4 vertragingen - Ws8

*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: Tue, 30 Nov 2010 21:32:25 +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/Nov/30/t1291152642ao5wgemsn436i3a.htm/, Retrieved Tue, 30 Nov 2010 22:30:52 +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/Nov/30/t1291152642ao5wgemsn436i3a.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:

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30 35 47 30 37 43 30 35 47 30 82 43 30 35 47 40 82 43 30 35 47 40 82 43 30 19 47 40 82 43 52 19 47 40 82 136 52 19 47 40 80 136 52 19 47 42 80 136 52 19 54 42 80 136 52 66 54 42 80 136 81 66 54 42 80 63 81 66 54 42 137 63 81 66 54 72 137 63 81 66 107 72 137 63 81 58 107 72 137 63 36 58 107 72 137 52 36 58 107 72 79 52 36 58 107 77 79 52 36 58 54 77 79 52 36 84 54 77 79 52 48 84 54 77 79 96 48 84 54 77 83 96 48 84 54 66 83 96 48 84 61 66 83 96 48 53 61 66 83 96 30 53 61 66 83 74 30 53 61 66 69 74 30 53 61 59 69 74 30 53 42 59 69 74 30 65 42 59 69 74 70 65 42 59 69 100 70 65 42 59 63 100 70 65 42 105 63 100 70 65 82 105 63 100 70 81 82 105 63 100 75 81 82 105 63 102 75 81 82 105 121 102 75 81 82 98 121 102 75 81 76 98 121 102 75 77 76 98 121 102 63 77 76 98 121 37 63 77 76 98 35 37 63 77 76 23 35 37 63 77 40 23 35 37 63 29 40 23 35 37 37 29 40 23 35 51 37 29 40 23 20 51 37 29 40 28 20 51 37 29 13 28 20 51 37 22 13 28 20 51 25 22 13 28 20 13 25 22 13 2 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 7 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Ye[t] = + 27.2834214053944 + 0.397125190922649`Ye-1`[t] + 0.241628865980788`Ye-2`[t] -0.0721154477263936`Ye-3`[t] + 0.194553871149098`Ye-4`[t] -8.55123851482719M1[t] -2.43346143954193M2[t] + 8.75387561158733M3[t] -12.7401440949288M4[t] -2.18969425871734M5[t] -16.6086800675589M6[t] -17.5742918715359M7[t] + 23.4944770899369M8[t] + 3.14624069205249M9[t] -11.5904562802941M10[t] -13.6691227830971M11[t] -0.290723078985568t + 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) 27.2834214053944 13.826997 1.9732 0.053163 0.026582 `Ye-1` 0.397125190922649 0.126329 3.1436 0.002612 0.001306 `Ye-2` 0.241628865980788 0.135726 1.7803 0.080182 0.040091 `Ye-3` -0.0721154477263936 0.134954 -0.5344 0.595094 0.297547 `Ye-4` 0.194553871149098 0.123448 1.576 0.120373 0.060186 M1 -8.55123851482719 12.080546 -0.7079 0.481824 0.240912 M2 -2.43346143954193 12.239001 -0.1988 0.843081 0.42154 M3 8.75387561158733 12.311775 0.711 0.479876 0.239938 M4 -12.7401440949288 12.466598 -1.0219 0.31098 0.15549 M5 -2.18969425871734 12.734413 -0.172 0.864065 0.432032 M6 -16.6086800675589 12.472345 -1.3316 0.1881 0.09405 M7 -17.5742918715359 12.332299 -1.4251 0.159408 0.079704 M8 23.4944770899369 12.332275 1.9051 0.061642 0.030821 M9 3.14624069205249 13.509236 0.2329 0.816649 0.408324 M10 -11.5904562802941 13.781157 -0.841 0.403723 0.201862 M11 -13.6691227830971 13.151083 -1.0394 0.302864 0.151432 t -0.290723078985568 0.13498 -2.1538 0.03535 0.017675 Multiple Linear Regression - Regression Statistics Multiple R 0.819156252095656 R-squared 0.671016965347402 Adjusted R-squared 0.581801227136528 F-TEST (value) 7.52128468366599 F-TEST (DF numerator) 16 F-TEST (DF denominator) 59 p-value 3.64684837883544e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 21.0468770596200 Sum Squared Residuals 26135.2910038028 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 30 48.7324279956963 -18.7324279956963 2 43 47.086469936221 -4.08646993622098 3 82 66.1103682427065 15.8896317572935 4 40 60.980613945781 -20.9806139457810 5 47 62.0743382813179 -15.0743382813179 6 19 39.7127912223652 -20.7127912223652 7 52 39.6448028347573 12.3551971652427 8 136 78.086301047883 