trend huwelijken

*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 09:39:32 +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/t1292751501z295y1l3hkpurj2.htm/, Retrieved Sun, 19 Dec 2010 10:38:33 +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/t1292751501z295y1l3hkpurj2.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 «

3111 3995 5245 5588 10681 10516 7496 9935 10249 6271 3616 3724 2886 3318 4166 6401 9209 9820 7470 8207 9564 5309 3385 3706 2733 3045 3449 5542 10072 9418 7516 7840 10081 4956 3641 3970 2931 3170 3889 4850 8037 12370 6712 7297 10613 5184 3506 3810 2692 3073 3713 4555 7807 10869 9682 7704 9826 5456 3677 3431 2765 3483 3445 6081 8767 9407 6551 12480 9530 5960 3252 3717 2642 2989 3607 5366 8898 9435 7328 8594 11349 5797 3621 3851

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 60 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Huwelijken[t] = + 3942.16666666666 -966.666170634917M1[t] -489.254960317458M2[t] + 149.299107142860M3[t] + 1706.13888888889M4[t] + 5294.26438492063M5[t] + 6493.24702380953M6[t] + 3771.65823412699M7[t] + 5104.64087301587M8[t] + 6416.62351190477M9[t] + 1809.46329365079M10[t] -219.982638888888M11[t] -4.12549603174602t + 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) 3942.16666666666 360.987825 10.9205 0 0 M1 -966.666170634917 444.048678 -2.1769 0.032812 0.016406 M2 -489.254960317458 443.714202 -1.1026 0.273909 0.136955 M3 149.299107142860 443.411363 0.3367 0.737332 0.368666 M4 1706.13888888889 443.140227 3.8501 0.000256 0.000128 M5 5294.26438492063 442.900851 11.9536 0 0 M6 6493.24702380953 442.693288 14.6676 0 0 M7 3771.65823412699 442.517581 8.5232 0 0 M8 5104.64087301587 442.373769 11.5392 0 0 M9 6416.62351190477 442.261883 14.5087 0 0 M10 1809.46329365079 442.181947 4.0921 0.000111 5.6e-05 M11 -219.982638888888 442.133978 -0.4975 0.620341 0.31017 t -4.12549603174602 3.76029 -1.0971 0.276295 0.138148 Multiple Linear Regression - Regression Statistics Multiple R 0.962996808516119 R-squared 0.92736285321223 Adjusted R-squared 0.915086152346691 F-TEST (value) 75.5384417498834 F-TEST (DF numerator) 12 F-TEST (DF denominator) 71 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 827.127017118907 Sum Squared Residuals 48573876.2738095 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3111 2971.375 139.625000000002 2 3995 3444.66071428572 550.339285714281 3 5245 4079.08928571429 1165.91071428571 4 5588 5631.80357142857 -43.8035714285721 5 10681 9215.80357142858 1465.19642857142 6 10516 10410.6607142857 105.339285714295 7 7496 7684.94642857144 -188.946428571435 8 9935 9013.80357142857 921.196428571426 9 10249 10321.6607142857 -72.6607142857083 10 6271 5710.37500000001 560.624999999992 11 3616 3676.80357142857 -60.8035714285711 12 3724 3892.66071428571 -168.660714285713 13 2886 2921.86904761905 -35.8690476190466 14 3318 3395.15476190476 -77.154761904761 15 4166 4029.58333333333 136.416666666666 16 6401 5582.29761904762 818.702380952381 17 9209 9166.29761904762 42.7023809523817 18 9820 10361.1547619048 -541.154761904763 19 7470 7635.44047619047 -165.440476190474 20 8207 8964.29761904762 -757.297619047618 21 9564 10272.1547619048 -708.154761904762 22 5309 5660.86904761905 -351.869047619046 23 3385 3627.29761904762 -242.297619047619 24 3706 3843.15476190476 -137.154761904761 25 2733 2872.36309523809 -139.363095238095 26 3045 3345.64880952381 -300.648809523809 27 3449 3980.07738095238 -531.077380952382 28 5542 5532.79166666667 9.20833333333374 29 10072 9116.79166666667 955.208333333334 30 9418 10311.6488095238 -893.648809523811 31 7516 7585.93452380952 -69.9345238095219 32 7840 8914.79166666667 -1074.79166666667 33 10081 10222.6488095238 -141.648809523810 34 4956 5611.36309523809 -655.363095238094 