paper 2 multiple regressie

*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: Sat, 26 Dec 2009 11:49:42 -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/26/t12618534761mjam9tjwkpbab7.htm/, Retrieved Sat, 26 Dec 2009 19:51:28 +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/26/t12618534761mjam9tjwkpbab7.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:

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111.6 0 104.6 0 91.6 0 98.3 0 97.7 0 106.3 0 102.3 0 106.6 0 108.1 0 93.8 0 88.2 0 108.9 0 114.2 0 102.5 0 94.2 0 97.4 0 98.5 0 106.5 0 102.9 0 97.1 0 103.7 0 93.4 0 85.8 0 108.6 0 110.2 0 101.2 0 101.2 0 96.9 0 99.4 0 118.7 0 108.0 0 101.2 0 119.9 0 94.8 0 95.3 0 118.0 0 115.9 0 111.4 0 108.2 0 108.8 0 109.5 0 124.8 0 115.3 0 109.5 0 124.2 0 92.9 0 98.4 0 120.9 0 111.7 0 116.1 0 109.4 0 111.7 0 114.3 0 133.7 0 114.3 0 126.5 0 131.0 0 104.0 0 108.9 0 128.5 0 132.4 0 128.0 0 116.4 0 120.9 0 118.6 0 133.1 0 121.1 0 127.6 0 135.4 0 114.9 0 114.3 0 128.9 0 138.9 0 129.4 0 115.0 0 128.0 1 127.0 1 128.8 1 137.9 1 128.4 1 135.9 1 122.2 1 113.1 1 136.2 1 138.0 1 115.2 1 111.0 1 99.2 1 102.4 1 112.7 1 105.5 1 98.3 1 116.4 1 97.4 1 93.3 1 117.4 1

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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Yt[t] = + 103.814355265646 -10.6915674040462Xt_dummy[t] + 3.38103374929093M1[t] -5.04782803932688M2[t] -13.0891898279448M3[t] -10.3441056910569M4[t] -9.9354674796748M5[t] + 1.84817073170732M6[t] -5.68069105691055M7[t] -7.55955284552844M8[t] + 1.99908536585367M9[t] -18.5172764227642M10[t] -20.8961382113821M11[t] + 0.366361788617887t + 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) 103.814355265646 3.429858 30.2678 0 0 Xt_dummy -10.6915674040462 2.89231 -3.6965 0.000394 0.000197 M1 3.38103374929093 4.070678 0.8306 0.408621 0.204311 M2 -5.04782803932688 4.068623 -1.2407 0.218265 0.109132 M3 -13.0891898279448 4.067024 -3.2184 0.001847 0.000924 M4 -10.3441056910569 4.071745 -2.5405 0.012957 0.006478 M5 -9.9354674796748 4.06832 -2.4422 0.01675 0.008375 M6 1.84817073170732 4.06535 0.4546 0.650587 0.325294 M7 -5.68069105691055 4.062834 -1.3982 0.165821 0.08291 M8 -7.55955284552844 4.060775 -1.8616 0.066242 0.033121 M9 1.99908536585367 4.059173 0.4925 0.623692 0.311846 M10 -18.5172764227642 4.058028 -4.5631 1.7e-05 9e-06 M11 -20.8961382113821 4.057341 -5.1502 2e-06 1e-06 t 0.366361788617887 0.043112 8.4979 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.817912848421904 R-squared 0.668981427613633 Adjusted R-squared 0.616502873454818 F-TEST (value) 12.7477107236818 F-TEST (DF numerator) 13 F-TEST (DF denominator) 82 p-value 9.43689570931383e-15 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 8.1142235003832 Sum Squared Residuals 5398.93108716203 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 111.6 107.561750803555 4.03824919644507 2 104.6 99.4992508035545 5.10074919644548 3 91.6 91.8242508035545 -0.224250803554514 4 98.3 94.9356967290603 3.36430327093967 5 97.7 95.7106967290603 1.98930327093968 6 106.3 107.860696729060 -1.56069672906033 7 102.3 100.698196729060 1.60180327093973 8 106.6 99.1856967290603 7.41430327093971 9 108.1 109.110696729060 -1.01069672906031 10 93.8 88.9606967290603 4.83930327093968 11 88.2 86.9481967290603 1.25180327093970 12 108.9 108.210696729060 0.689303270939719 13 114.2 111.958092266969 2.24190773303092 14 102.5 103.895592266969 -1.39559226696918 15 94.2 96.2205922669692 -2.02059226696917 16 97.4 99.332038192475 -1.93203819247493 17 98.5 100.107038192475 -1.60703819247493 18 106.5 112.257038192475 -5.75703819247493 19 102.9 105.094538192475 -2.19453819247494 20 97.1 103.582038192475 -6.48203819247494 21 103.7 113.507038192475 -9.80703819247493 22 93.4 93.357038192475 0.0429618075250712 23 85.8 91.344538192475 -5.54453819247494 24 108.6 112.607038192475 -4.00703819247494 25 110.2 116.354433730384 -6.15443373038376 26 101.2 108.291933730384 -7.0919337303838 27 101.2 100.616933730384 0.583066269616191 28 96.9 103.728379655890 -6.82837965588957 29 99.4 104.503379655890 -5.10337965588957 30 118.7 116.653379655890 2.04662034411043 31 108 109.490879655890 -1.49087965588958 32 101.2 107.978379655890 -6.77837965588957 33 119.9 117.903379655890 1.99662034411043 34 94.8 97.7533796558896 -2.95337965588958 35 95.3 95.7408796558896 -0.440879655889577 36 118 117.003379655890 0.99662034411042 37 115.9 120.750775193798 -4.85077519379839 38 111.4 112.688275193798 -1.28827519379844 39 108.2 105.013275193798 3.18672480620155 40 108.8 108.124721119304 0.675278880695782 41 109.5 108.899721119304 0.600278880695786 42 124.8 121.049721119304 3.75027888069579 43 115.3 113.887221119304 1.41277888069578 44 109.5 112.374721119304 -2.87472111930422 45 124.2 122.299721119304 1.90027888069578 46 92.9 102.149721119304 -9.24972111930421 47 98.4 100.137221119304 -1.73722111930421 48 120.9 121.399721119304 -0.499721119304216 49 111.7 125.147116657213 -13.4471166572130 50 116.1 117.084616657213 -0.984616657213097 51 109.4 109.409616657213 -0.00961665721308802 52 111.7 112.521062582719 -0.821062582718852 53 114.3 113.296062582719 1.00393741728114 54 133.7 125.446062582719 8.25393741728114 55 114.3 118.283562582719 -3.98356258271886 56 126.5 116.771062582719 9.72893741728114 57 131 126.696062582719 4.30393741728113 58 104 106.546062582719 -2.54606258271886 59 108.9 104.533562582719 4.36643741728115 60 128.5 125.796062582719 2.70393741728114 61 132.4 129.543458120628 2.85654187937232 62 128 121.480958120628 6.51904187937227 63 116.4 113.805958120628 2.59404187937227 64 120.9 116.917404046133 3.98259595386651 65 118.6 117.692404046133 0.9075959538665 66 133.1 129.842404046133 3.25759595386650 67 121.1 122.679904046134 -1.57990404613351 68 127.6 121.167404046133 6.4325959538665 69 135.4 131.092404046134 4.3075959538665 70 114.9 110.942404046133 3.95759595386651 71 114.3 108.929904046134 5.3700959538665 72 128.9 130.192404046134 -1.2924040461335 73 138.9 133.939799584042 4.96020041595768 74 129.4 125.877299584042 3.52270041595763 75 115 118.202299584042 -3.20229958404237 76 128 110.622178105502 17.3778218944980 77 127 111.397178105502 15.6028218944980 78 128.8 123.547178105502 5.25282189449803 79 137.9 116.384678105502 21.515321894498 80 128.4 114.872178105502 13.5278218944980 81 135.9 124.797178105502 11.1028218944980 82 122.2 104.647178105502 17.5528218944980 83 113.1 102.634678105502 10.465321894498 84 136.2 123.897178105502 12.302821894498 85 138 127.644573643411 10.3554263565892 86 115.2 119.582073643411 -4.38207364341086 87 111 111.907073643411 -0.907073643410865 88 99.2 115.018519568917 -15.8185195689166 89 102.4 115.793519568917 -13.3935195689166 90 112.7 127.943519568917 -15.2435195689166 91 105.5 120.781019568917 -15.2810195689166 92 98.3 119.268519568917 -20.9685195689166 93 116.4 129.193519568917 -12.7935195689166 94 97.4 109.043519568917 -11.6435195689166 95 93.3 107.031019568917 -13.7310195689166 96 117.4 128.293519568917 -10.8935195689166 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00958825817779522 0.0191765163555904 0.990411741822205 18 0.00151295130037155 0.0030259026007431 0.998487048699628 19 0.000211035520379756 0.000422071040759512 0.99978896447962 20 0.0046862017915729 0.0093724035831458 0.995313798208427 21 0.00201818016707644 0.00403636033415288 0.997981819832924 22 0.000582984406437064 0.00116596881287413 0.999417015593563 23 0.000169318227838988 