Paper: Multiple Regression (4 maanden)

*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, 19 Dec 2010 19:21:41 +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/19/t1292786408r53hjydos65x08t.htm/, Retrieved Sun, 19 Dec 2010 20:20:19 +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/19/t1292786408r53hjydos65x08t.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 «

634 0 627 691 651 608 731 0 634 627 691 651 475 0 731 634 627 691 337 0 475 731 634 627 803 0 337 475 731 634 722 0 803 337 475 731 590 0 722 803 337 475 724 0 590 722 803 337 627 0 724 590 722 803 696 0 627 724 590 722 825 0 696 627 724 590 677 0 825 696 627 724 656 0 677 825 696 627 785 0 656 677 825 696 412 0 785 656 677 825 352 0 412 785 656 677 839 0 352 412 785 656 729 0 839 352 412 785 696 0 729 839 352 412 641 0 696 729 839 352 695 0 641 696 729 839 638 0 695 641 696 729 762 0 638 695 641 696 635 0 762 638 695 641 721 0 635 762 638 695 854 0 721 635 762 638 418 0 854 721 635 762 367 0 418 854 721 635 824 0 367 418 854 721 687 0 824 367 418 854 601 0 687 824 367 418 676 0 601 687 824 367 740 0 676 601 687 824 691 0 740 676 601 687 683 0 691 740 676 601 594 0 683 691 740 676 729 0 594 683 691 740 731 0 729 594 683 691 386 0 731 729 594 683 331 0 386 731 729 594 706 0 331 386 731 729 715 0 706 331 386 731 657 0 715 706 331 386 653 0 657 715 706 331 642 0 653 65 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 8 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation faillissement[t] = + 778.604796059064 + 76.3933609657064crisis[t] + 0.280149255301893`t-1`[t] -0.495364240293815`t-2`[t] + 0.311554991320822`t-3`[t] -0.296255280190032`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) 778.604796059064 148.317073 5.2496 1e-06 1e-06 crisis 76.3933609657064 34.13332 2.2381 0.028224 0.014112 `t-1` 0.280149255301893 0.111732 2.5073 0.014358 0.007179 `t-2` -0.495364240293815 0.113207 -4.3757 3.9e-05 2e-05 `t-3` 0.311554991320822 0.111022 2.8062 0.006402 0.003201 `t-4` -0.296255280190032 0.117648 -2.5181 0.013959 0.00698 Multiple Linear Regression - Regression Statistics Multiple R 0.5331509684037 R-squared 0.284249955109803 Adjusted R-squared 0.235888465590195 F-TEST (value) 5.87760960080759 F-TEST (DF numerator) 5 F-TEST (DF denominator) 74 p-value 0.000125716661025455 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 136.596211836813 Sum Squared Residuals 1380730.85652439 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 634 634.660778084641 -0.660778084641084 2 731 668.048356855219 62.9516431447808 3 475 659.965554285313 -184.965554285313 4 337 561.338236490936 -224.338236490936 5 803 677.63793197128 125.362068028719 6 722 768.052910145945 -46.0529101459454 7 590 547.367847415949 42.632152584051 8 724 736.580503801625 -12.5805038016255 9 627 676.217668865322 -49.2176688653216 10 696 565.53580174271 130.464198257289 11 825 713.770497489116 111.229502510884 12 677 645.810577139203 31.1894228607972 13 656 590.68055693619 65.3194430638098 14 785 677.860309685609 107.139690314391 15 412 640.075142805728 -228.075142805728 16 352 508.980610230607 -156.980610230607 17 839 723.354471306463 115.645528693537 18 729 735.082070148933 -6.08207014893312 19 696 554.833187074269 141.166812925731 20 641 769.580925666269 -128.580925666269 21 695 591.972366056525 103.027633943475 22 638 656.652225166303 -18.6522251663030 23 762 606.574948361855 155.425051638145 24 635 702.667227657813 -67.6672276578133 25 721 571.906686802491 149.093313197509 26 854 714.430151170382 139.569848829618 27 418 632.785538818958 -214.785538818958 28 367 509.17516938598 -142.175169385979 29 824 726.825225883013 97.174774116987 30 687 704.87708332981 -17.8770833298102 31 601 553.393175144669 47.6068248553309 32 676 754.654890432266 -78.6548904322662 33 740 640.19571238738 99.8042876126207 34 691 634.766190837108 