35 3641 3577.79166666667 63.2083333333335 36 3970 3793.64880952381 176.351190476192 37 2931 2822.85714285714 108.142857142857 38 3170 3296.14285714286 -126.142857142857 39 3889 3930.57142857143 -41.5714285714294 40 4850 5483.28571428571 -633.285714285714 41 8037 9067.28571428571 -1030.28571428571 42 12370 10262.1428571429 2107.85714285714 43 6712 7536.42857142857 -824.42857142857 44 7297 8865.2857142857 -1568.28571428571 45 10613 10173.1428571429 439.857142857142 46 5184 5561.85714285714 -377.857142857141 47 3506 3528.28571428571 -22.2857142857143 48 3810 3744.14285714286 65.8571428571439 49 2692 2773.35119047619 -81.3511904761907 50 3073 3246.63690476190 -173.636904761904 51 3713 3881.06547619048 -168.065476190477 52 4555 5433.77976190476 -878.779761904762 53 7807 9017.77976190476 -1210.77976190476 54 10869 10212.6369047619 656.363095238094 55 9682 7486.92261904762 2195.07738095238 56 7704 8815.77976190476 -1111.77976190476 57 9826 10123.6369047619 -297.636904761906 58 5456 5512.35119047619 -56.3511904761893 59 3677 3478.77976190476 198.220238095238 60 3431 3694.63690476190 -263.636904761904 61 2765 2723.84523809524 41.1547619047617 62 3483 3197.13095238095 285.869047619048 63 3445 3831.55952380952 -386.559523809525 64 6081 5384.27380952381 696.726190476191 65 8767 8968.27380952381 -201.273809523809 66 9407 10163.1309523810 -756.130952380954 67 6551 7437.41666666667 -886.416666666665 68 12480 8766.27380952381 3713.72619047619 69 9530 10074.1309523810 -544.130952380953 70 5960 5462.84523809524 497.154761904763 71 3252 3429.27380952381 -177.273809523810 72 3717 3645.13095238095 71.8690476190484 73 2642 2674.33928571429 -32.3392857142862 74 2989 3147.625 -158.625000000000 75 3607 3782.05357142857 -175.053571428573 76 5366 5334.76785714286 31.2321428571427 77 8898 8918.76785714286 -20.7678571428571 78 9435 10113.625 -678.625000000002 79 7328 7387.91071428571 -59.9107142857128 80 8594 8716.76785714286 -122.767857142857 81 11349 10024.625 1324.37500000000 82 5797 5413.33928571428 383.660714285715 83 3621 3379.76785714286 241.232142857143 84 3851 3595.625 255.375000000001 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.288172335725881 0.576344671451761 0.711827664274119 17 0.28746458153376 0.57492916306752 0.71253541846624 18 0.168566834925211 0.337133669850422 0.83143316507479 19 0.106238687748047 0.212477375496094 0.893761312251953 20 0.127712502580572 0.255425005161145 0.872287497419428 21 0.073820508857265 0.14764101771453 0.926179491142735 22 0.0421460271840534 0.0842920543681067 0.957853972815947 23 0.0246600399067090 0.0493200798134179 0.97533996009329 24 0.0160275885708956 0.0320551771417912 0.983972411429104 25 0.0106922779108151 0.0213845558216302 0.989307722089185 26 0.00524740430808635 0.0104948086161727 0.994752595691914 27 0.00321826457719601 0.00643652915439202 0.996781735422804 28 0.00170972978220659 0.00341945956441317 0.998290270217793 29 0.00270148950502061 0.00540297901004121 0.99729851049498 30 0.00143372952922057 0.00286745905844113 0.99856627047078 31 0.00111304798248370 0.00222609596496740 0.998886952017516 32 0.000902918297571093 0.00180583659514219 0.99909708170243 33 0.000838528255254145 0.00167705651050829 0.999161471744746 34 0.000435144082996888 0.000870288165993777 0.999564855917003 35 0.000353705810130411 0.000707411620260821 0.99964629418987 36 0.00031659529624784 0.00063319059249568 0.999683404703752 37 0.000242132650344418 0.000484265300688835 0.999757867349656 38 0.000122541873446564 0.000245083746893129 0.999877458126553 39 5.82204485618780e-05 0.000116440897123756 0.999941779551438 40 3.18262091116634e-05 6.36524182233268e-05 0.999968173790888 41 9.10163813340042e-05 0.000182032762668008 0.999908983618666 42 0.0327281705843358 