MR

*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: Sun, 26 Dec 2010 06:49:21 +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/26/t12933461229cy4yrbumhetm8v.htm/, Retrieved Sun, 26 Dec 2010 07:48:53 +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/26/t12933461229cy4yrbumhetm8v.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 «

1,8 0,8 2,9 1,8 2,3 0,8 2,6 1,7 -0,1 2,9 1,7 2,2 1 2,2 1,4 -1,5 2,9 1,6 2,1 0,6 2,3 1,2 -4,4 1,4 1,8 2,4 0,9 2,4 1 -4,2 1,1 1,6 2,5 0,6 2,1 1,7 3,5 1,9 1,5 2,4 0,6 1,9 2,4 10 2,8 1,5 2,3 0,4 2,2 2 8,6 1,4 1,3 2,1 0,3 1,9 2,1 9,5 0,7 1,4 2,3 0 2,3 2 9,9 -0,8 1,4 2,2 0,3 2,1 1,8 10,4 -3,1 1,3 2,1 0,1 2,2 2,7 16 0,1 1,3 2 0 2,3 2,3 12,7 1 1,2 2,1 0 1,9 1,9 10,2 1,9 1,1 2,1 0 1,7 2 8,9 -0,5 1,4 2,5 -0,2 2,5 2,3 12,6 1,5 1,2 2,2 -0,3 2,1 2,8 13,6 3,9 1,5 2,3 0,1 2,4 2,4 14,8 1,9 1,1 2,3 0,1 1,5 2,3 9,5 2,6 1,3 2,2 0,4 1,9 2,7 13,7 1,7 1,5 2,2 0,4 2,1 2,7 17 1,4 1,1 1,6 -0,5 2,2 2,9 14,7 2,8 1,4 1,8 0,5 2 3 17,4 0,5 1,3 1,7 0,4 2 2,2 9 1 1,5 1,9 0,7 2,2 2,3 9,1 1,5 1,6 1,8 0,8 2,3 2,8 12,2 1,8 1,7 1,9 0,8 2,3 2,8 15,9 2,7 1,1 1,5 0 2 2,8 12,9 3 1,6 1 1,1 2,2 2,2 10,9 -0,3 1,3 0,8 0,9 1,9 2,6 10,6 1,1 1,7 1,1 1,1 2,3 2,8 13,2 1,7 1,6 1,5 1 2,2 2,5 9,6 1,6 1,7 1,7 1,1 2,3 2,4 6,4 3 1,9 2,3 1,5 2,1 2,3 5,8 3,3 1,8 2,4 1 2,4 1,9 -1 6,7 1,9 3 1 2,3 1,7 -0,2 5,6 1,6 3 0,9 1,9 2 2,7 6 1 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 HCPI[t] = -0.0999629355292632 + 0.102771874381054ED[t] + 0.0799171884528425NBL[t] + 0.299497088937924IT[t] + 0.0759467616143747BL[t] + 0.254171329601758NEI[t] + 0.258910129983403D[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) -0.0999629355292632 0.040991 -2.4386 0.018062 0.009031 ED 0.102771874381054 0.000902 113.8859 0 0 NBL 0.0799171884528425 0.004212 18.9755 0 0 IT 0.299497088937924 0.200864 1.491 0.141768 0.070884 BL 0.0759467616143747 0.030516 2.4887 0.015935 0.007968 NEI 0.254171329601758 0.078473 3.239 0.002054 0.001027 D 0.258910129983403 0.093519 2.7685 0.007699 0.00385 Multiple Linear Regression - Regression Statistics Multiple R 0.999078733038373 R-squared 0.99815831480956 Adjusted R-squared 0.997953683121733 F-TEST (value) 4877.82867556148 F-TEST (DF numerator) 6 F-TEST (DF denominator) 54 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.052813378113236 Sum Squared Residuals 0.150619657017508 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.8 1.8042901239284 -0.00429012392840041 2 1.7 1.62152126585721 0.0784787341427856 3 1.4 1.36431873782615 0.0356812621738534 4 1.2 1.13123037759259 0.0687696224074087 5 1 0.921580416431254 0.0784195835687461 6 1.7 1.68753118887573 0.0124688111242695 7 2.4 2.44671793887337 -0.0467179388733699 8 2 2.01277430884026 -0.0127743088402587 9 2.1 2.12177867819572 -0.0217786781957181 10 2 2.05988634199129 -0.0598863419912855 11 1.8 1.86497510776303 -0.0649751077630332 12 2.7 2.68911181122276 0.0108881887772431 13 2.3 2.29597101064712 0.00402898935287778 14 1.9 2.02923505941157 -0.129235059411573 15 2 1.98035203982288 0.0196479601771210 16 2.3 2.30877772071303 -0.00877772071302889 17 2.8 2.88013622105944 -0.0801362210594432 18 2.4 2.49081014085079 -0.0908101408507907 19 2.3 2.23418143104823 0.0658185689517684 20 2.7 2.70557927762536 -0.00557927762536377 