*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, 20 Dec 2009 12:57:16 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/20/t12613391238cxm2iueep7n928.htm/, Retrieved Sun, 20 Dec 2009 20:58:57 +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/2009/Dec/20/t12613391238cxm2iueep7n928.htm/}, year = {2009}, } @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 = {2009}, 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 «

156,9 0 136,9 128,7 109,1 0 156,9 136,9 122,3 0 109,1 156,9 123,9 0 122,3 109,1 90,9 0 123,9 122,3 77,9 0 90,9 123,9 120,3 0 77,9 90,9 118,9 0 120,3 77,9 125,5 0 118,9 120,3 98,9 0 125,5 118,9 102,9 0 98,9 125,5 105,9 0 102,9 98,9 117,6 0 105,9 102,9 113,6 0 117,6 105,9 115,9 0 113,6 117,6 118,9 0 115,9 113,6 77,6 0 118,9 115,9 81,2 0 77,6 118,9 123,1 0 81,2 77,6 136,6 0 123,1 81,2 112,1 0 136,6 123,1 95,1 0 112,1 136,6 96,3 0 95,1 112,1 105,7 0 96,3 95,1 115,8 0 105,7 96,3 105,7 0 115,8 105,7 105,7 0 105,7 115,8 111,1 0 105,7 105,7 82,4 0 111,1 105,7 60 0 82,4 111,1 107,3 0 60 82,4 99,3 0 107,3 60 113,5 0 99,3 107,3 108,9 0 113,5 99,3 100,2 0 108,9 113,5 103,9 0 100,2 108,9 138,7 0 103,9 100,2 120,2 0 138,7 103,9 100,2 0 120,2 138,7 143,2 0 100,2 120,2 70,9 0 143,2 100,2 85,2 0 70,9 143,2 133 0 85,2 70,9 136,6 0 133 85,2 117,9 0 136,6 133 106,3 0 117,9 136,6 122,3 0 106,3 117,9 125,5 0 122,3 106,3 148,4 0 125,5 122,3 126,3 0 148,4 125,5 99,6 0 126,3 148,4 14 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation autoprod[t] = + 35.7573039309252 -8.98791834332252crisis[t] + 0.347101915345739autoprod1[t] + 0.372187267732638`autoprod2 `[t] + 12.6743252271334M1[t] -14.5074404099936M2[t] -15.4590446081435M3[t] + 12.4393559876213M4[t] -39.7731547715001M5[t] -33.8408857027352M6[t] + 29.3437475247696M7[t] + 13.1734504311751M8[t] -10.0699472818691M9[t] -24.5358224404104M10[t] -10.2267828010560M11[t] + 0.0667958759252148t + 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) 35.7573039309252 10.200119 3.5056 0.000723 0.000361 crisis -8.98791834332252 4.237576 -2.121 0.036767 0.018383 autoprod1 0.347101915345739 0.098075 3.5391 0.000647 0.000323 `autoprod2 ` 0.372187267732638 0.097093 3.8333 0.000238 0.000119 M1 12.6743252271334 5.686969 2.2287 0.028414 0.014207 M2 -14.5074404099936 5.943831 -2.4408 0.016684 0.008342 M3 -15.4590446081435 6.2038 -2.4919 0.014604 0.007302 M4 12.4393559876213 5.694242 2.1846 0.031611 0.015806 M5 -39.7731547715001 5.927418 -6.71 0 0 M6 -33.8408857027352 7.189936 -4.7067 9e-06 5e-06 M7 29.3437475247696 6.026885 4.8688 5e-06 2e-06 M8 13.1734504311751 6.794125 1.9389 0.055749 0.027875 M9 -10.0699472818691 6.21787 -1.6195 0.108956 0.054478 M10 -24.5358224404104 6.152504 -3.9879 0.000138 6.9e-05 M11 -10.2267828010560 6.164386 -1.659 0.100715 0.050357 t 0.0667958759252148 0.056359 1.1852 0.239169 0.119585 Multiple Linear Regression - Regression Statistics Multiple R 0.880535870435265 R-squared 0.77534341912319 Adjusted R-squared 0.736609525868568 F-TEST (value) 20.0171827300284 F-TEST (DF numerator) 15 F-TEST (DF denominator) 87 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 11.6046182875216 Sum Squared Residuals 11716.0434071200 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 156.9 143.917178602006 12.9828213979938 2 109.1 126.796182743127 -17.6961827431267 3 122.3 116.763648222028 5.5363517779715 4 123.9 131.520038578662 -7.62003857866221 5 90.9 84.84255869409 6.05744130591002 6 77.9 79.982760060743 -2.08276006074291 7 120.3 126.439684429501 -6.13968442950131 8 118.9 120.214869941967 -1.31486994196703 9 125.5 112.333065575228 13.1669344247722 10 98.9 99.703796759068 -0.803796759068003 11 102.9 107.303157293186 -4.40315729318625 12 105.9 109.084962309862 -3.18496230986229 13 117.6 124.356138229889 -6.75613822988865 14 113.6 102.41882268143 11.1811773185700 15 115.9 104.500197730294 11.3998022697059 16 118.9 131.774979536349 -12.8749795363489 17 77.6 81.526601114975 -3.92660111497494 18 81.2 74.306918759084 6.89308124091605 19 123.1 123.436580600401 -0.336580600400741 20 136.6 123.216523799555 13.3834762004446 21 112.1 120.320444337601 -8.22044433760137 22 95.1 102.441896243405 -7.34189624340533 23 96.3 101.798411138358 -5.4984111383577 24 105.7 106.181328562299 -0.481328562298956 25 115.8 122.631832390887 -6.83183239088665 26 105.7 102.521152291364 3.17884770863632 27 105.7 101.889706028247 3.81029397175337 28 111.1 126.095811095837 -14.9958110958371 29 82.4 75.8244465555078 6.57555344449219 30 60 73.8714977755315 -13.8714977755315 31 107.3 118.666069391290 -11.3660693912903 32 99.3 110.643493972263 -11.3434939722633 33 113.5 102.294534576132 11.2054654238678 34 108.9 89.8468043495645 19.0531956504355 35 100.2 107.911030256057 -7.71103025605717 36 103.9 113.472760837960 -9.57276083796033 37 138.7 124.260129798524 14.4398702014758 38 120.2 110.601399581965 9.59860041803507 39 100.2 116.247322742940 -16.0473227429398 40 143.2 130.385016454661 12.8149835453386 41 70.9 85.7209385766791 -14.8209385766791 42 85.2 82.6285875543758 2.57141244562424 43 133 123.934434590180 9.06556540981984 44 136.6 129.744682854614 6.85531714538609 45 117.9 125.608199310360 -7.70819931035963 46 106.3 106.058188374616 0.241811625384228 47 122.3 109.447739765284 12.8522602347156 48 125.5 120.977576782099 4.52242321790112 49 148.4 140.784420297986 7.61557970201397 50 126.3 122.809083654946 3.49091634505382 51 99.6 122.776411434658 -23.176411434658 52 140.4 133.248648149726 7.15135185027444 53 80.3 85.327291364174 -5.02729136417404 54 92.6 85.6507717200769 6.94922827992308 55 138.5 130.803099591528 7.69690040847199 56 110.9 135.209479681340 -24.3094796813396 57 119.6 119.536260569606 0.0637394303937566 58 105 97.8845993610773 7.1154006389227 59 109 110.430776141583 -1.43077614158303 60 129.4 116.678828371051 12.7211716289493 61 148.6 137.989577618093 10.6104223819071 62 101.4 125.131584893275 -23.7315848932752 63 134.8 115.009561707198 19.7904382928018 64 143.7 137.000723114455 6.69927688554452 65 81.6 100.375270020106 -18.7752700201064 66 90.3 88.1317727046467 2.16822729535335 67 141.5 131.290159145388 10.2098408546122 68 140.7 136.196305222694 4.50369477730569 69 140.2 131.798009961210 8.40199003879024 70 100.2 116.927629906735 -16.7276299067347 71 125.7 117.233295174318 8.4667048256816 72 119.6 121.490481983310 -1.89048198331047 73 134.7 141.605056729942 -6.90505672994231 74 109 117.460983557292 -8.46098355729213 75 116.3 113.275683753445 3.02431624655525 76 146.9 134.20951142643 12.6904885735701 77 97.4 95.4020822072616 1.99791779273845 78 89.4 95.6085327349564 -6.20853273495635 79 132.1 137.659876762855 -5.55987676285493 80 139.8 133.400129188588 6.39987081141246 81 129 119.800690088492 9.19930991150816 82 112.5 104.518752081683 7.98124791831687 83 121.9 109.147783502246 12.7522164977545 84 121.7 116.563030265888 5.13696973411184 85 123.1 132.733291302564 -9.6332913025644 86 131.6 106.029826769300 25.5701732306998 87 119.3 108.61644690234 10.6835530976601 88 132.5 135.475881591005 -2.97588159100483 89 98.3 83.3340085972609 14.9659914027391 90 85.1 82.3750599711976 2.72494002880241 91 131.7 128.315939235608 3.38406076439232 92 129.3 123.474515338979 5.82548466102106 93 90.7 116.808795581371 -26.1087955813711 94 78.6 88.1183329238512 -9.5183329238512 95 68.9 83.9278067289675 -15.0278067289675 96 79.1 86.3510308875301 -7.25103088753013 97 83.5 99.0223750301086 -15.5223750301086 98 74.1 77.2309638273011 -3.13096382730107 99 59.7 74.72102147885 -15.02102147885 100 93.3 94.1893900528746 -0.889390052874652 101 61.3 48.3468028699452 12.9531971300548 102 56.6 55.7440987193884 0.855901280611623 103 98.5 105.454156253249 -6.95415625324906 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.765321399441293 0.469357201117415 0.234678600558707 20 