invoer - werkloosheid

*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, 07 Dec 2008 07:27:22 -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/07/t1228660181rmgqdr7grl9uk7a.htm/, Retrieved Sun, 07 Dec 2008 14:29:42 +0000

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/07/t1228660181rmgqdr7grl9uk7a.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}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

Feedback Forum:

2008-11-27 13:41:43 [a2386b643d711541400692649981f2dc]  [reply]  test Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

11554.5 7.5 13182.1 7.2 14800.1 6.9 12150.7 6.7 14478.2 6.4 13253.9 6.3 12036.8 6.8 12653.2 7.3 14035.4 7.1 14571.4 7.1 15400.9 6.8 14283.2 6.5 14485.3 6.3 14196.3 6.1 15559.1 6.1 13767.4 6.3 14634 6.3 14381.1 6 12509.9 6.2 12122.3 6.4 13122.3 6.8 13908.7 7.5 13456.5 7.5 12441.6 7.6 12953 7.6 13057.2 7.4 14350.1 7.3 13830.2 7.1 13755.5 6.9 13574.4 6.8 12802.6 7.5 11737.3 7.6 13850.2 7.8 15081.8 8 13653.3 8.1 14019.1 8.2 13962 8.3 13768.7 8.2 14747.1 8 13858.1 7.9 13188 7.6 13693.1 7.6 12970 8.2 11392.8 8.3 13985.2 8.4 14994.7 8.4 13584.7 8.4 14257.8 8.6 13553.4 8.9 14007.3 8.8 16535.8 8.3 14721.4 7.5 13664.6 7.2 16805.9 7.5 13829.4 8.8 13735.6 9.3 15870.5 9.3 15962.4 8.7 15744.1 8.2 16083.7 8.3 14863.9 8.5 15533.1 8.6 17473.1 8.6 15925.5 8.2 15573.7 8.1 17495 8 14155.8 8.6 14913.9 8.7 17250.4 8.8 15879.8 8.5 17647.8 8.4 17749.9 8.5 17111.8 8.7 16934.8 8.7 20280 8.6 16238.2 8.5 17896.1 8.3 18089.3 8.1 15660 8.2 16162.4 8.1 17850.1 8.1 18520.4 7.9 18524.7 7.9 16843.7 7.9

