werkloosheid - Europa

*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: Fri, 19 Dec 2008 15:27:01 -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/19/t1229725970bzj6agxxlv15jyp.htm/, Retrieved Fri, 19 Dec 2008 23:32:50 +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/19/t1229725970bzj6agxxlv15jyp.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:

Dataseries X:

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180144 8955,5 173666 10423,9 165688 11617,2 161570 9391,1 156145 10872 153730 10230,4 182698 9221 200765 9428,6 176512 10934,5 166618 10986 158644 11724,6 159585 11180,9 163095 11163,2 159044 11240,9 155511 12107,1 153745 10762,3 150569 11340,4 150605 11266,8 179612 9542,7 194690 9227,7 189917 10571,9 184128 10774,4 175335 10392,8 179566 9920,2 181140 9884,9 177876 10174,5 175041 11395,4 169292 10760,2 166070 10570,1 166972 10536 206348 9902,6 215706 8889 202108 10837,3 195411 11624,1 193111 10509 195198 10984,9 198770 10649,1 194163 10855,7 190420 11677,4 189733 10760,2 186029 10046,2 191531 10772,8 232571 9987,7 243477 8638,7 227247 11063,7 217859 11855,7 208679 10684,5 213188 11337,4 216234 10478 213586 11123,9 209465 12909,3 204045 11339,9 200237 10462,2 203666 12733,5 241476 10519,2 260307 10414,9 243324 12476,8 244460 12384,6 233575 12266,7 237217 12919,9 235243 11497,3 230354 12142 227184 13919,4 221678 12656,8 217142 12034,1 219452 13199,7 256446 10881,3 265845 11301,2 248624 13643,9 241114 12517 229245 13981,1 231805 14275,7 219277 13435 219313 13565,7 212610 16216,3 214771 12970 211142 14079,9 211457 14235 240048 12213,4 240636 12581 230580 14130,4 208795 14210,8 197922 14378,5 194596 13142,8

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 8 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Multiple Linear Regression - Estimated Regression Equation werkloosheid[t] = + 217808.739352694 -6.19368899531241Europa[t] + 3991.80149233433M1[t] + 2150.08058275451M2[t] + 5487.86504845769M3[t] -8641.81126884436M4[t] -13100.2472264452M5[t] -9708.44343219584M6[t] + 14153.1279875561M7[t] + 23112.6435492637M8[t] + 18835.3878057790M9[t] + 9682.32094298233M10[t] -730.588482714191M11[t] + 1206.21536731755t + 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) 217808.739352694 20018.015565 10.8806 0 0 Europa -6.19368899531241 1.960306 -3.1596 0.002334 0.001167 M1 3991.80149233433 7028.934575 0.5679 0.571913 0.285957 M2 2150.08058275451 6930.068088 0.3103 0.75729 0.378645 M3 5487.86504845769 7356.344356 0.746 0.458163 0.229081 M4 -8641.81126884436 6952.600968 -1.243 0.21803 0.109015 M5 -13100.2472264452 6936.887136 -1.8885 0.063104 0.031552 M6 -9708.44343219584 6914.565218 -1.4041 0.164723 0.082362 M7 14153.1279875561 7442.105996 1.9018 0.061318 0.030659 M8 23112.6435492637 7679.228908 3.0098 0.003633 0.001817 M9 18835.3878057790 6904.916167 2.7278 0.008053 0.004027 M10 9682.32094298233 6908.108294 1.4016 0.165457 0.082728 M11 -730.588482714191 6900.239494 -0.1059 0.915981 0.457991 t 1206.21536731755 106.179062 11.3602 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.915447709162076 R-squared 0.838044508210093 Adjusted R-squared 0.807967059734824 F-TEST (value) 27.8628856732706 F-TEST (DF numerator) 13 F-TEST (DF denominator) 70 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 12906.1317316674 Sum Squared Residuals 11659776539.2606 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 180144 167539.174414826 12604.8255851738 2 173666 157808.855951847 15857.1440481530 3 165688 154961.926706762 10726.0732932385 4 161570 155826.236829242 5743.76317075806 5 156145 143401.782205800 12743.2177941995 6 153730 151973.67222676 1756.32777324013 7 182698 183293.368685698 -595.368685697753 8 200765 192173.289779296 8591.71022070399 9 176512 179775.173145088 -3263.17314508788 10 166618 171509.346666350 -4891.34666635019 11 158644 157727.993916033 916.006083966527 12 159585 163032.306472817 -3447.30647281658 13 163095 168339.951627685 -5244.95162768549 14 159044 167223.196450487 -8179.19645048744 15 155511 166402.222875769 -10891.2228757686 16 153745 161808.034886680 -8063.0348866802 17 150569 154975.242688207 -4406.2426882068 18 150605 160029.117359829 -9424.11735982873 19 179612 195775.443343716 -16163.4433437164 20 194690 207892.186306265 -13202.1863062649 21 189917 196495.589182599 -6578.58918259878 22 184128 187294.515665569 -3166.51566556893 23 175335 180451.333327801 -5116.33332780119 24 179566 185315.274597018 -5749.27459701758 25 181140 190731.928678204 -9591.92867820398 26 177876 188302.730802899 -10426.7308028992 27 175041 185284.855741543 -10243.8557415431 28 169292 176295.626041381 -7003.626041381 29 166070 174220.825729107 -8150.82572910657 30 166972 179030.049685414 -12058.0496854137 31 206348 208020.919082114 -1672.91908211409 32 215706 224464.573176788 -8758.57317678787 33 202108 209326.368531054 -7218.36853105351 34 195411 196506.322534063 -1095.32253406261 35 193111 194206.211074357 -1095.21107435652 36 195198 193195.438331519 2002.56166848091 37 198770 200473.295955797 -1703.29595579686 38 194163 198558.174267103 -4395.17426710306 39 190420 198012.819852676 -7592.8198526756 40 189733 190770.210449192 -1037.21044919163 41 186029 191940.283801561 -5911.28380156138 42 191531 192037.968539134 -506.968539134335 43 232571 221968.420556424 10602.5794435764 44 243477 240489.437940125 2987.56205987483 45 227247 222398.701750325 4848.29824967458 46 217859 209546.448570559 8312.55142944111 47 208679 207593.80306349 1085.19693651018 48 213188 205486.747368482 7701.25263151791 49 216234 216007.620550705 226.379449294546 50 213586 211371.611286371 2214.38871362909 51 209465 204857.398787161 4607.60121283913 52 204045 201654.313346420 2390.68665358033 53 200237 203838.293587322 -3601.29358732206 54 203666 194368.586933836 9297.41306616409 55 241476 233151.059263226 8324.94073677428 56 260307 243962.791954462 16344.2080455381 57 243324 228120.98423886 15203.0157611399 58 244460 220745.190868749 23714.8091312512 59 233575 212268.732742917 21306.2672570829 60 237217 210159.818941211 27057.1810587892 61 235243 224168.977765594 11074.0222344059 62 230354 219540.400928054 10813.5990719460 63 227184 213075.737940806 14108.2620591936 64 221678 207972.428716303 13705.5712836966 65 217142 208577.018263401 8564.9817365989 66 219452 205955.673532032 13496.3264679681 67 256446 245382.908885834 11063.0911141663 68 265845 252947.909805727 12897.0901942729 69 248624 235366.914220242 13257.0857797584 70 241114 234399.73085358 6714.26914641994 71 229245 216124.856737164 13120.1432628358 72 231805 216236.999809177 15568.0001908231 73 219277 226642.051007188 -7365.0510071879 74 219313 225197.030313238 -5884.03031323832 75 212610 213324.038095284 -714.038095283981 76 214771 220507.149730782 -5736.14973078217 77 211142 210380.553724602 761.446275398394 78 211457 214017.931722996 -2560.93172299559 79 240048 251606.880182989 -11558.8801829887 80 240636 259495.811037337 -18859.8110373369 81 230580 246828.268931833 -16248.2689318327 82 208795 238383.444841131 -29588.4448411305 83 197922 228138.069138238 -30216.0691382377 84 194596 237728.414479777 -43132.414479777 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0133867086108278 0.0267734172216556 0.986613291389172 18 0.0082736882755499 0.0165473765510998 0.99172631172445 19 0.00228384009810179 0.00456768019620358 0.997716159901898 20 0.000571379429787221 0.00114275885957444 0.999428620570213 21 0.00267113679104145 0.0053422735820829 0.997328863208959 22 0.00521202908147388 0.0104240581629478 0.994787970918526 23 0.00191160499609947 0.00382320999219894 0.9980883950039 24 0.00075493181691444 0.00150986363382888 0.999245068183086 25 0.000286273120828372 0.000572546241656743 0.999713726879172 26 9.91767784550026e-05 0.000198353556910005 0.999900823221545 27 4.16207633223468e-05 8.32415266446935e-05 0.999958379236678 28 9.26057176410641e-05 0.000185211435282128 0.999907394282359 29 3.55971231213934e-05 7.11942462427869e-05 0.999964402876879 30 2.11154329517782e-05 4.22308659035563e-05 0.999978884567048 31 0.000435295148467372 0.000870590296934744 0.999564704851533 32 0.000273875056305473 0.000547750112610945 0.999726124943695 33 0.000278164367790517 0.000556328735581034 0.99972183563221 34 0.000674074167780353 