paper

*Unverified author*

R Software Module: rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Fri, 12 Dec 2008 06:41:54 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/12/t1229089519jculjpobz36744t.htm/, Retrieved Fri, 12 Dec 2008 14:45:31 +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/2008/Dec/12/t1229089519jculjpobz36744t.htm/}, year = {2008}, } @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 = {2008}, 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:

marie

Dataseries X:

» Textbox « » Textfile « » CSV «

60804 21863 30811 57907 20403 29877 54355 18792 28303 52536 17931 27605 49081 16475 26074 48877 16205 26112 64599 25134 32350 75314 31896 35804 71209 26537 36574 65210 22801 34486 59829 20200 32158 57656 19666 30965 57428 19809 30505 55315 18799 29629 52790 17884 28169 51050 17512 26972 48519 16327 25752 48354 16880 25027 65333 26537 31530 73990 31867 34705 72755 29427 35223 67424 25800 33471 59214 22041 29239 57427 21759 27954 56681 21333 27727 55437 20462 27314 53600 19594 26576 51641 18564 25775 49478 17640 24669 50124 18614 24480 71313 32562 30834 76208 35640 33218 74387 31865 33783 69520 28117 32546 64735 25508 30661 63413 25006 30070 62553 24452 29722 60109 22643 29075 57764 21474 28136 55667 20500 27315 53103 19505 26125 55301 21769 26057 76795 36062 32601 80928 38633 34214 79213 34629 35232 72759 30184 33565 67802 27271 31931 66940 26841 31779 66396 26482 31626 67539 25538 31230 67776 23789 29574 68014 22386 28312 68251 21087 27186 68488 22891 27397 68725 36192 33387 68962 38922 34996 69200 34669 36251 69437 30197 34284 68212 27001 32349 65444 25891 30991 63181 24879 29916 61198 23662 29067 59010 22741 27978 56388 21615 26719 53723 20305 25544 55340 21877 25703 75352 35369 31703 79817 37941 33733 78289 33480 35121 71892 29757 32714 66448 26323 31111 64167 25359 29977 61250 22207 30375 59580 21763 29323 56417 19944 28193 54662 19662 27222 53349 18624 26904 55385 19902 27952 73546 31726 33512 77683 32860 36215 74995 28894 36856 67282 22949 35341 60742 19758 32624 57283 18420 30885 57314 18245 31108 54704 16761 30267 51578 15341 28645 49962 14271 28474 46252 13418 25805 47234 15218 24756 64708 26485 30437 68753 27457 33177 62970 21402 33069 57474 17879 31342 52494 15607 28912 51831 15626 28373 51663 15303 28599 49637 14296 27884 46679 13686 25727 45557 12948 25393 41630 11609 23147 44417 14602 23164 60070 23629 29286 63157 24680 31008

