*The author of this computation has been verified*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Fri, 26 Nov 2010 13:22:56 +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/Nov/26/t1290777810bdreeum8tepv3v1.htm/, Retrieved Fri, 26 Nov 2010 14:23:30 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Nov/26/t1290777810bdreeum8tepv3v1.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 «

102.86 102.38 102.37 101.76 102.87 102.86 102.38 102.37 102.92 102.87 102.86 102.38 102.95 102.92 102.87 102.86 103.02 102.95 102.92 102.87 104.08 103.02 102.95 102.92 104.16 104.08 103.02 102.95 104.24 104.16 104.08 103.02 104.33 104.24 104.16 104.08 104.73 104.33 104.24 104.16 104.86 104.73 104.33 104.24 105.03 104.86 104.73 104.33 105.62 105.03 104.86 104.73 105.63 105.62 105.03 104.86 105.63 105.63 105.62 105.03 105.94 105.63 105.63 105.62 106.61 105.94 105.63 105.63 107.69 106.61 105.94 105.63 107.78 107.69 106.61 105.94 107.93 107.78 107.69 106.61 108.48 107.93 107.78 107.69 108.14 108.48 107.93 107.78 108.48 108.14 108.48 107.93 108.48 108.48 108.14 108.48 108.89 108.48 108.48 108.14 108.93 108.89 108.48 108.48 109.21 108.93 108.89 108.48 109.47 109.21 108.93 108.89 109.80 109.47 109.21 108.93 111.73 109.80 109.47 109.21 111.85 111.73 109.80 109.47 112.12 111.85 111.73 109.80 112.15 112.12 111.85 111.73 112.17 112.15 112.12 111.85 112.67 112.17 112.15 112.12 112.80 11 etc...

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 5 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Y1[t] = + 14.24777984831 + 0.757347311456523Y2[t] + 0.162331012991481Y3[t] -0.0605178371933684Y4[t] + 0.406078167562165M1[t] + 0.020950647807322M2[t] + 0.198187463583225M3[t] + 0.0855484377373725M4[t] + 0.238923020163983M5[t] + 1.22373482995670M6[t] + 0.319554519385565M7[t] + 0.018134861825965M8[t] + 0.148386559720362M9[t] -0.0214456339183264M10[t] + 0.210413993094089M11[t] + 0.0490649360196894t + 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) 14.24777984831 6.174673 2.3075 0.026424 0.013212 Y2 0.757347311456523 0.158309 4.784 2.5e-05 1.2e-05 Y3 0.162331012991481 0.202296 0.8024 0.427159 0.213579 Y4 -0.0605178371933684 0.154901 -0.3907 0.698155 0.349077 M1 0.406078167562165 0.166528 2.4385 0.019404 0.009702 M2 0.020950647807322 0.157001 0.1334 0.89453 0.447265 M3 0.198187463583225 0.174562 1.1353 0.263161 0.13158 M4 0.0855484377373725 0.153416 0.5576 0.580287 0.290144 M5 0.238923020163983 0.162506 1.4702 0.149518 0.074759 M6 1.22373482995670 0.155792 7.8549 0 0 M7 0.319554519385565 0.228585 1.398 0.170023 0.085012 M8 0.018134861825965 0.270897 0.0669 0.946968 0.473484 M9 0.148386559720362 0.168844 0.8788 0.384871 0.192436 M10 -0.0214456339183264 0.164159 -0.1306 0.896732 0.448366 M11 0.210413993094089 0.168737 1.247 0.219836 0.109918 t 0.0490649360196894 0.020315 2.4153 0.020511 0.010255 Multiple Linear Regression - Regression Statistics Multiple R 0.999350423129952 R-squared 0.998701268210013 Adjusted R-squared 0.998201755983095 F-TEST (value) 1999.35299756734 F-TEST (DF numerator) 15 F-TEST (DF denominator) 39 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.228410216986259 Sum Squared Residuals 2.03467786172468 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 102.86 102.699671385951 0.160328614048532 2 102.87 102.691842941157 0.178157058842593 3 102.92 103.003031873932 -0.0830318739315512 4 102.95 102.949899897955 0.000100102044690802 5 103.02 103.182571208023 -0.162571208022951 6 104.08 104.271306304167 -0.191306304167381 7 104.16 104.228526715553 -0.0685267155534581 8 104.24 104.204594404098 0.035405595902495 9 104.33 104.393336396542 -0.0633363965424561 10 104.73 104.348875451018 0.38112454898161 11 104.86 104.942507302827 -0.0825073028268773 12 105.03 104.939099196091 0.090900803908989 13 105.62 105.519887239432 0.100112760567984 14 105.63 105.650388522830 -0.0203885228296381 15 105.63 105.969751013082 -0.339751013081891 16 105.94 105.872094709442 0.0679052905584495 17 106.61 106.308706716067 0.301293283932561 18 107.69 107.900328774583 -0.210328774583075 19 107.78 107.953149745579 -0.173149745579023 20 107.93 107.903726825181 0.0262731748185617 21 108.48 108.145896082814 0.334103917185593 22 108.14 108.460572893098 -0.320572893097816 23 108.48 108.564203751801 -0.0842037518010056 24 108.48 108.571875425748 -0.0918754257483703 25 108.89 109.102787138393 -0.212787138393077 26 108.93 109.056660887709 -0.126660887709344 27 