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 7 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Multiple Linear Regression - Estimated Regression Equation Invoer[t] = + 18263.6991138870 -865.008398021156Werkloosheid[t] -154.337544400460M1[t] -16.7039004027792M2[t] + 1624.38640656512M3[t] -543.895194925334M4[t] -408.547985043754M5[t] + 38.5860188691167M6[t] -1448.31743563298M7[t] -1530.62400940008M8[t] + 358.725599916112M9[t] + 679.002820887032M10[t] + 373.379322027568M11[t] + 77.1653962284749t + 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) 18263.6991138870 1459.564086 12.5131 0 0 Werkloosheid -865.008398021156 209.802535 -4.123 0.000101 5.1e-05 M1 -154.337544400460 488.896239 -0.3157 0.75318 0.37659 M2 -16.7039004027792 485.448118 -0.0344 0.972649 0.486324 M3 1624.38640656512 483.777618 3.3577 0.001274 0.000637 M4 -543.895194925334 486.970763 -1.1169 0.267858 0.133929 M5 -408.547985043754 494.642372 -0.8259 0.411641 0.20582 M6 38.5860188691167 499.190424 0.0773 0.938608 0.469304 M7 -1448.31743563298 482.920155 -2.9991 0.003748 0.001874 M8 -1530.62400940008 483.253754 -3.1673 0.00228 0.00114 M9 358.725599916112 483.936683 0.7413 0.461012 0.230506 M10 679.002820887032 483.103084 1.4055 0.164295 0.082147 M11 373.379322027568 482.371161 0.774 0.441508 0.220754 t 77.1653962284749 6.811534 11.3286 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.891232133751907 R-squared 0.794294716231977 Adjusted R-squared 0.756092306389345 F-TEST (value) 20.7917437539652 F-TEST (DF numerator) 13 F-TEST (DF denominator) 70 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 902.376677379974 Sum Squared Residuals 56999856.7515525 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 11554.5 11698.9639805563 -144.463980556320 2 13182.1 12173.2655401888 1008.83445981119 3 14800.1 14151.0237627915 649.076237208475 4 12150.7 12232.9092371338 -82.2092371337771 5 14478.2 12704.9243626502 1773.27563734982 6 13253.9 13315.7246025936 -61.8246025936413 7 12036.8 11473.4823453094 563.317654690562 8 12653.2 11035.8369687602 1617.36303123977 9 14035.4 13175.3536539091 860.046346090863 10 14571.4 13572.7962711085 998.603728891467 11 15400.9 13603.8406878839 1797.05931211611 12 14283.2 13567.1292814911 716.070718508858 13 14485.3 13662.9588129234 822.34118707661 14 14196.3 14050.7595327538 145.540467246223 15 15559.1 15769.0152359501 -209.915235950150 16 13767.4 13504.8973510839 262.502648916059 17 14634 13717.409957194 916.590042806006 18 14381.1 14501.2118767417 -120.111876741687 19 12509.9 12918.4721388638 -408.572138863831 20 12122.3 12740.3292817210 -618.029281720974 21 13122.3 14360.8409280572 -1238.54092805718 22 13908.7 14152.7776666418 -244.077666641767 23 13456.5 13924.3195640108 -467.819564010779 24 12441.6 13541.6047984096 -1100.00479840957 25 12953 13464.4326502376 -511.432650237585 26 13057.2 13852.2333700680 -795.033370067971 27 14350.1 15656.9899130665 -1306.88991306646 28 13830.2 13738.8753874087 91.324612591286 29 13755.5 14124.389673123 -368.889673123000 30 13574.4 14735.1899130665 -1160.78991306646 31 12802.6 12719.9459761780 82.6540238219735 32 11737.3 12628.3039588373 -891.003958837286 33 13850.2 14421.8172847777 -571.617284777724 34 15081.8 14646.2582223729 435.54177762711 35 13653.3 14331.2992799398 -677.999279939785 36 14019.1 13948.5845143386 70.5154856614242 37 13962 13784.9115263645 177.088473635526 38 13768.7 14086.2114063927 -317.511406392746 39 14747.1 15977.4687891933 -1230.36878919335 40 13858.1 13972.8534237335 -114.753423733488 41 13188 14444.8685492499 -1256.86854924989 42 13693.1 14969.1679493912 -1276.06794939124 43 12970 13040.4248523049 -70.4248523049171 44 11392.8 12948.7828349642 -1555.98283496417 45 13985.2 14828.7970007067 -843.597000706729 46 14994.7 15226.2396179061 -231.539617906124 47 13584.7 14997.7815152751 -1413.08151527513 48 14257.8 14528.5659098718 -270.765909871813 49 13553.4 14191.8912422935 -638.49124229348 50 14007.3 14493.1911223218 -485.891122321751 51 16535.8 16643.9510245287 -108.151024528703 52 14721.4 15244.8415376836 -523.44153768365 53 13664.6 15716.8566632001 -2052.25666320005 54 16805.9 15981.6535439351 824.246456064952 55 13829.4 13447.4045682339 381.995431766079 56 13735.6 13009.7591916847 725.840808315283 57 15870.5 14976.2741972294 894.225802770612 58 15962.4 15892.7218532415 69.6781467585216 59 15744.1 16096.7679496211 -352.667949621066 60 16083.7 15714.0531840199 369.646815980143 61 14863.9 15463.8793562436 -599.979356243642 62 15533.1 15592.1775566677 -59.077556667682 63 17473.1 17310.4332598641 162.666740135943 64 15925.5 15565.3204138105 360.17958618946 65 15573.7 15864.3338597227 -290.633859722709 66 17495 16475.1340996662 1019.86590033383 67 14155.8 14546.3910025799 -390.591002579853 68 14913.9 14454.7489852391 459.151014760889 69 17250.4 16334.7631509817 915.636849018336 70 15879.8 16991.7082875874 -1111.90828758741 71 17647.8 16849.7510247585 798.048975241465 72 17749.9 16467.0362591573 1282.86374084268 73 17111.8 16216.8624313811 894.93756861889 74 16934.8 16431.6614716073 503.138528392734 75 20280 18236.4180146058 2043.58198539425 76 16238.2 16231.8026491459 6.39735085410969 77 17896.1 16617.3169348602 1278.78306513982 78 18089.3 17314.6180146058 774.681985394245 79 15660 