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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Totale[t] = -1050.4280324071 + 1.05644865942879Vlaamse[t] + 1.24407254790969Waalse[t] + 66.220705569209M1[t] + 428.310079100591M2[t] + 1044.76038536411M3[t] + 1385.86632438017M4[t] + 1881.55721241687M5[t] + 1552.96809285461M6[t] -2145.39571244007M7[t] -3262.49548144678M8[t] -2239.85680165956M9[t] -881.97205844622M10[t] + 2.29819642144381M11[t] + 9.4964824801608t + 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) -1050.4280324071 7294.771502 -0.144 0.885828 0.442914 Vlaamse 1.05644865942879 0.085762 12.3184 0 0 Waalse 1.24407254790969 0.255574 4.8678 5e-06 2e-06 M1 66.220705569209 1380.395382 0.048 0.961846 0.480923 M2 428.310079100591 1395.126355 0.307 0.759557 0.379779 M3 1044.76038536411 1472.653064 0.7094 0.479905 0.239953 M4 1385.86632438017 1550.117622 0.894 0.373712 0.186856 M5 1881.55721241687 1722.008245 1.0927 0.277495 0.138748 M6 1552.96809285461 1736.968067 0.8941 0.373697 0.186848 M7 -2145.39571244007 1521.604881 -1.41 0.162038 0.081019 M8 -3262.49548144678 1743.634946 -1.8711 0.064618 0.032309 M9 -2239.85680165956 1853.20499 -1.2086 0.230003 0.115001 M10 -881.97205844622 1615.559672 -0.5459 0.586484 0.293242 M11 2.29819642144381 1437.395117 0.0016 0.998728 0.499364 t 9.4964824801608 9.753722 0.9736 0.332881 0.16644 Multiple Linear Regression - Regression Statistics Multiple R 0.958800738989298 R-squared 0.919298857086424 Adjusted R-squared 0.906604295279794 F-TEST (value) 72.4167459333887 F-TEST (DF numerator) 14 F-TEST (DF denominator) 89 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2835.69213875183 Sum Squared Residuals 715662341.614326 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 60804 60453.545470379 350.454529621016 2 57907 58120.7525238772 -213.752523877159 3 54355 55086.5903318712 -731.590331871202 4 52536 53659.2278191583 -1123.22781915827 5 49081 50721.5508706971 -1640.55087069708 6 48877 50164.4918523898 -1287.49185238978 7 64599 63669.1791634755 929.82083652446 8 75314 74002.3082924865 1311.69170751347 9 71209 70330.8709507655 878.129049234501 10 65210 65153.7365047976 56.2634952023826 11 59829 60403.4793874374 -574.479387437405 12 57656 58362.3555397049 -706.35553970489 13 57428 58016.8715140141 -588.871514014121 14 55315 56231.6366720337 -916.6366720337 15 52790 54074.5870174519 -1284.58701745189 16 51050 52543.0356977927 -1493.03569779271 17 48519 50278.5628984366 -1759.56289843663 18 48354 49641.7337727841 -1287.73377278413 19 65333 64245.1949331301 1087.80506686989 20 73990 72718.3933409723 1271.60665902773 21 72755 71817.2233540506 937.776645949376 22 67424 67173.2501880581 250.749811941859 23 59214 58830.9113918593 383.088608140655 24 57427 56941.5579318952 485.442068104809 25 56681 56284.8235226524 396.176477347602 26 55437 55222.4406340148 214.559365985233 27 53600 54013.2644460169 -413.264446016910 28 51641 52279.2226374258 -638.222637425816 29 49478 50432.3072086424 -954.307208642364 30 50124 50907.065854289 -783.065854288971 31 71313 69858.3814026053 1454.61859739467 32 76208 74968.3960440173 1239.60395598270 33 74387 72715.33850651 1671.66149349002 34 69520 68584.2324149001 935.767585099897 35 64735 64376.6478469885 358.352153011542 36 63413 63118.2620301993 294.737969800704 37 62553 62175.7694142525 377.230585747454 38 60109 59831.3247068598 277.675293140155 39 57764 58054.0988902441 -290.098890244077 40 55667 56354.3367556228 -687.336755622802 41 53103 54327.9113779955 -1224.91137799549 42 55301 56316.0215726023 -1015.02157260230 43 76795 75868.1856925244 926.814307475562 44 80928 79483.4009291676 1444.59907083236 45 79213 77551.9815128542 1661.01848714578 46 72759 72149.5795100213 609.420489978689 47 67802 67933.0967591686 -131.096759168646 48 66940 67296.9230943907 -356.923094390711 49 66396 66803.032113875 -407.032113874964 50 67539 65684.6777064135 1854.32229358650 51 67776 62402.7116504778 5373.28834952221 52 68014 59701.0970473334 8312.90295266662 53 68251 57433.1319203059 10817.8680796941 54 68488 59282.3719724423 9205.62802755768 55 68725 77097.3228306691 -8372.32283066913 56 68962 80875.5371139699 -11913.5371139699 57 69200 78975.9071753133 -9775.90717531328 58 69437 73171.7592943029 -3734.75929430288 59 68212 68281.835735911 -69.8357359110542 60 65444 65426.9254899425 17.0745100575424 61 63181 63096.138645647 84.8613543530224 62 61198 61125.8088899584 72.1911100416395 63 59010 59423.9714586945 -413.971458694477 64 56388 57018.7253518556 -630.725351855583 65 53723 54678.1797347269 -955.17973472685 66 55340 56217.6319253844 -877.631925384442 67 75352 74246.8052030413 1105.19479695875 68 79817 78381.8551408222 1435.14485917779 69 78289 76427.9455298764 1861.05447012357 70 71892 70867.685773698 1024.31422630207 71 66448 66139.359520268 308.640479731931 72 64167 63717.3630293078 449.636970692154 73 61250 60958.2949169057 291.705083094261 74 59580 59552.0532477299 27.9467522700938 75 56417 56850.5179458347 -433.517945834676 76 54662 55695.2074013517 -1033.20740135167 77 53349 54708.1859931462 -1359.18599314617 78 55385 57043.0227730234 -1658.02277302341 79 73546 72762.6477656727 783.352234327261 80 77683 