57.913698952117 9 80 102.160719820264 -22.1607198202642 10 42 77.3637956525042 -35.3637956525042 11 54 46.735012459634 7.26498754036598 12 66 76.0780077967497 -10.0780077967497 13 81 66.7464651150316 14.2535348849684 14 63 73.171510890558 -10.1715108905580 15 137 82.0135654968783 54.9864345031217 16 72 86.5196819898917 -14.5196819898917 17 107 93.0631935460354 13.9368064539646 18 58 67.7084772393128 -9.70847723931278 19 36 74.5345088777169 -38.5345088777169 20 52 79.565943831732 -27.5659438317321 21 79 70.3081947868588 8.69180521314117 22 77 61.9226169098056 15.0773830901944 23 54 59.8489239987506 -5.84892399875062 24 84 64.7759314294526 19.2240685705474 25 48 67.6874470622396 -19.6874470622396 26 96 67.7364077201564 28.2635922798436 27 83 82.3581892130578 0.641810786942179 28 66 75.4417767652626 -9.4417767652626 29 61 65.3437191668188 -4.34371916681884 30 53 54.8167802383049 -1.81678023830489 31 30 47.8720617844677 -17.8720617844677 32 74 74.636358776985 -0.636358776984946 33 69 65.517598009219 3.482401990781 34 59 59.2384464349425 -0.238446434942508 35 42 44.041841877633 -2.04184187763301 36 65 57.1737722454439 7.8262277545561 37 70 53.1063844426971 16.8936155573029 38 100 65.7569522110259 34.2430477889741 39 63 84.8093951335112 -21.8093951335112 40 105 59.6940480610925 45.3059519389075 41 82 76.5020707197341 5.49792928026586 42 81 71.3117825122287 9.6882174877713 43 75 53.8735164837603 21.1264835162397 44 102 101.8571002407 0.142899759300056 45 121 86.0881241341539 34.9118758658461 46 98 85.3682009070425 12.6317990929575 47 76 75.3414400721609 0.658559927839122 48 77 78.3083826726402 -1.30838267264018 49 63 69.9028900677126 -6.90289006771262 50 37 67.5236210706274 -30.5236210706274 51 35 60.3598753420447 -25.3598753420447 52 23 32.7027017985157 -9.70270179851573 53 40 36.8649159775072 3.13508402249283 54 29 21.0926191891718 7.90738081082816 55 37 20.0518755581521 16.9481244418479 56 51 57.788396377094 -6.78839637709397 57 20 48.7429062355124 -28.7429062355124 58 28 22.0703932248579 5.92960677514215 59 13 15.9343250260694 -2.93432502606939 60 22 30.2482108697931 -8.24821086979305 61 25 14.7478494171391 10.2521505828609 62 13 26.5791014651225 -13.5791014651225 63 16 29.8677526473628 -13.8677526473628 64 13 7.90947754002223 5.09052245997777 65 16 19.1517623085866 -3.15176230858660 66 17 2.35754959861670 14.6424504013833 67 9 3.02323446114568 5.97676553885432 68 17 40.065899725606 -23.065899725606 69 25 21.1824570139918 3.81754298600822 70 14 12.0365468708473 1.96345312915268 71 8 5.09845656575206 2.90154343424794 72 7 14.4156949859207 -7.41569498592066 73 10 6.07653589948362 3.92346410051638 74 7 11.1459367062888 -4.14593670628875 75 10 20.4808539244387 -10.4808539244387 76 3 -1.24830010056577 4.24830010056577 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 20 0.996099335949888 0.00780132810022442 0.00390066405011221 21 0.997024245112129 0.00595150977574312 0.00297575488787156 22 0.997010201153075 0.00597959769385076 0.00298979884692538 23 0.997975637239023 0.00404872552195301 0.00202436276097650 24 0.996893357106895 0.00621328578620933 0.00310664289310467 25 0.998285676495904 0.00342864700819255 0.00171432350409628 26 0.998646560570466 0.00270687885906715 0.00135343942953357 27 0.99898245301392 0.00203509397215923 0.00101754698607962 28 0.998989207834904 0.00202158433019232 0.00101079216509616 29 0.998622536587734 0.00275492682453228 0.00137746341226614 30 0.99733822958538 0.00532354082923934 0.00266177041461967 31 0.997549177556421 0.00490164488715757 0.00245082244357879 32 0.996572155052906 0.00685568989418842 0.00342784494709421 33 0.993491044187745 0.0130179116245098 