WS8

*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, 01 Dec 2010 15:54:43 +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/01/t1291218772kviepxyr0fh57aw.htm/, Retrieved Wed, 01 Dec 2010 16:53:05 +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/01/t1291218772kviepxyr0fh57aw.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 7 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Y1[t] = + 14.2477798483089 + 0.757347311456524Y2[t] + 0.162331012991509Y3[t] -0.0605178371933855Y4[t] + 0.406078167562174M1[t] + 0.0209506478073179M2[t] + 0.198187463583214M3[t] + 0.085548437737373M4[t] + 0.238923020163978M5[t] + 1.22373482995670M6[t] + 0.319554519385561M7[t] + 0.0181348618259313M8[t] + 0.148386559720352M9[t] -0.0214456339183326M10[t] + 0.210413993094083M11[t] + 0.0490649360196856t + 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) 14.2477798483089 6.174673 2.3075 0.026424 0.013212 Y2 0.757347311456524 0.158309 4.784 2.5e-05 1.2e-05 Y3 0.162331012991509 0.202296 0.8024 0.427159 0.213579 Y4 -0.0605178371933855 0.154901 -0.3907 0.698155 0.349077 M1 0.406078167562174 0.166528 2.4385 0.019404 0.009702 M2 0.0209506478073179 0.157001 0.1334 0.89453 0.447265 M3 0.198187463583214 0.174562 1.1353 0.263161 0.13158 M4 0.085548437737373 0.153416 0.5576 0.580287 0.290144 M5 0.238923020163978 0.162506 1.4702 0.149518 0.074759 M6 1.22373482995670 0.155792 7.8549 0 0 M7 0.319554519385561 0.228585 1.398 0.170023 0.085012 M8 0.0181348618259313 0.270897 0.0669 0.946968 0.473484 M9 0.148386559720352 0.168844 0.8788 0.384871 0.192436 M10 -0.0214456339183326 0.164159 -0.1306 0.896732 0.448366 M11 0.210413993094083 0.168737 1.247 0.219836 0.109918 t 0.0490649360196856 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) 1999.35299756731 F-TEST (DF numerator) 15 F-TEST (DF denominator) 39 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.228410216986260 Sum Squared Residuals 2.03467786172471 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 102.86 102.699671385951 0.160328614048589 2 102.87 102.691842941157 0.178157058842567 3 102.92 103.003031873932 -0.083031873931566 4 102.95 102.949899897955 0.000100102044675913 5 103.02 103.182571208023 -0.162571208022959 6 104.08 104.271306304167 -0.191306304167390 7 104.16 104.228526715553 -0.0685267155534605 8 104.24 104.204594404097 0.035405595902498 9 104.33 104.393336396542 -0.0633363965424573 10 104.73 104.348875451018 0.381124548981608 11 104.86 104.942507302827 -0.082507302826877 12 105.03 104.939099196091 0.0909008039089777 13 105.62 105.519887239432 0.100112760567971 14 105.63 105.650388522830 -0.0203885228296365 15 105.63 105.969751013082 -0.339751013081892 16 105.94 105.872094709442 0.0679052905584503 17 106.61 106.308706716067 0.301293283932571 18 107.69 107.900328774583 -0.210328774583076 19 107.78 107.953149745579 -0.173149745579029 20 107.93 107.903726825181 0.0262731748185706 21 108.48 108.145896082814 0.334103917185598 22 108.14 108.460572893098 -0.320572893097814 23 108.48 108.564203751801 -0.0842037518010124 24 108.48 108.571875425748 -0.0918754257483607 25 108.89 109.102787138393 -0.212787138393088 26 108.93 109.056660887709 -0.126660887709332 27 109.21 109.379812247290 -0.169812247289711 28 109.47 109.509976331942 -0.0399763319417409 29 109.8 109.952358121517 -0.152358121516618 30 111.73 111.261420549073 0.468579450926687 31 111.85 111.905820082250 -0.0558200822498856 32 112.12 112.037675006885 0.082324993115497 33 112.15 112.324155710668 -0.174155710667622 34 112.17 112.262676105437 -0.0926761054368275 35 112.67 112.547277729046 0.122722270954414 36 112.8 112.766033412843 0.0339665871565164 37 113.44 113.399586816667 0.0404131833334258 38 113.53 113.539070625356 -0.00907062535577818 39 114.53 113.929558164662 0.600441835338124 40 114.51 114.599209761658 -0.0892097616577106 41 115.05 114.943386741519 0.106613258481015 42 116.67 116.322466578065 0.347533421935313 43 117.07 116.783122951832 0.286877048167902 44 116.92 117.064003763837 -0.144003763836566 45 117 117.096611809976 -0.096611809975519 46 117.02 116.987875550447 0.0321244495530341 47 117.35 117.306011216327 0.0439887836734760 48 117.36 117.392991965317 -0.032991965317133 49 117.82 117.908067419557 -0.0880674195568973 50 117.88 117.902037022948 -0.0220370229478196 51 118.24 118.247846701035 -0.00784670103495437 52 118.5 118.438819299004 0.0611807009963253 53 118.8 118.892977212874 -0.0929772128740093 54 119.76 120.174477794112 -0.414477794111534 55 120.09 120.079380504786 0.0106194952144724 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.475405888342119 0.950811776684237 0.524594111657881 20 0.306829624036266 0.613659248072532 0.693170375963734 21 0.304606135715442 0.609212271430884 0.695393864284558 22 0.70200031152231 0.59599937695538 0.29799968847769 23 0.633023403227357 0.733953193545287 0.366976596772644 24 0.517716283864392 0.964567432271216 0.482283716135608 25 0.493490293887477 0.986980587774953 0.506509706112523 26 0.406301773605289 0.812603547210577 0.593698226394711 27 0.572727379139911 0.854545241720179 0.427272620860089 28 0.645913318614288 0.708173362771425 0.354086681385712 29 0.904395073885735 0.191209852228530 0.0956049261142649 30 0.96834681032877 0.06330637934246 0.03165318967123 31 0.96063555510026 0.0787288897994798 0.0393644448997399 32 0.942410451436538 0.115179097126924 0.0575895485634621 33 0.893444089408515 0.21311182118297 0.106555910591485 34 0.803777894176978 0.392444211646045 0.196222105823022 35 0.732308647731984 0.535382704536032 0.267691352268016 36 0.567518398049428 0.864963203901145 0.432481601950572 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/Dec/01/t1291218772kviepxyr0fh57aw/10w6h81291218875.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218772kviepxyr0fh57aw/10w6h81291218875.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218772kviepxyr0fh57aw/1ie101291218875.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218772kviepxyr0fh57aw/1ie101291218875.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218772kviepxyr0fh57aw/2ie101291218875.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218772kviepxyr0fh57aw/2ie101291218875.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218772kviepxyr0fh57aw/3ie101291218875.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218772kviepxyr0fh57aw/3ie101291218875.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218772kviepxyr0fh57aw/4t50l1291218875.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218772kviepxyr0fh57aw/4t50l1291218875.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218772kviepxyr0fh57aw/5t50l1291218875.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218772kviepxyr0fh57aw/5t50l1291218875.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218772kviepxyr0fh57aw/6t50l1291218875.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218772kviepxyr0fh57aw/6t50l1291218875.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218772kviepxyr0fh57aw/7mfz61291218875.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218772kviepxyr0fh57aw/7mfz61291218875.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218772kviepxyr0fh57aw/8w6h81291218875.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218772kviepxyr0fh57aw/8w6h81291218875.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218772kviepxyr0fh57aw/9w6h81291218875.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218772kviepxyr0fh57aw/9w6h81291218875.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