Multiple Regression werklh ecogr 3 vertragingen

*The author of this computation has been verified*

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

Title produced by software: Multiple Regression

Date of computation: Thu, 17 Dec 2009 07:35:30 -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/Dec/17/t1261060606acat0c79mu3yrlw.htm/, Retrieved Thu, 17 Dec 2009 15:36:58 +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/Dec/17/t1261060606acat0c79mu3yrlw.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 «

8.2 103.9 8.7 9.3 9.3 8.3 101.6 8.2 8.7 9.3 8.5 94.6 8.3 8.2 8.7 8.6 95.9 8.5 8.3 8.2 8.5 104.7 8.6 8.5 8.3 8.2 102.8 8.5 8.6 8.5 8.1 98.1 8.2 8.5 8.6 7.9 113.9 8.1 8.2 8.5 8.6 80.9 7.9 8.1 8.2 8.7 95.7 8.6 7.9 8.1 8.7 113.2 8.7 8.6 7.9 8.5 105.9 8.7 8.7 8.6 8.4 108.8 8.5 8.7 8.7 8.5 102.3 8.4 8.5 8.7 8.7 99 8.5 8.4 8.5 8.7 100.7 8.7 8.5 8.4 8.6 115.5 8.7 8.7 8.5 8.5 100.7 8.6 8.7 8.7 8.3 109.9 8.5 8.6 8.7 8 114.6 8.3 8.5 8.6 8.2 85.4 8 8.3 8.5 8.1 100.5 8.2 8 8.3 8.1 114.8 8.1 8.2 8 8 116.5 8.1 8.1 8.2 7.9 112.9 8 8.1 8.1 7.9 102 7.9 8 8.1 8 106 7.9 7.9 8 8 105.3 8 7.9 7.9 7.9 118.8 8 8 7.9 8 106.1 7.9 8 8 7.7 109.3 8 7.9 8 7.2 117.2 7.7 8 7.9 7.5 92.5 7.2 7.7 8 7.3 104.2 7.5 7.2 7.7 7 112.5 7.3 7.5 7.2 7 122.4 7 7.3 7.5 7 113.3 7 7 7.3 7.2 100 7 7 7 7.3 110.7 7.2 7 7 7.1 112.8 7.3 7.2 7 6.8 109.8 7.1 7.3 7.2 6.4 117.3 6.8 7.1 7.3 6.1 109.1 6.4 6.8 7.1 6.5 115.9 6.1 6.4 6.8 7.7 96 6.5 6.1 6.4 7.9 99.8 7.7 6.5 6.1 7.5 116.8 7.9 7.7 6.5 6.9 115.7 7.5 7.9 7.7 6.6 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 2.47606172468974 -0.0101296723117681X[t] + 1.60828180239800Y1[t] -1.17499297834727Y2[t] + 0.403742817331000Y3[t] -0.0323317391383595M1[t] + 0.0603188530954501M2[t] + 0.00766841204588546M3[t] -0.139875510891479M4[t] + 0.0415646067147772M5[t] -0.0899349877552565M6[t] -0.186943880017435M7[t] + 0.0422117435785372M8[t] + 0.345355463489295M9[t] -0.565778112424411M10[t] + 0.173536403281812M11[t] -0.00415623384772254t + 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) 2.47606172468974 0.926371 2.6729 0.010832 0.005416 X -0.0101296723117681 0.004085 -2.4798 0.017457 0.008728 Y1 1.60828180239800 0.130647 12.3102 0 0 Y2 -1.17499297834727 0.205054 -5.7302 1e-06 1e-06 Y3 0.403742817331000 0.131816 3.0629 0.00391 0.001955 M1 -0.0323317391383595 0.114051 -0.2835 0.778267 0.389133 M2 0.0603188530954501 0.130854 0.461 0.647322 0.323661 M3 0.00766841204588546 0.135075 0.0568 0.95501 0.477505 M4 -0.139875510891479 0.130083 -1.0753 0.288694 0.144347 M5 0.0415646067147772 0.113909 0.3649 0.717113 0.358557 M6 -0.0899349877552565 0.11612 -0.7745 0.443191 0.221595 M7 -0.186943880017435 0.118169 -1.582 0.121525 0.060763 M8 0.0422117435785372 0.110943 0.3805 0.705602 0.352801 M9 0.345355463489295 0.163829 2.108 0.041342 0.020671 M10 -0.565778112424411 0.161505 -3.5032 0.001147 0.000574 M11 0.173536403281812 0.133558 1.2993 0.20127 0.100635 t -0.00415623384772254 0.002591 -1.604 0.116588 0.058294 Multiple Linear Regression - Regression Statistics Multiple R 0.978546142626844 R-squared 0.957552553249876 Adjusted R-squared 0.940573574549827 F-TEST (value) 56.3963575292716 F-TEST (DF numerator) 16 F-TEST (DF denominator) 40 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.161239934390964 Sum Squared Residuals 1.03993265769610 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.2 8.2065259819223 -0.00652598192229426 2 8.3 8.21917347243481 0.080826527565189 3 8.5 8.73935348273474 -0.239353482734742 4 8.6 8.57677040592372 0.0232295940762796 5 8.5 8.63111703964214 -0.131117039642143 6 8.2 8.31712867410842 -0.117128674108420 7 8.1 7.93896204671225 0.161037953287747 8 7.9 8.15520804546585 -0.255208045465847 9 8.6 8.46319480997306 0.136805190026943 10 8.7 8.71840742561242 -0.0184074256124160 11 8.7 8.53388097394549 0.166119026054513 12 8.5 8.59525561898883 -0.0952556189888331 13 8.4 8.24810951755212 0.151890482447877 14 8.5 8.47661716139436 0.0233828386056436 15 8.7 8.65081731973423 0.0491826802657688 16 8.7 8.6456795009309 0.0543204990690887 17 8.6 8.47841992053892 0.121580079461076 18 8.5 8.41260362566173 0.0873963743382644 19 8.3 8.1749166318785 0.125083368121506 20 8 8.10777521738346 -0.107775217383461 21 8.2 8.41468890816708 -0.214688908167076 22 8.1 7.93984673701553 0.160153262984470 23 8.1 8.01320108370719 0.086798916292807 24 8 8.01653586494858 -0.0165358649485785 25 7.9 7.81531225031196 0.0846877496880385 26 7.9 