*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 10:08:15 -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/t1258564913ohm5v8m8pge0fv6.htm/, Retrieved Wed, 18 Nov 2009 18:22:06 +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/t1258564913ohm5v8m8pge0fv6.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 «

24 24 22 23 25 24 24 24 29 27 26 28 26 25 21 19 23 19 22 19 21 20 16 16 19 22 16 21 25 25 27 29 23 28 22 25 23 26 20 24 24 28 23 28 20 28 21 28 22 32 17 31 21 22 19 29 23 31 22 29 15 32 23 32 21 31 18 29 18 28 18 28 18 29 10 22 13 26 10 24 9 27 9 27 6 23 11 21 9 19 10 17 9 19 16 21 10 13 7 8 7 5 14 10 11 6 10 6 6 8 8 11 13 12 12 13 15 19 16 19 16 18

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation s[t] = + 5.58678911521391 + 0.525575434025019consv[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) 5.58678911521391 2.079406 2.6867 0.009359 0.00468 consv 0.525575434025019 0.089021 5.9039 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.609410364300489 R-squared 0.371380992116854 Adjusted R-squared 0.360726432661208 F-TEST (value) 34.8565319535606 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 1.86578364536061e-07 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.95273565874902 Sum Squared Residuals 1447.2458398212 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 24 18.2005995318144 5.79940046818562 2 22 17.6750240977894 4.32497590221065 3 25 18.2005995318144 6.79940046818562 4 24 18.2005995318144 5.79940046818562 5 29 19.7773258338894 9.22267416611056 6 26 20.3029012679145 5.69709873208555 7 26 18.7261749658394 7.2738250341606 8 21 15.5727223616893 5.42727763831072 9 23 15.5727223616893 7.42727763831072 10 22 15.5727223616893 6.42727763831072 11 21 16.0982977957143 4.9017022042857 12 16 13.9959960596142 2.00400394038578 13 19 17.1494486637643 1.85055133623566 14 16 16.6238732297393 -0.62387322973932 15 25 18.7261749658394 6.2738250341606 16 27 20.8284767019395 6.17152329806053 17 23 20.3029012679145 2.69709873208555 18 22 18.7261749658394 3.2738250341606 19 23 19.2517503998644 3.74824960013558 20 20 18.2005995318144 1.79940046818562 21 24 20.3029012679145 3.69709873208555 22 23 20.3029012679145 2.69709873208555 23 20 20.3029012679145 -0.302901267914455 24 21 20.3029012679145 0.697098732085545 25 22 22.4052030040145 -0.405203004014532 26 17 21.8796275699895 -4.87962756998951 27 21 17.1494486637643 3.85055133623566 28 19 20.8284767019395 -1.82847670193947 29 23 21.8796275699895 1.12037243001049 30 22 20.8284767019395 1.17152329806053 31 15 22.4052030040145 -7.40520300401453 32 23 22.4052030040145 0.594796995985468 33 21 21.8796275699895 -0.879627569989513 34 18 20.8284767019395 -2.82847670193947 35 18 20.3029012679145 -2.30290126791445 36 18 20.3029012679145 -2.30290126791445 37 18 20.8284767019395 -2.82847670193947 38 10 17.1494486637643 -7.14944866376434 39 13 19.2517503998644 -6.25175039986442 40 10 18.2005995318144 -8.20059953181438 41 9 19.7773258338894 -10.7773258338894 42 9 19.7773258338894 -10.7773258338894 43 6 17.6750240977894 -11.6750240977894 44 11 16.6238732297393 -5.62387322973932 45 9 15.5727223616893 -6.57272236168928 46 10 14.5215714936392 -4.52157149363924 47 9 15.5727223616893 -6.57272236168928 48 16 16.6238732297393 -0.62387322973932 49 10 12.4192697575392 -2.41926975753917 50 7 9.79139258741407 -2.79139258741407 51 7 8.21466628533901 -1.21466628533901 52 14 10.8425434554641 3.15745654453589 53 11 8.74024171936403 2.25975828063597 54 10 8.74024171936403 1.25975828063597 55 6 9.79139258741407 -3.79139258741407 56 8 11.3681188894891 -3.36811888948913 57 13 11.8936943235141 1.10630567648585 58 12 12.4192697575392 -0.419269757539166 59 15 15.5727223616893 -0.572722361689281 60 16 15.5727223616893 0.427277638310719 61 16 15.0471469276643 0.952853072335738 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.00258985070115058 0.00517970140230116 0.99741014929885 6 0.022585218327969 0.045170436655938 0.97741478167203 7 0.00873910104551276 0.0174782020910255 0.991260898954487 8 0.00323086283292124 0.00646172566584247 0.996769137167079 9 0.00268462936029552 0.00536925872059105 0.997315370639704 10 0.00102690921600391 0.00205381843200783 0.998973090783996 11 0.000480258811749379 0.000960517623498758 0.99951974118825 12 0.000767625705381711 0.00153525141076342 0.999232374294618 13 0.00238389170237270 0.00476778340474541 0.997616108297627 14 0.0146149538980228 0.0292299077960457 0.985385046101977 15 0.0103378756758973 