109.21 109.379812247290 -0.169812247289722 28 109.47 109.509976331942 -0.0399763319417501 29 109.8 109.952358121517 -0.152358121516629 30 111.73 111.261420549073 0.46857945092668 31 111.85 111.90582008225 -0.0558200822498957 32 112.12 112.037675006884 0.0823249931155014 33 112.15 112.324155710668 -0.174155710667628 34 112.17 112.262676105437 -0.0926761054368271 35 112.67 112.547277729046 0.122722270954406 36 112.8 112.766033412843 0.0339665871565115 37 113.44 113.399586816667 0.04041318333344 38 113.53 113.539070625356 -0.00907062535578622 39 114.53 113.929558164662 0.600441835338121 40 114.51 114.599209761658 -0.0892097616577136 41 115.05 114.943386741519 0.106613258481029 42 116.67 116.322466578065 0.34753342193531 43 117.07 116.783122951832 0.286877048167907 44 116.92 117.064003763837 -0.144003763836558 45 117 117.096611809976 -0.096611809975509 46 117.02 116.987875550447 0.0321244495530331 47 117.35 117.306011216327 0.0439887836734767 48 117.36 117.392991965317 -0.0329919653171300 49 117.82 117.908067419557 -0.0880674195568793 50 117.88 117.902037022948 -0.0220370229478243 51 118.24 118.247846701035 -0.00784670103495664 52 118.5 118.438819299004 0.0611807009963234 53 118.8 118.892977212874 -0.0929772128740098 54 119.76 120.174477794112 -0.414477794111534 55 120.09 120.079380504786 0.0106194952144697 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.475405888342108 0.950811776684217 0.524594111657892 20 0.306829624036283 0.613659248072567 0.693170375963717 21 0.304606135715391 0.609212271430782 0.695393864284609 22 0.702000311522285 0.59599937695543 0.297999688477715 23 0.633023403227338 0.733953193545325 0.366976596772663 24 0.517716283864379 0.964567432271242 0.482283716135621 25 0.493490293887498 0.986980587774996 0.506509706112502 26 0.406301773605282 0.812603547210564 0.593698226394718 27 0.572727379139901 0.854545241720197 0.427272620860099 28 0.645913318614289 0.708173362771423 0.354086681385711 29 0.904395073885683 0.191209852228634 0.0956049261143168 30 0.968346810328757 0.0633063793424853 0.0316531896712426 31 0.96063555510027 0.078728889799459 0.0393644448997295 32 0.942410451436532 0.115179097126936 0.057589548563468 33 0.893444089408526 0.213111821182948 0.106555910591474 34 0.803777894176996 0.392444211646009 0.196222105823004 35 0.732308647731969 0.535382704536062 0.267691352268031 36 0.567518398049462 0.864963203901077 0.432481601950538 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 2 0.111111111111111 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290777810bdreeum8tepv3v1/10f3jw1290777767.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290777810bdreeum8tepv3v1/10f3jw1290777767.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290777810bdreeum8tepv3v1/1qknl1290777767.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290777810bdreeum8tepv3v1/1qknl1290777767.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290777810bdreeum8tepv3v1/2qknl1290777767.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290777810bdreeum8tepv3v1/2qknl1290777767.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290777810bdreeum8tepv3v1/31t4n1290777767.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290777810bdreeum8tepv3v1/31t4n1290777767.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290777810bdreeum8tepv3v1/41t4n1290777767.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290777810bdreeum8tepv3v1/41t4n1290777767.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290777810bdreeum8tepv3v1/51t4n1290777767.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290777810bdreeum8tepv3v1/51t4n1290777767.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290777810bdreeum8tepv3v1/6u3381290777767.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290777810bdreeum8tepv3v1/6u3381290777767.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290777810bdreeum8tepv3v1/74ckt1290777767.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290777810bdreeum8tepv3v1/74ckt1290777767.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290777810bdreeum8tepv3v1/84ckt1290777767.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290777810bdreeum8tepv3v1/84ckt1290777767.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290777810bdreeum8tepv3v1/9f3jw1290777767.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290777810bdreeum8tepv3v1/9f3jw1290777767.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