multiple regression met 4 maanden minder, totale industrie zonder de bouwnijverheid, prijsindex

*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, 19 Nov 2009 08:40:21 -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/19/t1258645489yy3ijkj99s8cxl5.htm/, Retrieved Thu, 19 Nov 2009 16:45:01 +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/19/t1258645489yy3ijkj99s8cxl5.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 «

101.7 86.2 102.8 105.6 96.9 97.6 104.2 88.8 101.7 102.8 105.6 96.9 92.7 89.6 104.2 101.7 102.8 105.6 91.9 87.8 92.7 104.2 101.7 102.8 106.5 88.3 91.9 92.7 104.2 101.7 112.3 88.6 106.5 91.9 92.7 104.2 102.8 91 112.3 106.5 91.9 92.7 96.5 91.5 102.8 112.3 106.5 91.9 101 95.4 96.5 102.8 112.3 106.5 98.9 98.7 101 96.5 102.8 112.3 105.1 99.9 98.9 101 96.5 102.8 103 98.6 105.1 98.9 101 96.5 99 100.3 103 105.1 98.9 101 104.3 100.2 99 103 105.1 98.9 94.6 100.4 104.3 99 103 105.1 90.4 101.4 94.6 104.3 99 103 108.9 103 90.4 94.6 104.3 99 111.4 109.1 108.9 90.4 94.6 104.3 100.8 111.4 111.4 108.9 90.4 94.6 102.5 114.1 100.8 111.4 108.9 90.4 98.2 121.8 102.5 100.8 111.4 108.9 98.7 127.6 98.2 102.5 100.8 111.4 113.3 129.9 98.7 98.2 102.5 100.8 104.6 128 113.3 98.7 98.2 102.5 99.3 123.5 104.6 113.3 98.7 98.2 111.8 124 99.3 104.6 113.3 98.7 97.3 127.4 111.8 99.3 104.6 113.3 97.7 127.6 97.3 111.8 99.3 104.6 115.6 128.4 97.7 97.3 111.8 99.3 111.9 131.4 115.6 97.7 97.3 111.8 107 135.1 111.9 115.6 97 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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation tot_indus[t] = + 209.298982723361 + 0.200657477896676prijsindex[t] -0.0431033994434085`y(t-1)`[t] -0.6525411392476`y(t-2)`[t] -0.0302790380657440`y(t-3)`[t] -0.535338506090538`y(t-4)`[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) 209.298982723361 26.091732 8.0217 0 0 prijsindex 0.200657477896676 0.027945 7.1805 0 0 `y(t-1)` -0.0431033994434085 0.110024 -0.3918 0.696555 0.348278 `y(t-2)` -0.6525411392476 0.112174 -5.8172 0 0 `y(t-3)` -0.0302790380657440 0.110954 -0.2729 0.785825 0.392912 `y(t-4)` -0.535338506090538 0.117064 -4.5731 2.3e-05 1.2e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.75865628105714 R-squared 0.57555935278745 Adjusted R-squared 0.54187358713566 F-TEST (value) 17.0861294570833 F-TEST (DF numerator) 5 F-TEST (DF denominator) 63 p-value 1.20284671112358e-10 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5.35827567184311 Sum Squared Residuals 1808.80044505434 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 101.7 98.0732065677168 3.62679343228316 2 104.2 100.580754262622 3.61924573737824 3 92.7 96.7786533030993 -4.07865330309934 4 91.9 96.8140608472913 -4.91406084729132 5 106.5 104.966270168677 1.53372983132296 6 112.3 103.9290533641 8.37094663589992 7 102.8 100.814147011759 1.98585298824085 8 96.5 97.5254162868964 -1.02541628689636 9 101 96.787112080336 4.2128879196641 10 98.9 98.549013163459 0.350986836541081 11 105.1 101.220357896826 3.87964210317380 12 103 105.298975408506 -2.29897540850587 13 99 99.339417898957 -0.339417898956908 14 104.3 101.798582968143 2.50141703185665 15 94.6 100.964918245840 -6.36491824583976 16 90.4 99.3705376753783 -8.97053767537831 17 108.9 108.183148090991 0.716851909009263 18 111.4 108.806831188255 2.59316881174482 19 100.8 102.408529281683 -1.60852928168276 20 102.5 103.464107179349 -0.964107179348907 21 98.2 101.873370098285 -3.67337009828478 22 98.7 101.095819689242 -2.39581968924177 23 113.3 109.964820887295 3.33517911270495 24 104.6 107.848115881123 -3.24811588112258 25 99.3 100.079872229887 -0.779872229886675 26 111.8 105.376013688534 6.42398631146593 27 97.3 101.425410100603 -4.12541010060257 28 97.7 98.7517005522524 -1.05170055225244 29 115.6 110.815637800341 4.78436219965932 30 111.9 104.132357654116 7.7676423458838 31 107 101.104083230829 5.89591676917102 32 107.1 102.752838693818 4.34716130618157 33 100.6 98.582356635925 2.01764336407494 34 99.2 101.488235315550 -2.28823531555028 35 108.4 109.132595176346 -0.732595176345546 36 103 109.351434941858 -6.35143494185842 37 99.8 105.638106172010 -5.83810617200971 38 115 109.269022265746 5.73097773425413 39 90.8 106.120966519427 -15.3209665194275 40 95.9 101.396977695894 -5.49697769589443 41 114.4 118.682999968578 -4.28299996857774 42 108.2 107.273629184065 0.926370815935249 43 112.6 108.791337380320 3.80866261967963 44 109.1 110.641256759365 -1.54125675936521 45 105 100.211480097027 4.78851990297287 46 105 106.821124886288 -1.82112488628781 47 118.5 107.487819737111 11.0121802628893 48 103.7 111.712957362565 -8.0129573625646 49 112.5 109.348304771596 3.15169522840418 50 116.6 116.130998933345 0.469001066654665 51 96.6 105.319153193628 -8.71915319362799 52 101.9 111.162356866742 -9.2623568667424 53 116.5 118.587767786867 -2.08776778686737 54 119.3 113.974167636177 5.32583236382262 55 115.4 114.792405713625 0.607594286375487 56 108.5 110.656655655108 -2.15665565510752 57 111.5 105.698584797775 5.80141520222492 58 108.8 109.473513055453 -0.673513055453239 59 121.8 111.714865905895 10.0851340941052 60 109.6 119.388783300849 -9.78878330084894 61 112.2 110.047808082873 2.15219191712671 62 119.6 117.082413070292 2.51758692970814 63 104.1 108.998554080123 -4.89855408012331 64 105.3 109.263616089641 -3.9636160896411 65 115 117.610005932177 -2.61000593217657 66 124.1 114.060421359438 10.0395786405615 67 116.8 114.335801261777 2.46419873822286 68 107.5 106.291353497579 1.20864650242091 69 115.6 111.967035514755 3.63296448524526 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 9 0.507426227272045 0.985147545455911 0.492573772727955 10 0.357463833644103 0.714927667288206 0.642536166355897 11 0.241708104014202 0.483416208028405 0.758291895985798 12 0.347043051433719 0.694086102867438 0.652956948566281 13 0.236733725378626 0.473467450757252 0.763266274621374 14 0.167362611780725 0.334725223561451 0.832637388219275 15 0.244787562666659 0.489575125333317 0.755212437333342 16 0.298850970417961 0.597701940835921 0.701149029582039 17 0.218682184421183 0.437364368842367 0.781317815578817 18 0.156821781202718 0.313643562405435 0.843178218797282 19 0.107924299925842 0.215848599851684 0.892075700074158 20 0.0750416912885637 0.150083382577127 0.924958308711436 21 0.0490128145962606 0.0980256291925212 0.95098718540374 22 0.0394272904855781 0.0788545809711562 0.960572709514422 23 0.0337843377145679 0.0675686754291357 0.966215662285432 24 0.0261260742569473 0.0522521485138946 0.973873925743053 25 0.0187390581070881 0.0374781162141763 0.981260941892912 26 0.0287120352840848 0.0574240705681696 0.971287964715915 27 0.0199006714412746 0.0398013428825493 0.980099328558725 28 0.0142087215926446 0.0284174431852893 0.985791278407355 29 0.0107353739277771 0.0214707478555542 0.989264626072223 30 0.0265928590501273 0.0531857181002545 0.973407140949873 31 0.0347894824103153 0.0695789648206307 0.965210517589685 32 0.0299507044351416 0.0599014088702831 0.970049295564858 33 0.0220842634996639 0.0441685269993278 0.977915736500336 34 0.0142877284058930 0.0285754568117861 