WS 7.1

*Unverified author*

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

Title produced by software: Multiple Regression

Date of computation: Fri, 20 Nov 2009 06:15:39 -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/20/t1258723019w8d8djnjs9nvrjk.htm/, Retrieved Fri, 20 Nov 2009 14:17:11 +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/20/t1258723019w8d8djnjs9nvrjk.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 «

9.9 8.2 9.8 8 9.3 7.5 8.3 6.8 8 6.5 8.5 6.6 10.4 7.6 11.1 8 10.9 8.1 10 7.7 9.2 7.5 9.2 7.6 9.5 7.8 9.6 7.8 9.5 7.8 9.1 7.5 8.9 7.5 9 7.1 10.1 7.5 10.3 7.5 10.2 7.6 9.6 7.7 9.2 7.7 9.3 7.9 9.4 8.1 9.4 8.2 9.2 8.2 9 8.2 9 7.9 9 7.3 9.8 6.9 10 6.6 9.8 6.7 9.3 6.9 9 7 9 7.1 9.1 7.2 9.1 7.1 9.1 6.9 9.2 7 8.8 6.8 8.3 6.4 8.4 6.7 8.1 6.6 7.7 6.4 7.9 6.3 7.9 6.2 8 6.5 7.9 6.8 7.6 6.8 7.1 6.4 6.8 6.1 6.5 5.8 6.9 6.1 8.2 7.2 8.7 7.3 8.3 6.9 7.9 6.1 7.5 5.8 7.8 6.2