21 2.7 2.65252123035995 0.047478769640048 22 2.9 2.83545776572684 0.0645422342731623 23 3 2.86617077509874 0.133829224901261 24 2.2 2.24596781951198 -0.0459678195119769 25 2.3 2.36986677986737 -0.0698667798673748 26 2.8 2.74997913203972 0.0500208679602761 27 2.8 2.71107347617225 0.0889265238277503 28 2.8 2.86987866178533 -0.0698786617853303 29 2.2 2.16706240720922 0.0329375927907824 30 2.6 2.54509609070208 0.0549039092979244 31 2.8 2.80937412695796 -0.00937412695796248 32 2.5 2.52785086751607 -0.0278508675160681 33 2.4 2.46621891393091 -0.0662189139309082 34 2.3 2.35676328729992 -0.0567632872999156 35 1.9 1.97925973511249 -0.079259735112492 36 1.7 1.75473801568429 -0.0547380156842941 37 2 1.96689279824437 0.0331072017556274 38 2.1 2.04521859393438 0.0547814060656172 39 1.7 1.67065406651777 0.0293459334822318 40 1.8 1.77062857762527 0.0293714223747302 41 1.8 1.80971031986385 -0.00971031986385004 42 1.8 1.78914823631657 0.0108517636834279 43 1.3 1.31442032023069 -0.0144203202306948 44 1.3 1.27932566256597 0.0206743374340254 45 1.3 1.30772817324992 -0.0077281732499199 46 1.2 1.19726710702037 0.00273289297962568 47 1.4 1.42316047429507 -0.0231604742950717 48 2.2 2.20250580055179 -0.00250580055178534 49 2.9 2.85873746063686 0.0412625393631379 50 3.1 3.10332590465758 -0.00332590465758297 51 3.5 3.51728231005729 -0.0172823100572907 52 3.6 3.64036614521704 -0.0403661452170365 53 4.4 4.41720979272857 -0.0172097927285728 54 4.1 4.05975795420987 0.040242045790132 55 5.1 5.12732058634347 -0.0273205863434747 56 5.8 5.78510951422341 0.0148904857765852 57 5.9 5.85311998582334 0.0468800141766634 58 5.4 5.4122897901338 -0.0122897901337956 59 5.5 5.4932118715932 0.00678812840680105 60 4.8 4.78284633530321 0.0171536646967917 61 3.2 3.21472393510501 -0.0147239351050119 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.188560917212367 0.377121834424735 0.811439082787633 11 0.100830059450119 0.201660118900237 0.899169940549881 12 0.414726513221313 0.829453026442626 0.585273486778687 13 0.313056067531453 0.626112135062907 0.686943932468547 14 0.578684861380296 0.842630277239408 0.421315138619704 15 0.626117641552986 0.747764716894028 0.373882358447014 16 0.536846538132947 0.926306923734106 0.463153461867053 17 0.659487358835324 0.681025282329352 0.340512641164676 18 0.779412896042138 0.441174207915724 0.220587103957862 19 0.871172417242234 0.257655165515531 0.128827582757766 20 0.885213874750556 0.229572250498888 0.114786125249444 21 0.9398180066759 0.1203639866482 0.0601819933241 22 0.9359496283398 0.128100743320402 0.0640503716602009 23 0.98075844479516 0.0384831104096812 0.0192415552048406 24 0.992534602884102 0.0149307942317969 0.00746539711589847 25 0.999184954161954 0.00163009167609243 0.000815045838046214 26 0.998609546521238 0.00278090695752320 0.00139045347876160 27 0.997961836903629 0.00407632619274229 0.00203816309637114 28 0.999743856850003 0.000512286299994044 0.000256143149997022 29 0.99945239810123 0.00109520379753867 0.000547601898769335 30 0.999658444330475 0.000683111339050641 0.000341555669525321 31 0.999270823066252 0.00145835386749530 0.000729176933747652 32 0.998626032738247 0.00274793452350661 0.00137396726175330 33 0.99762783254436 0.00474433491128156 0.00237216745564078 