0.00650895581225492 34 0.991527495424926 0.0169450091501476 0.00847250457507381 35 0.990574988283061 0.0188500234338774 0.0094250117169387 36 0.98320276505002 0.0335944698999592 0.0167972349499796 37 0.97468372855773 0.0506325428845392 0.0253162714422696 38 0.987462119912082 0.0250757601758366 0.0125378800879183 39 0.993952748110382 0.0120945037792358 0.00604725188961791 40 0.998617267523335 0.00276546495332905 0.00138273247666452 41 0.997799467942049 0.00440106411590238 0.00220053205795119 42 0.995644240347678 0.00871151930464418 0.00435575965232209 43 0.99429148746791 0.0114170250641821 0.00570851253209106 44 0.995401906397139 0.00919618720572168 0.00459809360286084 45 0.999896049871661 0.000207900256677033 0.000103950128338516 46 0.999862297511302 0.000275404977396668 0.000137702488698334 47 0.99962162047816 0.000756759043680463 0.000378379521840231 48 0.999428230221577 0.00114353955684585 0.000571769778422925 49 0.999298053114472 0.00140389377105624 0.000701946885528119 50 0.998815280229245 0.00236943954150934 0.00118471977075467 51 0.99758863707142 0.00482272585716041 0.00241136292858021 52 0.993688658217002 0.0126226835659961 0.00631134178299806 53 0.986947335476286 0.0261053290474277 0.0130526645237139 54 0.964516471543372 0.0709670569132558 0.0354835284566279 55 0.948650159108347 0.102699681783305 0.0513498408916527 56 0.995325050457116 0.00934989908576899 0.00467494954288449 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 25 0.675675675675676 NOK 5% type I error level 34 0.918918918918919 NOK 10% type I error level 36 0.972972972972973 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291152642ao5wgemsn436i3a/1063je1291152737.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291152642ao5wgemsn436i3a/1063je1291152737.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291152642ao5wgemsn436i3a/16a2q1291152736.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291152642ao5wgemsn436i3a/16a2q1291152736.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291152642ao5wgemsn436i3a/26a2q1291152736.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291152642ao5wgemsn436i3a/26a2q1291152736.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291152642ao5wgemsn436i3a/3zj1b1291152736.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291152642ao5wgemsn436i3a/3zj1b1291152736.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291152642ao5wgemsn436i3a/4zj1b1291152736.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291152642ao5wgemsn436i3a/4zj1b1291152736.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291152642ao5wgemsn436i3a/5zj1b1291152736.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291152642ao5wgemsn436i3a/5zj1b1291152736.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291152642ao5wgemsn436i3a/6k2391291152737.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291152642ao5wgemsn436i3a/6k2391291152737.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291152642ao5wgemsn436i3a/7vckc1291152737.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291152642ao5wgemsn436i3a/7vckc1291152737.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291152642ao5wgemsn436i3a/8vckc1291152737.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291152642ao5wgemsn436i3a/8vckc1291152737.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291152642ao5wgemsn436i3a/9vckc1291152737.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291152642ao5wgemsn436i3a/9vckc1291152737.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Include Monthly Dummies ; par3 = No 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') }

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