0.0206757513517945 0.989662124324103 16 0.00847266294876217 0.0169453258975243 0.991527337051238 17 0.0124208554488218 0.0248417108976436 0.987579144551178 18 0.0108573879881363 0.0217147759762726 0.989142612011864 19 0.00942853146272893 0.0188570629254579 0.990571468537271 20 0.0106002100570692 0.0212004201141383 0.98939978994293 21 0.0103369413443679 0.0206738826887359 0.989663058655632 22 0.0113307858294539 0.0226615716589078 0.988669214170546 23 0.0230972766549484 0.0461945533098967 0.976902723345052 24 0.0279537331874857 0.0559074663749715 0.972046266812514 25 0.0348664282312385 0.0697328564624769 0.965133571768761 26 0.0995386412528455 0.199077282505691 0.900461358747154 27 0.110302989587535 0.220605979175069 0.889697010412465 28 0.118533529929284 0.237067059858568 0.881466470070716 29 0.122844123153313 0.245688246306625 0.877155876846687 30 0.136831066192921 0.273662132385842 0.863168933807079 31 0.262523012185024 0.525046024370047 0.737476987814976 32 0.30879764676603 0.61759529353206 0.69120235323397 33 0.344972137851938 0.689944275703877 0.655027862148062 34 0.375442847073178 0.750885694146356 0.624557152926822 35 0.421432559674535 0.84286511934907 0.578567440325465 36 0.491083933640069 0.982167867280139 0.508916066359931 37 0.59926872010119 0.80146255979762 0.40073127989881 38 0.784972503055719 0.430054993888562 0.215027496944281 39 0.820308089403473 0.359383821193053 0.179691910596527 40 0.878349466892344 0.243301066215312 0.121650533107656 41 0.93037395042875 0.139252099142501 0.0696260495712507 42 0.960276281496772 0.079447437006455 0.0397237185032275 43 0.995174818519763 0.00965036296047376 0.00482518148023688 44 0.994693280657404 0.0106134386851924 0.0053067193425962 45 0.996967206758925 0.00606558648214971 0.00303279324107485 46 0.996690979649488 0.00661804070102471 0.00330902035051236 47 0.999314505837432 0.00137098832513696 0.000685494162568478 48 0.99823797331751 0.00352405336497821 0.00176202668248911 49 0.996980287031592 0.00603942593681514 0.00301971296840757 50 0.995537720515136 0.00892455896972756 0.00446227948486378 51 0.990110566837715 0.0197788663245690 0.00988943316228452 52 0.98913310804008 0.0217337839198409 0.0108668919599205 53 0.987894575624643 0.0242108487507133 0.0121054243753566 54 0.992686432222124 0.0146271355557514 0.0073135677778757 55 0.982048619827077 0.0359027603458452 0.0179513801729226 56 0.99344077611317 0.0131184477736595 0.00655922388682976 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 14 0.269230769230769 NOK 5% type I error level 33 0.634615384615385 NOK 10% type I error level 36 0.692307692307692 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564913ohm5v8m8pge0fv6/103cid1258564090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564913ohm5v8m8pge0fv6/103cid1258564090.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564913ohm5v8m8pge0fv6/1mk261258564090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564913ohm5v8m8pge0fv6/1mk261258564090.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564913ohm5v8m8pge0fv6/2seea1258564090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564913ohm5v8m8pge0fv6/2seea1258564090.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564913ohm5v8m8pge0fv6/3t9d11258564090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564913ohm5v8m8pge0fv6/3t9d11258564090.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564913ohm5v8m8pge0fv6/4wz5o1258564090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564913ohm5v8m8pge0fv6/4wz5o1258564090.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564913ohm5v8m8pge0fv6/5r88q1258564090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564913ohm5v8m8pge0fv6/5r88q1258564090.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564913ohm5v8m8pge0fv6/6aihd1258564090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564913ohm5v8m8pge0fv6/6aihd1258564090.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564913ohm5v8m8pge0fv6/75qm21258564090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564913ohm5v8m8pge0fv6/75qm21258564090.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564913ohm5v8m8pge0fv6/8eux51258564090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564913ohm5v8m8pge0fv6/8eux51258564090.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564913ohm5v8m8pge0fv6/9q61f1258564090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564913ohm5v8m8pge0fv6/9q61f1258564090.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