6.21827398932271e-05 0.999968908630053 19 5.11178346741122e-06 1.02235669348224e-05 0.999994888216533 20 7.55776291522252e-06 1.51155258304450e-05 0.999992442237085 21 4.00578488810076e-05 8.01156977620152e-05 0.999959942151119 22 0.00213163071082835 0.00426326142165671 0.997868369289172 23 0.00514288932324537 0.0102857786464907 0.994857110676755 24 0.039955158948939 0.079910317897878 0.96004484105106 25 0.133434120681572 0.266868241363145 0.866565879318428 26 0.265238930588497 0.530477861176994 0.734761069411503 27 0.312098352572307 0.624196705144614 0.687901647427693 28 0.422836399797084 0.845672799594167 0.577163600202916 29 0.426053116597359 0.852106233194717 0.573946883402641 30 0.439358905648440 0.878717811296881 0.56064109435156 31 0.483288581567829 0.966577163135657 0.516711418432171 32 0.642277132579275 0.715445734841449 0.357722867420725 33 0.830524476560002 0.338951046879996 0.169475523439998 34 0.896506947663126 0.206986104673747 0.103493052336874 35 0.897613163991576 0.204773672016848 0.102386836008424 36 0.945482212142274 0.109035575715452 0.0545177878577262 37 0.978092502759603 0.0438149944807933 0.0219074972403966 38 0.98036881550522 0.0392623689895602 0.0196311844947801 39 0.965362569962627 0.0692748600747455 0.0346374300373728 40 0.937566606392113 0.124866787215774 0.0624333936078872 41 0.960266256245741 0.0794674875085174 0.0397337437542587 42 0.972042089417532 0.0559158211649357 0.0279579105824679 43 0.945090469630732 0.109819060738536 0.0549095303692682 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 7 0.25 NOK 5% type I error level 10 0.357142857142857 NOK 10% type I error level 14 0.5 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586680957gz823xab7bmv65/1000sp1258668002.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586680957gz823xab7bmv65/1000sp1258668002.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586680957gz823xab7bmv65/1x78g1258668001.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586680957gz823xab7bmv65/1x78g1258668001.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586680957gz823xab7bmv65/2h6yn1258668001.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586680957gz823xab7bmv65/2h6yn1258668001.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586680957gz823xab7bmv65/3q0ks1258668001.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586680957gz823xab7bmv65/3q0ks1258668001.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586680957gz823xab7bmv65/4ycgz1258668001.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586680957gz823xab7bmv65/4ycgz1258668001.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586680957gz823xab7bmv65/518jb1258668001.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586680957gz823xab7bmv65/518jb1258668001.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586680957gz823xab7bmv65/6j3vp1258668001.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586680957gz823xab7bmv65/6j3vp1258668001.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586680957gz823xab7bmv65/7k0nd1258668001.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586680957gz823xab7bmv65/7k0nd1258668001.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586680957gz823xab7bmv65/8imqw1258668001.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586680957gz823xab7bmv65/8imqw1258668001.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586680957gz823xab7bmv65/9qoh41258668001.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586680957gz823xab7bmv65/9qoh41258668001.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by