0.730668004046507 0.538663991906987 0.269331995953493 21 0.704989448406856 0.590021103186288 0.295010551593144 22 0.591244316166936 0.817511367666127 0.408755683833064 23 0.473529918181376 0.947059836362752 0.526470081818624 24 0.365648410074845 0.73129682014969 0.634351589925155 25 0.320133937858956 0.640267875717912 0.679866062141044 26 0.242938348930563 0.485876697861126 0.757061651069437 27 0.175218144616681 0.350436289233362 0.824781855383319 28 0.137326228321736 0.274652456643473 0.862673771678264 29 0.103923120842523 0.207846241685045 0.896076879157477 30 0.101542655885440 0.203085311770880 0.89845734411456 31 0.0790860777791295 0.158172155558259 0.92091392222087 32 0.0956709537822905 0.191341907564581 0.90432904621771 33 0.0762809573786664 0.152561914757333 0.923719042621334 34 0.155007173731157 0.310014347462315 0.844992826268843 35 0.126185372335376 0.252370744670753 0.873814627664624 36 0.101151636253814 0.202303272507627 0.898848363746186 37 0.13447266637143 0.26894533274286 0.86552733362857 38 0.132323685555095 0.26464737111019 0.867676314444905 39 0.151176606068124 0.302353212136248 0.848823393931876 40 0.286208624181499 0.572417248362998 0.713791375818501 41 0.289635686749978 0.579271373499955 0.710364313250022 42 0.241525350408082 0.483050700816164 0.758474649591918 43 0.26799353995823 0.53598707991646 0.73200646004177 44 0.23940398027761 0.47880796055522 0.76059601972239 45 0.206770197970144 0.413540395940288 0.793229802029856 46 0.160887943561670 0.321775887123339 0.83911205643833 47 0.183560938724512 0.367121877449023 0.816439061275488 48 0.162361131386388 0.324722262772777 0.837638868613612 49 0.13375418451898 0.26750836903796 0.86624581548102 50 0.101530745565797 0.203061491131593 0.898469254434203 51 0.221786541950769 0.443573083901538 0.778213458049231 52 0.19116434164951 0.38232868329902 0.80883565835049 53 0.168314661856766 0.336629323713533 0.831685338143234 54 0.135765109710861 0.271530219421721 0.86423489028914 55 0.114758479798829 0.229516959597657 0.885241520201171 56 0.338402810786039 0.676805621572078 0.661597189213961 57 0.287986868837698 0.575973737675395 0.712013131162302 58 0.236895480432477 0.473790960864953 0.763104519567523 59 0.206850740167094 0.413701480334188 0.793149259832906 60 0.189475002856564 0.378950005713129 0.810524997143436 61 0.179975671853792 0.359951343707584 0.820024328146208 62 0.445438103541239 0.890876207082478 0.554561896458761 63 0.482217576732499 0.964435153464998 0.517782423267501 64 0.448614353216134 0.897228706432268 0.551385646783866 65 0.821640960162495 0.356718079675010 0.178359039837505 66 0.843534546930297 0.312930906139407 0.156465453069704 67 0.83295005946021 0.334099881079579 0.167049940539789 68 0.954480886425243 0.0910382271495133 0.0455191135747567 69 0.945242057990915 0.10951588401817 0.054757942009085 70 0.962109305722449 0.0757813885551026 0.0378906942775513 71 0.950563983724823 0.098872032550354 0.049436016275177 72 0.92505019608296 0.149899607834081 0.0749498039170405 73 0.918508575117163 0.162982849765673 0.0814914248828367 74 0.957117127117252 0.0857657457654954 0.0428828728827477 75 0.943955818492919 0.112088363014163 0.0560441815070813 76 0.939774389717413 0.120451220565173 0.0602256102825867 77 0.917048314515456 0.165903370969087 0.0829516854845437 78 0.86990875795799 0.260182484084021 0.130091242042010 79 0.847541482760126 0.304917034479747 0.152458517239874 80 0.766941839526906 0.466116320946189 0.233058160473094 81 0.667191696552898 0.665616606894203 0.332808303447102 82 0.62275271055227 0.754494578895459 0.377247289447729 83 0.587650960123796 0.824698079752408 0.412349039876204 84 0.47710883245251 0.95421766490502 0.52289116754749 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 4 0.0606060606060606 OK

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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