0.00134814833556071 0.99932592583222 35 0.000538648951840746 0.00107729790368149 0.99946135104816 36 0.00106390865939770 0.00212781731879539 0.998936091340602 37 0.00128267899409166 0.00256535798818332 0.998717321005908 38 0.00131414017846645 0.00262828035693290 0.998685859821534 39 0.00102343763658425 0.0020468752731685 0.998976562363416 40 0.00154576297024974 0.00309152594049948 0.99845423702975 41 0.00135051952896634 0.00270103905793268 0.998649480471034 42 0.00202049770642083 0.00404099541284166 0.99797950229358 43 0.00950316955841536 0.0190063391168307 0.990496830441585 44 0.0087117351005059 0.0174234702010118 0.991288264899494 45 0.0115180654998384 0.0230361309996768 0.988481934500162 46 0.0231587737114028 0.0463175474228057 0.976841226288597 47 0.0186974134537337 0.0373948269074674 0.981302586546266 48 0.0221269783838767 0.0442539567677535 0.977873021616123 49 0.0191382178841063 0.0382764357682126 0.980861782115894 50 0.0201962249694818 0.0403924499389637 0.979803775030518 51 0.0231670989199617 0.0463341978399234 0.976832901080038 52 0.0417016192154414 0.0834032384308827 0.958298380784559 53 0.0693265861876877 0.138653172375375 0.930673413812312 54 0.223591776186716 0.447183552373431 0.776408223813284 55 0.528184980359648 0.943630039280704 0.471815019640352 56 0.62551853443242 0.748962931135161 0.374481465567581 57 0.836749861027396 0.326500277945208 0.163250138972604 58 0.857120515630642 0.285758968738716 0.142879484369358 59 0.829872818936687 0.340254362126625 0.170127181063313 60 0.815105605635542 0.369788788728916 0.184894394364458 61 0.736547873775927 0.526904252448147 0.263452126224073 62 0.660349516394976 0.679300967210049 0.339650483605024 63 0.550932802599528 0.898134394800945 0.449067197400472 64 0.669801266463235 0.66039746707353 0.330198733536765 65 0.623287917045811 0.753424165908378 0.376712082954189 66 0.764951594676194 0.470096810647613 0.235048405323806 67 0.728724447704477 0.542551104591046 0.271275552295523 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 23 0.450980392156863 NOK 5% type I error level 35 0.686274509803922 NOK 10% type I error level 36 0.705882352941177 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229725970bzj6agxxlv15jyp/10j9i21229725606.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229725970bzj6agxxlv15jyp/10j9i21229725606.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229725970bzj6agxxlv15jyp/1tem81229725606.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229725970bzj6agxxlv15jyp/1tem81229725606.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229725970bzj6agxxlv15jyp/2qegv1229725606.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229725970bzj6agxxlv15jyp/2qegv1229725606.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229725970bzj6agxxlv15jyp/3ehz91229725606.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229725970bzj6agxxlv15jyp/3ehz91229725606.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229725970bzj6agxxlv15jyp/4c8hs1229725606.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229725970bzj6agxxlv15jyp/4c8hs1229725606.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229725970bzj6agxxlv15jyp/5su4q1229725606.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229725970bzj6agxxlv15jyp/5su4q1229725606.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229725970bzj6agxxlv15jyp/6jmgn1229725606.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229725970bzj6agxxlv15jyp/6jmgn1229725606.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229725970bzj6agxxlv15jyp/79dhm1229725606.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229725970bzj6agxxlv15jyp/79dhm1229725606.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229725970bzj6agxxlv15jyp/8v7sp1229725606.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229725970bzj6agxxlv15jyp/8v7sp1229725606.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229725970bzj6agxxlv15jyp/98n1p1229725606.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229725970bzj6agxxlv15jyp/98n1p1229725606.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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