56.233809162892 35 683 638.180144393915 44.8198556060846 36 594 657.932171556177 -63.9321715561774 37 729 602.735269249777 126.264730750223 38 731 696.666904900427 34.3330950995727 39 386 604.994678985333 -218.994678985333 40 331 575.779101190816 -244.779101190816 41 706 691.900202207565 14.0997977924345 42 715 716.122223595872 -1.12222359587171 43 657 617.954523926324 39.0454760736763 44 653 730.37475111193 -77.3747511119296 45 642 649.693544878389 -7.69354487838882 46 643 627.856873012925 15.1431269870747 47 718 649.522615197198 68.4773848028023 48 654 667.796361320777 -13.7963613207770 49 632 616.284854032831 15.7151459671692 50 731 664.895250863865 66.104749136135 51 392 737.762735932138 -345.762735932138 52 344 605.857206718812 -261.857206718812 53 792 797.700080228867 -5.70008022886674 54 852 812.038015341646 39.9619846583536 55 649 692.399691409152 -43.399691409152 56 629 759.604427726088 -130.604427726088 57 685 740.53131735381 -55.5313173538102 58 617 685.105980407064 -68.1059804070638 59 715 692.224155642241 22.7758443577586 60 715 776.735736119573 -61.735736119573 61 629 690.414005470322 -61.4140054703215 62 916 716.998917716721 199.001082283279 63 531 810.97006119501 -279.97006119501 64 357 534.149331685865 -177.149331685865 65 917 791.012830381873 125.987169618127 66 828 829.115854089002 -1.11585408900189 67 708 586.626310185936 121.373689814064 68 858 823.115030828584 34.884969171416 69 775 730.949776825155 44.0502231748449 70 785 622.37287356944 162.62712643056 71 1006 748.573480387772 257.426519612228 72 789 735.23546709842 53.7645329015802 73 734 592.672319761957 141.327680238043 74 906 750.649251144112 155.350748855888 75 532 693.000106233582 -161.000106233582 76 387 550.173506698729 -163.173506698729 77 991 764.699589467475 226.300410532525 78 841 838.260079565749 2.73992043425136 79 892 562.661691182553 329.338308817447 80 782 882.390169632353 -100.390169632353 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 9 0.727985493373582 0.544029013252836 0.272014506626418 10 0.781913763469962 0.436172473060077 0.218086236530038 11 0.748089492746155 0.50382101450769 0.251910507253845 12 0.656085366030044 0.687829267939913 0.343914633969956 13 0.565942782952815 0.86811443409437 0.434057217047185 14 0.49386707450882 0.98773414901764 0.50613292549118 15 0.603183250425277 0.793633499149445 0.396816749574723 16 0.583526525842801 0.832946948314399 0.416473474157199 17 0.518813481553604 0.962373036892791 0.481186518446396 18 0.42554155036036 0.85108310072072 0.57445844963964 19 0.405852948467305 0.81170589693461 0.594147051532695 20 0.458929964559452 0.917859929118903 0.541070035440548 21 0.456287330894152 0.912574661788305 0.543712669105848 22 0.374737425405250 0.749474850810499 0.62526257459475 23 0.394682606461005 0.78936521292201 0.605317393538995 24 0.331422684134943 0.662845368269886 0.668577315865057 25 0.338129496087290 0.676258992174581 0.66187050391271 26 0.345218365146975 0.69043673029395 0.654781634853025 27 0.411837576876923 0.823675153753846 0.588162423123077 28 0.423093193429794 0.846186386859589 0.576906806570206 29 0.372114618838709 0.744229237677417 0.627885381161291 30 0.307042941359825 0.614085882719651 0.692957058640175 31 0.249702353381858 0.499404706763715 0.750297646618142 32 0.217282767643386 0.434565535286771 0.782717232356614 33 0.195915790780190 0.391831581560381 0.80408420921981 34 0.158827748549335 0.317655497098671 0.841172251450665 35 0.124741743798783 0.249483487597565 0.875258256201217 36 0.09644125292723 0.19288250585446 0.90355874707277 37 0.092332154552409 0.184664309104818 0.907667845447591 38 0.0686910570034295 0.137382114006859 0.93130894299657 39 0.103774092214417 0.207548184428835 0.896225907785583 40 0.194683208035267 0.389366416070534 0.805316791964733 41 0.152165244162264 0.304330488324528 