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 WLVrouw[t] = + 0.757256418655039 + 1.14003880168309WLMan[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) 0.757256418655039 0.877241 0.8632 0.391569 0.195785 WLMan 1.14003880168309 0.122392 9.3147 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.77417457022882 R-squared 0.599346265188978 Adjusted R-squared 0.592438442174995 F-TEST (value) 86.7634078023935 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 4.02788913334007e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.629565297392559 Sum Squared Residuals 22.9884428934969 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 9.9 10.1055745924563 -0.205574592456331 2 9.8 9.87756683211973 -0.0775668321197314 3 9.3 9.30754743127819 -0.00754743127818791 4 8.3 8.50952027010003 -0.209520270100027 5 8 8.1675086295951 -0.167508629595102 6 8.5 8.28151250976341 0.21848749023659 7 10.4 9.4215513114465 0.978448688553503 8 11.1 9.87756683211973 1.22243316788027 9 10.9 9.99157071228804 0.90842928771196 10 10 9.5355551916148 0.464444808385194 11 9.2 9.30754743127819 -0.107547431278189 12 9.2 9.4215513114465 -0.221551311446498 13 9.5 9.64955907178311 -0.149559071783114 14 9.6 9.64955907178311 -0.0495590717831148 15 9.5 9.64955907178311 -0.149559071783114 16 9.1 9.30754743127819 -0.207547431278189 17 8.9 9.30754743127819 -0.407547431278188 18 9 8.85153191060495 0.148468089395046 19 10.1 9.30754743127819 0.79245256872181 20 10.3 9.30754743127819 0.992452568721812 21 10.2 9.4215513114465 0.778448688553502 22 9.6 9.5355551916148 0.0644448083851935 23 9.2 9.5355551916148 -0.335555191614807 24 9.3 9.76356295195142 -0.463562951951423 25 9.4 9.99157071228804 -0.59157071228804 26 9.4 10.1055745924563 -0.705574592456348 27 9.2 10.1055745924563 -0.90557459245635 28 9 10.1055745924563 -1.10557459245635 29 9 9.76356295195142 -0.763562951951424 30 9 9.07953967094157 -0.079539670941571 31 9.8 8.62352415026834 1.17647584973166 32 10 8.28151250976341 1.71848749023659 33 9.8 8.39551638993172 1.40448361006828 34 9.3 8.62352415026834 0.676475849731664 35 9 8.73752803043664 0.262471969563355 36 9 8.85153191060495 0.148468089395046 37 9.1 8.96553579077326 0.134464209226737 38 9.1 8.85153191060495 0.248468089395046 39 9.1 8.62352415026834 0.476475849731663 40 9.2 8.73752803043664 0.462471969563354 41 8.8 8.50952027010003 0.290479729899973 42 8.3 8.0535047494268 0.246495250573207 43 8.4 8.39551638993172 0.00448361006828094 44 8.1 8.28151250976341 -0.181512509763411 45 7.7 8.0535047494268 -0.353504749426793 46 7.9 7.93950086925848 -0.0395008692584838 47 7.9 7.82549698909018 0.0745030109098244 48 8 8.1675086295951 -0.167508629595102 49 7.9 8.50952027010003 -0.609520270100027 50 7.6 8.50952027010003 -0.909520270100028 51 7.1 8.0535047494268 -0.953504749426794 52 6.8 7.71149310892187 -0.911493108921867 53 6.5 7.36948146841694 -0.869481468416941 54 6.9 7.71149310892187 -0.811493108921866 55 8.2 8.96553579077326 -0.765535790773264 56 8.7 9.07953967094157 -0.379539670941572 57 8.3 8.62352415026834 -0.323524150268336 58 7.9 7.71149310892187 0.188506891078134 59 7.5 7.36948146841694 0.130518531583059 60 7.8 7.82549698909018 -0.0254969890901761 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.00465075781167010 0.00930151562334021 0.99534924218833 6 0.00934018358019272 0.0186803671603854 0.990659816419807 7 0.227955057512367 0.455910115024733 0.772044942487633 8 0.451528593939032 0.903057187878063 0.548471406060968 9 0.421348306970608 0.842696613941215 0.578651693029392 10 0.322823651906066 0.645647303812131 0.677176348093934 11 0.265738467150519 0.531476934301039 0.73426153284948 12 0.237202810385708 0.474405620771416 0.762797189614292 13 0.201256647477508 0.402513294955016 0.798743352522492 14 0.153235029908178 0.306470059816356 0.846764970091822 15 0.120542127868378 0.241084255736756 0.879457872131622 16 0.0905155242451258 0.181031048490252 0.909484475754874 17 0.0805809465452186 0.161161893090437 0.919419053454781 18 0.0536239253888581 0.107247850777716 0.946376074611142 19 0.0696635411743914 0.139327082348783 0.930336458825609 20 0.122690387975791 0.245380775951583 0.877309612024209 21 0.141688378327632 0.283376756655263 0.858311621672368 22 0.106812980350975 0.213625960701951 0.893187019649025 23 0.096726565846077 0.193453131692154 0.903273434153923 24 0.0987577979707936 0.197515595941587 0.901242202029206 25 0.109542620716524 0.219085241433048 0.890457379283476 26 0.123911390308241 0.247822780616482 0.87608860969176 27 0.161854973477761 0.323709946955521 0.83814502652224 28 0.262598967517865 0.525197935035730 0.737401032482135 29 0.318393635278371 0.636787270556742 0.681606364721629 30 0.262921995754271 0.525843991508543 0.737078004245729 31 0.368879306084432 0.737758612168863 0.631120693915568 32 0.754791057724568 0.490417884550864 0.245208942275432 33 0.92997451733204 0.140050965335919 0.0700254826679595 34 0.93938529028265 0.121229419434701 0.0606147097173503 35 0.922490953850443 0.155018092299115 0.0775090461495574 36 0.896870423859167 0.206259152281666 0.103129576140833 37 0.86383488688215 0.2723302262357 0.13616511311785 38 0.83716579205301 0.32566841589398 0.16283420794699 39 0.853853971577844 0.292292056844313 0.146146028422156 40 0.886803720660501 0.226392558678997 0.113196279339499 41 0.904885857040587 0.190228285918827 0.0951141429594134 42 0.920120381820176 0.159759236359649 0.0798796181798243 43 0.91653495313061 0.166930093738778 0.0834650468693892 44 0.90093467111299 0.198130657774022 0.0990653288870108 45 0.879042385654834 0.241915228690332 0.120957614345166 46 0.86003950230578 0.27992099538844 0.13996049769422 47 0.857546217310632 0.284907565378736 0.142453782689368 48 0.828250930407895 0.34349813918421 0.171749069592105 49 0.775426438267226 0.449147123465547 0.224573561732773 50 0.763966309419469 0.472067381161062 0.236033690580531 51 0.781694144013219 0.436611711973562 0.218305855986781 52 0.805517984251325 0.38896403149735 0.194482015748675 53 0.887291866079329 0.225416267841343 0.112708133920672 54 0.989987915539383 0.0200241689212336 0.0100120844606168 55 0.99588512034877 0.00822975930246136 0.00411487965123068 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 2 0.0392156862745098 NOK 5% type I error level 4 0.0784313725490196 NOK 10% type I error level 4 0.0784313725490196 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723019w8d8djnjs9nvrjk/10fcxo1258722934.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723019w8d8djnjs9nvrjk/10fcxo1258722934.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723019w8d8djnjs9nvrjk/1pky31258722934.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723019w8d8djnjs9nvrjk/1pky31258722934.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723019w8d8djnjs9nvrjk/203ye1258722934.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723019w8d8djnjs9nvrjk/203ye1258722934.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723019w8d8djnjs9nvrjk/3szi41258722934.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723019w8d8djnjs9nvrjk/3szi41258722934.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723019w8d8djnjs9nvrjk/4u5cb1258722934.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723019w8d8djnjs9nvrjk/4u5cb1258722934.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723019w8d8djnjs9nvrjk/5zej91258722934.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723019w8d8djnjs9nvrjk/5zej91258722934.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723019w8d8djnjs9nvrjk/6wfl81258722934.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723019w8d8djnjs9nvrjk/6wfl81258722934.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723019w8d8djnjs9nvrjk/7vdsx1258722934.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723019w8d8djnjs9nvrjk/7vdsx1258722934.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723019w8d8djnjs9nvrjk/8g7141258722934.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723019w8d8djnjs9nvrjk/8g7141258722934.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723019w8d8djnjs9nvrjk/9j8k81258722934.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723019w8d8djnjs9nvrjk/9j8k81258722934.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