7.97089115449125 -0.0708911544912477 27 8 7.95069080644851 0.0493091935514851 28 8 7.92653531878836 0.0734646812116348 29 7.9 7.8495693285033 0.0504306714966971 30 8 7.7221064400383 0.277893559961698 31 7.7 7.86685384060527 -0.166853840605269 32 7.2 7.37147069880332 -0.171470698803323 33 7.5 7.50939236500531 -0.0093923650053092 34 7.3 7.42444357388993 -0.124443573889929 35 7 7.19949991291147 -0.199499912911472 36 7 6.79516042004479 0.204839579955214 37 7 7.12260179513377 -0.122601795133774 38 7.2 7.22469795006708 -0.0246979500670767 39 7.3 7.38116014191347 -0.0811601419134722 40 7.1 7.13401725784402 -0.034017257844018 41 6.8 6.98328306368973 -0.183283063689729 42 6.4 6.56454302971686 -0.164543029716863 43 6.1 6.17487782564224 -0.0748778256422407 44 6.5 6.19738524909067 0.302614750909328 45 7.7 7.53226670168888 0.167733298311124 46 7.9 7.91730226348213 -0.0173022634821257 47 7.5 7.55341802943585 -0.0534180294358478 48 6.9 6.9930480960178 -0.0930480960178024 49 6.6 6.70745045507985 -0.107450455079847 50 6.9 6.90862026161251 -0.00862026161250823 51 7.7 7.47797824916904 0.222021750830960 52 8 8.11699751651298 -0.116997516512985 53 8 7.8576106476259 0.142389352374098 54 7.7 7.78361823047468 -0.0836182304746782 55 7.3 7.34438965516174 -0.0443896551617431 56 7.4 7.1681607892567 0.231839210743302 57 8.1 8.18045721516568 -0.080457215165682 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 20 0.0579671292348971 0.115934258469794 0.942032870765103 21 0.656550946909541 0.686898106180918 0.343449053090459 22 0.5740736420742 0.851852715851599 0.425926357925799 23 0.469739568932071 0.939479137864142 0.530260431067929 24 0.365214345002387 0.730428690004774 0.634785654997613 25 0.305243624721757 0.610487249443513 0.694756375278243 26 0.295402319032801 0.590804638065603 0.704597680967199 27 0.19818384174624 0.39636768349248 0.80181615825376 28 0.144332065156976 0.288664130313952 0.855667934843024 29 0.103772431179154 0.207544862358308 0.896227568820846 30 0.349820481539287 0.699640963078574 0.650179518460713 31 0.407770913215974 0.81554182643195 0.592229086784026 32 0.479973682558649 0.959947365117298 0.520026317441351 33 0.36841660771305 0.7368332154261 0.63158339228695 34 0.333534565734883 0.667069131469766 0.666465434265117 35 0.259388227984045 0.51877645596809 0.740611772015955 36 0.562186873578296 0.875626252843409 0.437813126421704 37 0.702344788705741 0.595310422588517 0.297655211294259 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 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261060606acat0c79mu3yrlw/10936y1261060525.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261060606acat0c79mu3yrlw/10936y1261060525.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261060606acat0c79mu3yrlw/1sb5c1261060525.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261060606acat0c79mu3yrlw/1sb5c1261060525.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261060606acat0c79mu3yrlw/2514b1261060525.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261060606acat0c79mu3yrlw/2514b1261060525.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261060606acat0c79mu3yrlw/3nxne1261060525.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261060606acat0c79mu3yrlw/3nxne1261060525.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261060606acat0c79mu3yrlw/4e3641261060525.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261060606acat0c79mu3yrlw/4e3641261060525.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261060606acat0c79mu3yrlw/53wwq1261060525.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261060606acat0c79mu3yrlw/53wwq1261060525.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261060606acat0c79mu3yrlw/6sguc1261060525.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261060606acat0c79mu3yrlw/6sguc1261060525.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261060606acat0c79mu3yrlw/7s6sl1261060525.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261060606acat0c79mu3yrlw/7s6sl1261060525.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261060606acat0c79mu3yrlw/8hgki1261060525.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261060606acat0c79mu3yrlw/8hgki1261060525.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261060606acat0c79mu3yrlw/9hk8o1261060525.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261060606acat0c79mu3yrlw/9hk8o1261060525.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