0.985712271594107 35 0.00920689027723683 0.0184137805544737 0.990793109722763 36 0.0136231069897485 0.027246213979497 0.986376893010251 37 0.0126436543231170 0.0252873086462339 0.987356345676883 38 0.0150233237718476 0.0300466475436952 0.984976676228152 39 0.178224892294303 0.356449784588606 0.821775107705697 40 0.176954902346483 0.353909804692967 0.823045097653517 41 0.165151262397159 0.330302524794318 0.83484873760284 42 0.136569588234607 0.273139176469214 0.863430411765393 43 0.113880348545090 0.227760697090180 0.88611965145491 44 0.0835289372949523 0.167057874589905 0.916471062705048 45 0.0799067398872224 0.159813479774445 0.920093260112778 46 0.056260788505587 0.112521577011174 0.943739211494413 47 0.167775105162648 0.335550210325296 0.832224894837352 48 0.187490601798392 0.374981203596784 0.812509398201608 49 0.157451938478646 0.314903876957293 0.842548061521354 50 0.118355969655952 0.236711939311904 0.881644030344048 51 0.249512670946705 0.49902534189341 0.750487329053295 52 0.384906761729616 0.769813523459232 0.615093238270384 53 0.335015917454758 0.670031834909515 0.664984082545242 54 0.280130541707999 0.560261083415998 0.719869458292001 55 0.199437447594646 0.398874895189293 0.800562552405354 56 0.135056038966217 0.270112077932433 0.864943961033783 57 0.106938096933296 0.213876193866591 0.893061903066704 58 0.0705540858946835 0.141108171789367 0.929445914105317 59 0.0836400325316719 0.167280065063344 0.916359967468328 60 0.246902375499842 0.493804750999685 0.753097624500158 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 10 0.192307692307692 NOK 10% type I error level 18 0.346153846153846 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645489yy3ijkj99s8cxl5/10qh421258645214.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645489yy3ijkj99s8cxl5/10qh421258645214.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645489yy3ijkj99s8cxl5/1iz7a1258645213.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645489yy3ijkj99s8cxl5/1iz7a1258645213.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645489yy3ijkj99s8cxl5/257ku1258645214.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645489yy3ijkj99s8cxl5/257ku1258645214.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645489yy3ijkj99s8cxl5/3qvz41258645214.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645489yy3ijkj99s8cxl5/3qvz41258645214.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645489yy3ijkj99s8cxl5/4pn7j1258645214.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645489yy3ijkj99s8cxl5/4pn7j1258645214.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645489yy3ijkj99s8cxl5/5anv51258645214.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645489yy3ijkj99s8cxl5/5anv51258645214.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645489yy3ijkj99s8cxl5/6x2hs1258645214.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645489yy3ijkj99s8cxl5/6x2hs1258645214.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645489yy3ijkj99s8cxl5/7rez01258645214.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645489yy3ijkj99s8cxl5/7rez01258645214.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645489yy3ijkj99s8cxl5/8tz8g1258645214.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645489yy3ijkj99s8cxl5/8tz8g1258645214.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645489yy3ijkj99s8cxl5/9x0951258645214.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645489yy3ijkj99s8cxl5/9x0951258645214.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