autoregressie

*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 09:49:17 +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/t1292752064g3n3qpiwtvgbx1q.htm/, Retrieved Sun, 19 Dec 2010 10:47:47 +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/t1292752064g3n3qpiwtvgbx1q.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 «

10681 5588 5245 3995 3111 10516 10681 5588 5245 3995 7496 10516 10681 5588 5245 9935 7496 10516 10681 5588 10249 9935 7496 10516 10681 6271 10249 9935 7496 10516 3616 6271 10249 9935 7496 3724 3616 6271 10249 9935 2886 3724 3616 6271 10249 3318 2886 3724 3616 6271 4166 3318 2886 3724 3616 6401 4166 3318 2886 3724 9209 6401 4166 3318 2886 9820 9209 6401 4166 3318 7470 9820 9209 6401 4166 8207 7470 9820 9209 6401 9564 8207 7470 9820 9209 5309 9564 8207 7470 9820 3385 5309 9564 8207 7470 3706 3385 5309 9564 8207 2733 3706 3385 5309 9564 3045 2733 3706 3385 5309 3449 3045 2733 3706 3385 5542 3449 3045 2733 3706 10072 5542 3449 3045 2733 9418 10072 5542 3449 3045 7516 9418 10072 5542 3449 7840 7516 9418 10072 5542 10081 7840 7516 9418 10072 4956 10081 7840 7516 9418 3641 4956 10081 7840 7516 3970 3641 4956 10081 7840 2931 3970 3641 4956 10081 3170 2931 3970 3641 4956 3889 3170 2931 3970 3641 4850 3889 3170 2931 3970 8037 4850 3889 3170 2931 12370 8037 4850 3889 3170 6712 12370 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 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation Huwelijken[t] = + 6214.96128800579 -0.175516351406775Y1[t] -0.106575914618364Y2[t] + 0.0271398679986119Y3[t] + 0.073760150845012Y4[t] + 4031.97577374594M1[t] + 5971.83245211946M2[t] + 3751.46212297632M3[t] + 4520.05202572045M4[t] + 5476.46010737663M5[t] + 1224.78647547959M6[t] -1311.17591604365M7[t] -2074.6126564527M8[t] -3196.31196964168M9[t] -2550.02236330078M10[t] -1903.49497481489M11[t] -2.59492029511821t + 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) 6214.96128800579 1103.233208 5.6334 0 0 Y1 -0.175516351406775 0.125627 -1.3971 0.167279 0.083639 Y2 -0.106575914618364 0.125055 -0.8522 0.397312 0.198656 Y3 0.0271398679986119 0.124762 0.2175 0.828495 0.414248 Y4 0.073760150845012 0.126251 0.5842 0.561148 0.280574 M1 4031.97577374594 548.519602 7.3507 0 0 M2 5971.83245211946 932.517564 6.404 0 0 M3 3751.46212297632 1381.153969 2.7162 0.008514 0.004257 M4 4520.05202572045 1571.123217 2.877 0.005474 0.002737 M5 5476.46010737663 1728.233747 3.1688 0.002362 0.001181 M6 1224.78647547959 1779.278932 0.6884 0.493752 0.246876 M7 -1311.17591604365 1553.168165 -0.8442 0.401755 0.200878 M8 -2074.6126564527 1387.083795 -1.4957 0.139734 0.069867 M9 -3196.31196964168 1036.867145 -3.0827 0.003043 0.001521 M10 -2550.02236330078 566.083573 -4.5047 2.9e-05 1.5e-05 M11 -1903.49497481489 503.278873 -3.7822 0.000348 0.000174 t -2.59492029511821 4.295635 -0.6041 0.547958 0.273979 Multiple Linear Regression - Regression Statistics Multiple R 0.965373182951927 R-squared 0.931945382362735 Adjusted R-squared 0.914661669946921 F-TEST (value) 53.9204402354016 F-TEST (DF numerator) 16 F-TEST (DF denominator) 63 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 839.615049495978 Sum Squared Residuals 44412066.1744284 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 10681 9042.45769955553 1638.54230044447 2 10516 10148.3879495504 367.612050449628 3 7496 7513.1009282227 -17.1009282226929 4 9935 8990.26339728897 944.73660271103 5 10249 10209.0338097502 39.9661902497656 6 6271 5545.58164121692 725.418358783078 7 3616 3515.20202060124 100.797979398761 8 3724 3827.54818769646 -103.548187696455 9 2886 2882.45553403903 3.54446596096899 10 3318 3296.24849418714 21.7515058128584 11 4166 3760.76642027071 405.233579729285 12 6401 5452.01070059083 948.989299409166 