76215.7853559383 1467.21464406166 81 74995 73855.4956381213 1139.50436187874 82 67282 67057.5196734275 224.480326572557 83 60742 61200.0136258674 -458.01362586738 84 57283 57630.2414447954 -347.241444795427 85 57314 57798.5082956286 -484.508295628623 86 54704 55556.0593282558 -852.059328255794 87 51578 52663.9633479011 -1085.96334790108 88 49962 51671.429298116 -1709.42929811594 89 46252 47955.0363317691 -1703.03633176909 90 47234 48232.5191789015 -998.519178901536 91 64708 63514.2350465461 1193.7649534539 92 68753 66842.2586382569 1910.74136174311 93 62970 61343.2373325087 1626.76266749128 94 57474 56840.2366407946 633.76335920543 95 52494 52310.6557324996 183.344267500356 96 51831 51667.3714397642 163.628560235816 97 51663 51683.0161066456 -20.0161066456461 98 49637 50101.246290857 -464.246290856972 99 46679 47399.2949115079 -720.294911507894 100 45557 46554.7179913438 -997.71799134383 101 41630 42851.1336642804 -1221.13366428038 102 44417 45715.1410981831 -1298.14109818310 103 60070 59179.0479623354 890.952037664638 104 63157 61324.065144369 1832.93485563104 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 18 7.02537988630135e-06 1.40507597726027e-05 0.999992974620114 19 3.26340189928423e-07 6.52680379856846e-07 0.99999967365981 20 2.81975202108412e-08 5.63950404216824e-08 0.99999997180248 21 5.32641696375494e-08 1.06528339275099e-07 0.99999994673583 22 2.56932653950875e-09 5.1386530790175e-09 0.999999997430673 23 1.77470247317384e-10 3.54940494634769e-10 0.99999999982253 24 1.06694676609249e-11 2.13389353218499e-11 0.99999999998933 25 4.2865695491007e-13 8.5731390982014e-13 0.999999999999571 26 1.79396645665261e-13 3.58793291330522e-13 0.99999999999982 27 9.14404874020955e-14 1.82880974804191e-13 0.999999999999909 28 7.12845684644371e-14 1.42569136928874e-13 0.999999999999929 29 4.41548042045917e-14 8.83096084091834e-14 0.999999999999956 30 4.08410232374475e-15 8.16820464748949e-15 0.999999999999996 31 8.18422433591052e-16 1.63684486718210e-15 1 32 8.42767642354754e-16 1.68553528470951e-15 1 33 6.964460939419e-17 1.3928921878838e-16 1 34 9.03847116976694e-18 1.80769423395339e-17 1 35 1.35396293230651e-18 2.70792586461301e-18 1 36 2.10626365600615e-19 4.21252731201231e-19 1 37 2.14699146521360e-20 4.29398293042721e-20 1 38 5.43576910092493e-21 1.08715382018499e-20 1 39 9.28863572439242e-22 1.85772714487848e-21 1 40 1.13047766784228e-22 2.26095533568456e-22 1 41 1.35456292383505e-23 2.7091258476701e-23 1 42 2.38239935429932e-24 4.76479870859865e-24 1 43 1.89194354945081e-24 3.78388709890162e-24 1 44 2.12398598961081e-25 4.24797197922161e-25 1 45 1.84166780311256e-26 3.68333560622512e-26 1 46 1.53492225862021e-27 3.06984451724041e-27 1 47 1.70252919673763e-28 3.40505839347525e-28 1 48 1.79641669794382e-29 3.59283339588764e-29 1 49 2.68275820092435e-30 5.3655164018487e-30 1 50 2.48817973018719e-25 4.97635946037437e-25 1 51 2.54320504093808e-14 5.08641008187616e-14 0.999999999999975 52 8.4924914352189e-08 1.69849828704378e-07 0.999999915075086 53 0.00208793830807880 0.00417587661615759 0.997912061691921 54 0.126552874258153 0.253105748516306 0.873447125741847 55 0.682259560489034 0.635480879021931 0.317740439510966 56 0.999896972060409 0.000206055879182144 0.000103027939591072 57 1 3.24752483261800e-16 1.62376241630900e-16 58 1 2.24001114919449e-32 1.12000557459725e-32 59 1 2.60339958157354e-31 1.30169979078677e-31 60 1 2.40314845628714e-30 1.20157422814357e-30 61 1 1.36136802019672e-29 6.80684010098358e-30 62 1 2.54995383184211e-28 1.27497691592106e-28 63 1 4.20737971089322e-27 2.10368985544661e-27 64 1 7.21494947053017e-26 3.60747473526509e-26 65 1 1.18799809972355e-24 5.93999049861777e-25 66 1 1.94093969041489e-23 9.70469845207445e-24 67 1 1.67910101984035e-22 8.39550509920173e-23 68 1 8.17721583438274e-26 4.08860791719137e-26 69 1 1.76785857001911e-24 8.83929285009557e-25 70 1 1.97888067636391e-23 9.89440338181953e-24 71 1 3.27386382710771e-22 1.63693191355386e-22 72 1 7.11984719262795e-21 3.55992359631398e-21 73 1 1.78279113974635e-19 8.91395569873177e-20 74 1 3.94914027931354e-18 1.97457013965677e-18 75 1 7.33700311344483e-17 3.66850155672241e-17 76 1 1.99774320914338e-16 9.9887160457169e-17 77 0.999999999999998 4.36018178848758e-15 2.18009089424379e-15 78 0.999999999999951 9.70281835724196e-14 4.85140917862098e-14 79 0.9999999999989 2.2006248700579e-12 1.10031243502895e-12 80 0.999999999976035 4.79290849913067e-11 2.39645424956534e-11 81 0.999999999492088 1.01582400064581e-09 5.07912000322905e-10 82 0.999999997757058 4.48588324121214e-09 2.24294162060607e-09 83 0.999999989734778 2.05304444338058e-08 1.02652222169029e-08 84 0.999999765794154 4.68411691411482e-07 2.34205845705741e-07 85 0.999994328188056 1.13436238873769e-05 5.67181194368847e-06 86 0.9998879380473 0.000224123905400258 0.000112061952700129 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 67 0.971014492753623 NOK 5% type I error level 67 0.971014492753623 NOK 10% type I error level 67 0.971014492753623 NOK

Charts produced by software:

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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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