34 0.99903986089315 0.00192027821370082 0.000960139106850412 35 0.999689381615445 0.000621236769110915 0.000310618384555457 36 0.99998410967388 3.17806522409666e-05 1.58903261204833e-05 37 0.99997521979664 4.95604067179121e-05 2.47802033589560e-05 38 0.999964128561625 7.17428767509856e-05 3.58714383754928e-05 39 0.999904142036771 0.000191715926457419 9.58579632287096e-05 40 0.99976829495162 0.000463410096760779 0.000231705048380390 41 0.999477292682915 0.00104541463417013 0.000522707317085067 42 0.998969461989456 0.00206107602108896 0.00103053801054448 43 0.997877695388684 0.00424460922263205 0.00212230461131602 44 0.99506407799515 0.00987184400970033 0.00493592200485017 45 0.988380286510352 0.0232394269792966 0.0116197134896483 46 0.976932130150532 0.0461357396989355 0.0230678698494678 47 0.995031054338057 0.00993789132388547 0.00496894566194274 48 0.988381408464773 0.0232371830704538 0.0116185915352269 49 0.974288341240792 0.0514233175184153 0.0257116587592077 50 0.932992447714565 0.134015104570869 0.0670075522854345 51 0.921878573668533 0.156242852662933 0.0781214263314667 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 21 0.5 NOK 5% type I error level 26 0.619047619047619 NOK 10% type I error level 27 0.642857142857143 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/26/t12933461229cy4yrbumhetm8v/105vkq1293346154.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t12933461229cy4yrbumhetm8v/105vkq1293346154.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t12933461229cy4yrbumhetm8v/1crf71293346154.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t12933461229cy4yrbumhetm8v/1crf71293346154.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t12933461229cy4yrbumhetm8v/2crf71293346154.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t12933461229cy4yrbumhetm8v/2crf71293346154.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t12933461229cy4yrbumhetm8v/3crf71293346154.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t12933461229cy4yrbumhetm8v/3crf71293346154.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t12933461229cy4yrbumhetm8v/44jxa1293346154.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t12933461229cy4yrbumhetm8v/44jxa1293346154.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t12933461229cy4yrbumhetm8v/54jxa1293346154.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t12933461229cy4yrbumhetm8v/54jxa1293346154.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t12933461229cy4yrbumhetm8v/6fawd1293346154.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t12933461229cy4yrbumhetm8v/6fawd1293346154.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t12933461229cy4yrbumhetm8v/7fawd1293346154.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t12933461229cy4yrbumhetm8v/7fawd1293346154.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t12933461229cy4yrbumhetm8v/8q1vg1293346154.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t12933461229cy4yrbumhetm8v/8q1vg1293346154.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t12933461229cy4yrbumhetm8v/9q1vg1293346154.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/26/t12933461229cy4yrbumhetm8v/9q1vg1293346154.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