13 9209 8948.64954961842 260.350450381579 14 9820 10209.7432170024 -389.743217002425 15 7470 7703.47852149972 -233.478521499725 16 8207 9057.88173240155 -850.881732401546 17 9564 10356.4937050489 -792.49370504891 18 5309 5766.79177729359 -457.791777293586 19 3385 3677.09875280315 -292.098752803146 20 3706 3793.43110095364 -87.4311009536374 21 2733 2802.46056475628 -69.4605647562788 22 3045 3216.65524425351 -171.655244253511 23 3449 3776.32234313078 -327.322343130782 24 5542 5570.33202317989 -28.3320231798896 25 10072 9125.99949567388 946.00050432612 26 9418 10079.0864663184 -661.08646631844 27 7516 7574.7228621415 -58.7228621415015 28 7840 9021.57419087894 -1181.57419087894 29 10081 10437.6114536451 -356.611453645136 30 4956 5655.62099402805 -699.620994028046 31 3641 3646.249868834 -5.24986883400449 32 3970 3741.94250570753 228.05749429247 33 2931 2726.25539488453 204.744605115468 34 3170 3103.53839463366 66.461605366341 35 3889 3728.18924833704 160.810751662962 36 4850 5473.490169379 -623.490169379002 37 8037 9187.42135824101 -1150.42135824101 38 12370 10500.0352915807 1869.96470841927 39 6712 7256.01521321242 -544.015213212417 40 7297 8710.66653815323 -1413.66653815323 41 10613 10517.4798076331 95.5201923669351 42 5184 5784.89748459958 -600.897484599585 43 3506 3444.35460099224 61.6453990077638 44 3810 3684.58550893946 125.414491060541 45 2692 2783.01500619491 -91.0150061949061 46 3073 3144.55333763025 -71.5533376302484 47 3713 3725.24693523201 -12.2469352320115 48 4555 5465.29181481629 -910.291814816287 49 7807 9206.5557560896 -1399.5557560896 50 10869 10528.7735522756 340.226447724432 51 9682 7491.85062588648 2190.14937411352 52 7704 8290.2129646369 -586.212964636895 53 9826 10040.6733660923 -214.673366092279 54 5456 5818.40483390314 -362.40483390314 55 3677 3679.46392895795 -2.4639289579463 56 3431 3603.1058258103 -172.1058258103 57 2765 2749.50498381751 15.4950161824862 58 3483 3165.6975505341 317.3024494659 59 3445 3616.6931216697 -171.693121669697 60 6081 5411.521141652 669.478858348005 61 8767 8952.65294231028 -185.65294231028 62 9407 10189.4721428988 -782.47214289885 63 6551 7636.65132820756 -1085.65132820756 64 12480 9103.0418679903 3376.95813200971 65 9530 9536.08867469944 -6.08867469943836 66 5960 5137.39979492176 822.60020507824 67 3252 3490.08809230019 -238.088092300188 68 3717 3937.09205015729 -220.092050157292 69 2642 2705.30851630774 -63.3085163077388 70 2989 3151.30697876134 -162.306978761339 71 3607 3661.78193135976 -54.7819313597573 72 5366 5422.35415038199 -56.3541503819943 73 8898 9007.26319851128 -109.26319851128 74 9435 10179.5013803736 -744.501380373613 75 7328 7579.18052082963 -251.180520829627 76 8594 8883.35930865013 -289.35930865013 77 11349 10114.6191831309 1234.38081686906 78 5797 5224.30347403696 572.69652596304 79 3621 3245.54273551124 375.457264488761 80 3851 3621.29482073533 229.705179264674 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 20 0.14547698558413 0.29095397116826 0.85452301441587 21 0.104772651692018 0.209545303384037 0.895227348307982 22 0.0502418130461135 0.100483626092227 0.949758186953887 23 0.0208840920762421 0.0417681841524842 0.979115907923758 24 0.00792058352949427 0.0158411670589885 0.992079416470506 25 0.0432202901536641 0.0864405803073281 0.956779709846336 26 0.0215607865680516 0.0431215731361031 0.978439213431948 27 0.013641889342496 0.0272837786849921 0.986358110657504 28 0.00948698190243536 0.0189739638048707 0.990513018097565 29 0.0121544755288266 0.0243089510576533 0.987845524471173 30 0.00689408613747985 0.0137881722749597 0.99310591386252 31 0.00564917733802718 0.0112983546760544 0.994350822661973 32 0.00488678917537225 0.00977357835074449 0.995113210824628 33 0.00348865745029402 