multiple regression nieuwbouw - lening

*The author of this computation has been verified*

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

Title produced by software: Multiple Regression

Date of computation: Fri, 24 Dec 2010 15:36:05 +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/24/t1293204926xfvflkihab78iiu.htm/, Retrieved Fri, 24 Dec 2010 16:35:37 +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/24/t1293204926xfvflkihab78iiu.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 «

3.48 4143 3.6 4429 3.66 5219 3.45 4929 3.3 5761 3.14 5592 3.21 4163 3.12 4962 3.14 5208 3.4 4755 3.42 4491 3.29 5732 3.49 5731 3.52 5040 3.81 6102 4.03 4904 3.98 5369 4.1 5578 3.96 4619 3.83 4731 3.72 5011 3.82 5299 3.76 4146 3.98 4625 4.14 4736 4 4219 4.13 5116 4.28 4205 4.46 4121 4.63 5103 4.49 4300 4.41 4578 4.5 3809 4.39 5657 4.33 4248 4.45 3830 4.17 4736 4.13 4839 4.33 4411 4.47 4570 4.63 4104 4.9 4801 4.77 3953 4.51 3828 4.63 4440 4.36 4026 3.95 4109 3.74 4785 4.15 3224 4.14 3552 3.97 3940 3.81 3913 4.07 3681 3.84 4309 3.63 3830 3.55 4143 3.6 4087 3.63 3818 3.55 3380 3.69 3430 3.53 3458 3.43 3970 3.4 5260 3.41 5024 3.09 5634 3.35 6549 3.22 4676