0.000338636455677976 0.99983068177216 24 4.50921391838976e-05 9.01842783677952e-05 0.999954907860816 25 1.19242550379534e-05 2.38485100759068e-05 0.999988075744962 26 3.01272000662773e-06 6.02544001325546e-06 0.999996987279993 27 4.02382399537543e-05 8.04764799075087e-05 0.999959761760046 28 1.32931689006513e-05 2.65863378013026e-05 0.9999867068311 29 4.61625019143791e-06 9.23250038287582e-06 0.999995383749809 30 7.26256187722001e-05 0.000145251237544400 0.999927374381228 31 3.92193297369082e-05 7.84386594738164e-05 0.999960780670263 32 1.7539687621392e-05 3.5079375242784e-05 0.999982460312379 33 0.000101943578444070 0.000203887156888139 0.999898056421556 34 4.37601635753008e-05 8.75203271506017e-05 0.999956239836425 35 3.17577386308187e-05 6.35154772616374e-05 0.99996824226137 36 2.46862913784352e-05 4.93725827568704e-05 0.999975313708622 37 1.15587860567240e-05 2.31175721134479e-05 0.999988441213943 38 7.06308652081117e-06 1.41261730416223e-05 0.99999293691348 39 7.76459352460209e-06 1.55291870492042e-05 0.999992235406475 40 5.61517115662545e-06 1.12303423132509e-05 0.999994384828843 41 3.53962307994491e-06 7.07924615988981e-06 0.99999646037692 42 3.70598624524423e-06 7.41197249048846e-06 0.999996294013755 43 1.95460446103235e-06 3.90920892206470e-06 0.99999804539554 44 9.83385089960234e-07 1.96677017992047e-06 0.99999901661491 45 7.83529387547875e-07 1.56705877509575e-06 0.999999216470612 46 1.81683930665354e-06 3.63367861330709e-06 0.999998183160693 47 1.12914920837435e-06 2.25829841674869e-06 0.999998870850792 48 6.95580323733278e-07 1.39116064746656e-06 0.999999304419676 49 1.46366329276573e-05 2.92732658553146e-05 0.999985363367072 50 1.77152318480656e-05 3.54304636961313e-05 0.999982284768152 51 1.90743644987284e-05 3.81487289974567e-05 0.999980925635501 52 1.83889201701135e-05 3.67778403402271e-05 0.99998161107983 53 1.79221244334448e-05 3.58442488668895e-05 0.999982077875567 54 3.08605686653066e-05 6.17211373306133e-05 0.999969139431335 55 6.1493113790906e-05 0.000122986227581812 0.99993850688621 56 0.000179741668081528 0.000359483336163056 0.999820258331918 57 0.000235422355356991 0.000470844710713983 0.999764577644643 58 0.00116271324468839 0.00232542648937677 0.998837286755312 59 0.00278397921195035 0.00556795842390069 0.99721602078805 60 0.0110975725509069 0.0221951451018139 0.988902427449093 61 0.102932241663000 0.205864483326001 0.897067758337 62 0.190911542719038 0.381823085438077 0.809088457280962 63 0.464529502779494 0.929059005558988 0.535470497220506 64 0.407784417131028 0.815568834262057 0.592215582868972 65 0.383858129975445 0.76771625995089 0.616141870024555 66 0.318377642378613 0.636755284757227 0.681622357621386 67 0.446469208214303 0.892938416428606 0.553530791785697 68 0.400463948849024 0.800927897698048 0.599536051150976 69 0.332057065757936 0.664114131515871 0.667942934242064 70 0.34287362649683 0.68574725299366 0.65712637350317 71 0.276733009258908 0.553466018517817 0.723266990741092 72 0.595921494694762 0.808157010610476 0.404078505305238 73 0.610091460711672 0.779817078576656 0.389908539288328 74 0.630760183379063 0.738479633241874 0.369239816620937 75 0.5324511149799 0.9350977700402 0.4675488850201 76 0.482005478878709 0.964010957757418 0.517994521121291 77 0.359300010340625 0.718600020681249 0.640699989659375 78 0.41745469628268 0.83490939256536 0.58254530371732 79 0.561187234727816 0.877625530544369 0.438812765272184 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 42 0.666666666666667 NOK 5% type I error level 44 0.698412698412698 NOK 10% type I error level 44 0.698412698412698 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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