0.00697731490058805 0.996511342549706 34 0.00169827195728582 0.00339654391457164 0.998301728042714 35 0.00088697421959303 0.00177394843918606 0.999113025780407 36 0.000879894153749292 0.00175978830749858 0.99912010584625 37 0.00187050763207849 0.00374101526415699 0.998129492367921 38 0.145959399204091 0.291918798408182 0.854040600795909 39 0.127963437829267 0.255926875658534 0.872036562170733 40 0.13682194998503 0.273643899970061 0.86317805001497 41 0.0960551897856938 0.192110379571388 0.903944810214306 42 0.0870504737755966 0.174100947551193 0.912949526224403 43 0.0757243673682433 0.151448734736487 0.924275632631757 44 0.0508616396653511 0.101723279330702 0.949138360334649 45 0.0329609595020219 0.0659219190040438 0.967039040497978 46 0.0203456805639422 0.0406913611278843 0.979654319436058 47 0.0122035318708848 0.0244070637417696 0.987796468129115 48 0.0107603454226639 0.0215206908453278 0.989239654577336 49 0.0243184516883457 0.0486369033766914 0.975681548311654 50 0.0154255121367062 0.0308510242734123 0.984574487863294 51 0.303194022057279 0.606388044114559 0.69680597794272 52 0.289229629290347 0.578459258580693 0.710770370709653 53 0.301975546212669 0.603951092425339 0.69802445378733 54 0.277111001637414 0.554222003274827 0.722888998362586 55 0.235816325996775 0.471632651993551 0.764183674003225 56 0.307484300252513 0.614968600505027 0.692515699747487 57 0.26734751803255 0.534695036065101 0.73265248196745 58 0.21174384493984 0.42348768987968 0.78825615506016 59 0.148556917251495 0.29711383450299 0.851443082748505 60 0.0951439222535181 0.190287844507036 0.904856077746482 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 6 0.146341463414634 NOK 5% type I error level 19 0.463414634146341 NOK 10% type I error level 21 0.51219512195122 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/19/t1292752064g3n3qpiwtvgbx1q/103zjn1292752147.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292752064g3n3qpiwtvgbx1q/103zjn1292752147.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292752064g3n3qpiwtvgbx1q/1p73w1292752147.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292752064g3n3qpiwtvgbx1q/1p73w1292752147.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292752064g3n3qpiwtvgbx1q/2p73w1292752147.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292752064g3n3qpiwtvgbx1q/2p73w1292752147.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292752064g3n3qpiwtvgbx1q/3p73w1292752147.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292752064g3n3qpiwtvgbx1q/3p73w1292752147.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292752064g3n3qpiwtvgbx1q/4iz2h1292752147.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292752064g3n3qpiwtvgbx1q/4iz2h1292752147.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292752064g3n3qpiwtvgbx1q/5iz2h1292752147.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292752064g3n3qpiwtvgbx1q/5iz2h1292752147.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292752064g3n3qpiwtvgbx1q/6t82k1292752147.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292752064g3n3qpiwtvgbx1q/6t82k1292752147.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292752064g3n3qpiwtvgbx1q/7t82k1292752147.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292752064g3n3qpiwtvgbx1q/7t82k1292752147.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292752064g3n3qpiwtvgbx1q/83zjn1292752147.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292752064g3n3qpiwtvgbx1q/83zjn1292752147.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292752064g3n3qpiwtvgbx1q/93zjn1292752147.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292752064g3n3qpiwtvgbx1q/93zjn1292752147.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by