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 12 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation leningen[t] = + 4.78593806830172 -0.000197392497503478nieuwbouw[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) 4.78593806830172 0.357334 13.3935 0 0 nieuwbouw -0.000197392497503478 7.7e-05 -2.5607 0.012779 0.00639 Multiple Linear Regression - Regression Statistics Multiple R 0.302708854078525 R-squared 0.0916326503375336 Adjusted R-squared 0.0776577680350341 F-TEST (value) 6.55695327903725 F-TEST (DF numerator) 1 F-TEST (DF denominator) 65 p-value 0.0127791131867792 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.446252110893405 Sum Squared Residuals 12.9441615209933 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3.48 3.96814095114480 -0.488140951144805 2 3.6 3.91168669685881 -0.311686696858813 3 3.66 3.75574662383107 -0.0957466238310654 4 3.45 3.81299044810707 -0.362990448107074 5 3.3 3.64875989018418 -0.348759890184181 6 3.14 3.68211922226227 -0.542119222262268 7 3.21 3.96419310119474 -0.754193101194738 8 3.12 3.80647649568946 -0.686476495689459 9 3.14 3.7579179413036 -0.617917941303604 10 3.4 3.84733674267268 -0.447336742672679 11 3.42 3.8994483620136 -0.479448362013597 12 3.29 3.65448427261178 -0.364484272611782 13 3.49 3.65468166510929 -0.164681665109285 14 3.52 3.79107988088419 -0.271079880884188 15 3.81 3.58144904853549 0.228550951464505 16 4.03 3.81792526054466 0.212074739455339 17 3.98 3.72613774920554 0.253862250794456 18 4.1 3.68488271722732 0.415117282772683 19 3.96 3.87418212233315 0.0858178776668478 20 3.83 3.85207416261276 -0.0220741626127626 21 3.72 3.79680426331179 -0.0768042633117888 22 3.82 3.73995522403079 0.0800447759692125 23 3.76 3.9675487736523 -0.207548773652297 24 3.98 3.87299776734813 0.107002232651869 25 4.14 3.85108720012525 0.288912799874754 26 4 3.95313912133454 0.0468608786654567 27 4.13 3.77607805107392 0.353921948926076 28 4.28 3.95590261629959 0.324097383700408 29 4.46 3.97248358608988 0.487516413910116 30 4.63 3.77864415354147 0.85135584645853 31 4.49 3.93715032903676 0.552849670963239 32 4.41 3.88227521473079 0.527724785269205 33 4.5 4.03407004531097 0.465929954689031 34 4.39 3.66928870992454 0.720711290075457 35 4.33 3.94741473890694 0.382585261093058 36 4.45 4.0299248028634 0.420075197136604 37 4.17 3.85108720012525 0.318912799874755 38 4.13 3.83075577288239 0.299244227117613 39 4.33 3.91523976181388 0.414760238186125 40 4.47 3.88385435471082 0.586145645289177 41 4.63 3.97583925854744 0.654160741452557 42 4.9 3.83825668778752 1.06174331221248 43 4.77 4.00564552567047 0.764354474329531 44 4.51 4.0303195878584 0.479680412141597 45 4.63 3.90951537938627 0.720484620613725 46 4.36 3.99123587335271 0.368764126647286 47 3.95 3.97485229605993 -0.0248522960599257 48 3.74 3.84141496774757 -0.101414967747575 49 4.15 4.1495446563505 0.000455343649496745 50 4.14 4.08479991716936 0.0552000828306367 51 3.97 4.00821162813801 -0.0382116281380134 52 3.81 4.01354122557061 -0.203541225570607 53 4.07 4.05933628499141 0.0106637150085860 54 3.84 3.93537379655923 -0.0953737965592304 55 3.63 4.0299248028634 -0.399924802863396 56 3.55 3.96814095114481 -0.418140951144808 57 3.6 3.979194931005 -0.379194931005002 58 3.63 4.03229351283344 -0.402293512833438 59 3.55 4.11875142673996 -0.568751426739961 60 3.69 4.10888180186479 -0.418881801864787 61 3.53 4.10335481193469 -0.57335481193469 62 3.43 4.00228985321291 -0.572289853212909 63 3.4 3.74765353143342 -0.347653531433423 64 3.41 3.79423816084424 -0.384238160844244 65 3.09 3.67382873736712 -0.583828737367122 66 3.35 3.49321460215144 -0.14321460215144 67 3.22 3.86293074997545 -0.642930749975454 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0425190627996538 0.0850381255993076 0.957480937200346 6 0.0374795094833129 0.0749590189666258 0.962520490516687 7 0.0477840597452631 0.0955681194905263 0.952215940254737 8 0.0430355765492777 0.0860711530985553 0.956964423450722 9 0.029905737605805 0.05981147521161 0.970094262394195 10 0.0145105662960974 0.0290211325921947 0.985489433703903 11 0.0068817344139875 0.013763468827975 0.993118265586012 12 0.00315510564151721 0.00631021128303441 0.996844894358483 13 0.00217402904838005 0.00434805809676009 0.99782597095162 14 0.00129700136513427 0.00259400273026853 0.998702998634866 15 0.00396379963427384 0.00792759926854769 0.996036200365726 16 0.0219148887329038 0.0438297774658075 0.978085111267096 17 0.0380929508145069 0.0761859016290138 0.961907049185493 18 0.0684113764314079 0.136822752862816 0.931588623568592 19 0.0793038352790291 0.158607670558058 0.920696164720971 20 0.066532137056193 0.133064274112386 0.933467862943807 21 0.0483183699153539 0.0966367398307078 0.951681630084646 22 0.0368178561250169 0.0736357122500337 0.963182143874983 23 0.0276628984085261 0.0553257968170523 0.972337101591474 24 0.0262916185986380 0.0525832371972761 0.973708381401362 25 0.0332936959846193 0.0665873919692386 0.96670630401538 26 0.027747184847027 0.055494369694054 0.972252815152973 27 0.0310509788329936 0.0621019576659873 0.968949021167006 28 0.038003582288611 0.076007164577222 0.96199641771139 29 0.057951348779451 0.115902697558902 0.94204865122055 30 0.159507182184840 0.319014364369680 0.84049281781516 31 0.195539262618350 0.391078525236699 0.80446073738165 32 0.217420825363817 0.434841650727635 0.782579174636183 33 0.218924877347215 0.437849754694429 0.781075122652785 34 0.305532199671798 0.611064399343596 0.694467800328202 35 0.284878674274275 0.56975734854855 0.715121325725725 36 0.270860732562088 0.541721465124176 0.729139267437912 37 0.239591892571929 0.479183785143858 0.760408107428071 38 0.208507157271292 0.417014314542585 0.791492842728708 39 0.198328802665227 0.396657605330454 0.801671197334773 40 0.235712563860969 0.471425127721937 0.764287436139031 41 0.302805082355585 0.60561016471117 0.697194917644415 42 0.701741870798423 0.596516258403153 0.298258129201577 43 0.86595065120552 0.268098697588961 0.134049348794481 44 0.912633522483036 0.174732955033928 0.0873664775169642 45 0.991473980546595 0.0170520389068109 0.00852601945340545 46 0.998037171022302 0.00392565795539553 0.00196282897769776 47 0.997689756803592 0.00462048639281595 0.00231024319640797 48 0.99679923301945 0.00640153396110066 0.00320076698055033 49 0.99689655065529 0.00620689868941974 0.00310344934470987 50 0.998263237496476 0.00347352500704767 0.00173676250352384 51 0.998746564086247 0.00250687182750559 0.00125343591375279 52 0.99822611403569 0.0035477719286184 0.0017738859643092 53 0.99971736754031 0.000565264919378538 0.000282632459689269 54 0.99994183726284 0.000116325474320970 5.81627371604849e-05 55 0.999861192799464 0.00027761440107252 0.00013880720053626 56 0.999611066925369 0.00077786614926197 0.000388933074630985 57 0.999109625462297 0.00178074907540567 0.000890374537702834 58 0.998094999073814 0.00381000185237219 0.00190500092618610 59 0.99434177836386 0.0113164432722823 0.00565822163614117 60 0.991830513377125 0.0163389732457506 0.00816948662287528 61 0.978975636734383 0.0420487265312339 0.0210243632656169 62 0.943475339179913 0.113049321640174 0.0565246608200869 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 17 0.293103448275862 NOK 5% type I error level 24 0.413793103448276 NOK 10% type I error level 38 0.655172413793103 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293204926xfvflkihab78iiu/10e4q91293204952.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293204926xfvflkihab78iiu/10e4q91293204952.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293204926xfvflkihab78iiu/1ida01293204952.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293204926xfvflkihab78iiu/1ida01293204952.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293204926xfvflkihab78iiu/2ida01293204952.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293204926xfvflkihab78iiu/2ida01293204952.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293204926xfvflkihab78iiu/3ida01293204952.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293204926xfvflkihab78iiu/3ida01293204952.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293204926xfvflkihab78iiu/4bm9l1293204952.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293204926xfvflkihab78iiu/4bm9l1293204952.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293204926xfvflkihab78iiu/5bm9l1293204952.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293204926xfvflkihab78iiu/5bm9l1293204952.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293204926xfvflkihab78iiu/6bm9l1293204952.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293204926xfvflkihab78iiu/6bm9l1293204952.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293204926xfvflkihab78iiu/7md8o1293204952.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293204926xfvflkihab78iiu/7md8o1293204952.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293204926xfvflkihab78iiu/8e4q91293204952.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293204926xfvflkihab78iiu/8e4q91293204952.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293204926xfvflkihab78iiu/9e4q91293204952.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293204926xfvflkihab78iiu/9e4q91293204952.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