15818.37911653 -158.379116530014 80 16162.4 15899.7387787935 262.661221206496 81 17850.1 17866.2537843382 -16.1537843381752 82 18520.4 18436.6980811418 83.7019188582013 83 18524.7 18208.2399785108 316.46002148919 84 16843.7 17912.0260527117 -1068.32605271172 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.522288115652889 0.955423768694221 0.477711884347111 18 0.379337540078398 0.758675080156797 0.620662459921602 19 0.313960847668408 0.627921695336817 0.686039152331592 20 0.60165198863593 0.79669602272814 0.39834801136407 21 0.645299689705895 0.709400620588209 0.354700310294105 22 0.549633793906482 0.900732412187035 0.450366206093518 23 0.490887348021024 0.981774696042048 0.509112651978976 24 0.391917242521526 0.783834485043052 0.608082757478474 25 0.436887429668699 0.873774859337398 0.563112570331301 26 0.359949212729184 0.719898425458367 0.640050787270816 27 0.28176681793424 0.56353363586848 0.71823318206576 28 0.415665082755233 0.831330165510465 0.584334917244767 29 0.394038128226323 0.788076256452645 0.605961871773677 30 0.322513884308082 0.645027768616165 0.677486115691918 31 0.448437375221839 0.896874750443679 0.551562624778161 32 0.382184794412648 0.764369588825295 0.617815205587352 33 0.363422425026138 0.726844850052277 0.636577574973862 34 0.507646498057441 0.984707003885119 0.492353501942559 35 0.439447736088537 0.878895472177073 0.560552263911463 36 0.507432639024557 0.985134721950886 0.492567360975443 37 0.638004789197774 0.723990421604451 0.361995210802225 38 0.63939582047955 0.7212083590409 0.36060417952045 39 0.582610745464301 0.834778509071397 0.417389254535698 40 0.587179469227433 0.825641061545135 0.412820530772567 41 0.580835977603567 0.838328044792866 0.419164022396433 42 0.5734446081854 0.8531107836292 0.4265553918146 43 0.619128586671917 0.761742826656166 0.380871413328083 44 0.605801854353414 0.788396291293172 0.394198145646586 45 0.546856272381971 0.906287455236057 0.453143727618029 46 0.52548492037987 0.949030159240259 0.474515079620130 47 0.579052440315203 0.841895119369595 0.420947559684797 48 0.535833995970488 0.928332008059023 0.464166004029512 49 0.498458669006057 0.996917338012114 0.501541330993943 50 0.450669262814389 0.901338525628777 0.549330737185611 51 0.491435266179165 0.98287053235833 0.508564733820835 52 0.475243972166261 0.950487944332522 0.524756027833739 53 0.51222158848091 0.97555682303818 0.48777841151909 54 0.726825426640819 0.546349146718362 0.273174573359181 55 0.716728534981863 0.566542930036273 0.283271465018137 56 0.712074113922805 0.575851772154391 0.287925886077195 57 0.705612273395522 0.588775453208955 0.294387726604478 58 0.641148441576857 0.717703116846287 0.358851558423143 59 0.569407580268259 0.861184839463482 0.430592419731741 60 0.557452511590022 0.885094976819957 0.442547488409978 61 0.487813521518125 0.97562704303625 0.512186478481875 62 0.392208298817786 0.784416597635572 0.607791701182214 63 0.475425017223803 0.950850034447607 0.524574982776197 64 0.457003518747359 0.914007037494718 0.542996481252641 65 0.416403716191613 0.832807432383225 0.583596283808387 66 0.355724651767915 0.71144930353583 0.644275348232085 67 0.220492731665492 0.440985463330984 0.779507268334508 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 0 0 OK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228660181rmgqdr7grl9uk7a/104uv71228660029.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228660181rmgqdr7grl9uk7a/104uv71228660029.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228660181rmgqdr7grl9uk7a/14h5y1228660029.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228660181rmgqdr7grl9uk7a/14h5y1228660029.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228660181rmgqdr7grl9uk7a/2o5c41228660029.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228660181rmgqdr7grl9uk7a/2o5c41228660029.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228660181rmgqdr7grl9uk7a/38okp1228660029.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228660181rmgqdr7grl9uk7a/38okp1228660029.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228660181rmgqdr7grl9uk7a/4p7vf1228660029.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228660181rmgqdr7grl9uk7a/4p7vf1228660029.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228660181rmgqdr7grl9uk7a/5fqor1228660029.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228660181rmgqdr7grl9uk7a/5fqor1228660029.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228660181rmgqdr7grl9uk7a/6kzsx1228660029.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228660181rmgqdr7grl9uk7a/6kzsx1228660029.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228660181rmgqdr7grl9uk7a/7m5ru1228660029.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228660181rmgqdr7grl9uk7a/7m5ru1228660029.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228660181rmgqdr7grl9uk7a/82ixm1228660029.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228660181rmgqdr7grl9uk7a/82ixm1228660029.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228660181rmgqdr7grl9uk7a/9l1z61228660029.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228660181rmgqdr7grl9uk7a/9l1z61228660029.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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