0.847834755837736 42 0.117413312591550 0.234826625183101 0.88258668740845 43 0.087849014582102 0.175698029164204 0.912150985417898 44 0.0711853710042623 0.142370742008525 0.928814628995738 45 0.0508846769780046 0.101769353956009 0.949115323021995 46 0.0353146652106301 0.0706293304212603 0.96468533478937 47 0.0255115589229595 0.051023117845919 0.97448844107704 48 0.0170238586439486 0.0340477172878972 0.982976141356051 49 0.0111065190146715 0.022213038029343 0.988893480985328 50 0.00736487567257901 0.014729751345158 0.99263512432742 51 0.0167084706699856 0.0334169413399712 0.983291529330014 52 0.0292440976072337 0.0584881952144673 0.970755902392766 53 0.0295776763402905 0.059155352680581 0.97042232365971 54 0.0273567595960009 0.0547135191920019 0.97264324040400 55 0.0213307581723645 0.0426615163447291 0.978669241827635 56 0.0228107506982487 0.0456215013964974 0.977189249301751 57 0.0173672971605862 0.0347345943211724 0.982632702839414 58 0.0125373928672546 0.0250747857345092 0.987462607132745 59 0.00923495926114089 0.0184699185222818 0.99076504073886 60 0.00662646859023674 0.0132529371804735 0.993373531409763 61 0.00502018015696507 0.0100403603139301 0.994979819843035 62 0.00803580617600081 0.0160716123520016 0.991964193824 63 0.0328616226411503 0.0657232452823007 0.96713837735885 64 0.0876679498676436 0.175335899735287 0.912332050132356 65 0.0654282652811641 0.130856530562328 0.934571734718836 66 0.0422622390375774 0.0845244780751548 0.957737760962423 67 0.0347137192475689 0.0694274384951377 0.965286280752431 68 0.0240769903927204 0.0481539807854408 0.97592300960728 69 0.0135677705516932 0.0271355411033864 0.986432229448307 70 0.00968000239521782 0.0193600047904356 0.990319997604782 71 0.0157066285221985 0.0314132570443970 0.984293371477801 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 16 0.253968253968254 NOK 10% type I error level 24 0.380952380952381 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/19/t1292786408r53hjydos65x08t/10q9qq1292786491.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292786408r53hjydos65x08t/10q9qq1292786491.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292786408r53hjydos65x08t/1jqbx1292786491.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292786408r53hjydos65x08t/1jqbx1292786491.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292786408r53hjydos65x08t/2c0si1292786491.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292786408r53hjydos65x08t/2c0si1292786491.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292786408r53hjydos65x08t/3c0si1292786491.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292786408r53hjydos65x08t/3c0si1292786491.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292786408r53hjydos65x08t/4c0si1292786491.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292786408r53hjydos65x08t/4c0si1292786491.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292786408r53hjydos65x08t/54ra31292786491.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292786408r53hjydos65x08t/54ra31292786491.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292786408r53hjydos65x08t/64ra31292786491.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292786408r53hjydos65x08t/64ra31292786491.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292786408r53hjydos65x08t/7xir61292786491.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292786408r53hjydos65x08t/7xir61292786491.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292786408r53hjydos65x08t/8xir61292786491.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292786408r53hjydos65x08t/8xir61292786491.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292786408r53hjydos65x08t/9q9qq1292786491.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292786408r53hjydos65x08t/9q9qq1292786491.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