0.0654563411686716 0.967271829415664 43 0.0246470805951845 0.049294161190369 0.975352919404815 44 0.0438018095828834 0.0876036191657668 0.956198190417117 45 0.0441104817601504 0.0882209635203007 0.95588951823985 46 0.0305778875953949 0.0611557751907897 0.969422112404605 47 0.0206877949390094 0.0413755898780187 0.97931220506099 48 0.0138689274905090 0.0277378549810180 0.98613107250949 49 0.00866668685165728 0.0173333737033146 0.991333313148343 50 0.00515991752360373 0.0103198350472075 0.994840082476396 51 0.00296419637295836 0.00592839274591673 0.997035803627042 52 0.00247870048641352 0.00495740097282704 0.997521299513586 53 0.0029811266432359 0.0059622532864718 0.997018873356764 54 0.00361382979802313 0.00722765959604626 0.996386170201977 55 0.103353394282320 0.206706788564639 0.89664660571768 56 0.289807649484976 0.579615298969951 0.710192350515024 57 0.240038569117979 0.480077138235957 0.759961430882022 58 0.192416728388238 0.384833456776476 0.807583271611762 59 0.142025417474524 0.284050834949047 0.857974582525476 60 0.104778267698033 0.209556535396066 0.895221732301967 61 0.0693164263892503 0.138632852778501 0.93068357361075 62 0.0460508305030805 0.0921016610061609 0.95394916949692 63 0.0277092531535783 0.0554185063071567 0.972290746846422 64 0.0199290796295586 0.0398581592591172 0.980070920370441 65 0.0102555576652720 0.0205111153305439 0.989744442334728 66 0.00538888293795216 0.0107777658759043 0.994611117062048 67 0.00354554597576883 0.00709109195153766 0.996454454024231 68 0.701681808818163 0.596636382363675 0.298318191181837 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 20 0.377358490566038 NOK 5% type I error level 32 0.60377358490566 NOK 10% type I error level 39 0.735849056603774 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/19/t1292751501z295y1l3hkpurj2/10fhvu1292751511.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292751501z295y1l3hkpurj2/10fhvu1292751511.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292751501z295y1l3hkpurj2/1qygi1292751511.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292751501z295y1l3hkpurj2/1qygi1292751511.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292751501z295y1l3hkpurj2/2qygi1292751511.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292751501z295y1l3hkpurj2/2qygi1292751511.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292751501z295y1l3hkpurj2/307f21292751511.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292751501z295y1l3hkpurj2/307f21292751511.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292751501z295y1l3hkpurj2/407f21292751511.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292751501z295y1l3hkpurj2/407f21292751511.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292751501z295y1l3hkpurj2/507f21292751511.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292751501z295y1l3hkpurj2/507f21292751511.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292751501z295y1l3hkpurj2/6bgwo1292751511.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292751501z295y1l3hkpurj2/6bgwo1292751511.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292751501z295y1l3hkpurj2/74pv81292751511.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292751501z295y1l3hkpurj2/74pv81292751511.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292751501z295y1l3hkpurj2/84pv81292751511.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292751501z295y1l3hkpurj2/84pv81292751511.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292751501z295y1l3hkpurj2/94pv81292751511.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292751501z295y1l3hkpurj2/94pv81292751511.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') }

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