werkloosheid/invoer

*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 13:20:41 -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/t12297183593l8hridc1uld4z4.htm/, Retrieved Fri, 19 Dec 2008 21:26:09 +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/t12297183593l8hridc1uld4z4.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:

» Textbox « » Textfile « » CSV «

180144 11554.5 173666 13182.1 165688 14800.1 161570 12150.7 156145 14478.2 153730 13253.9 182698 12036.8 200765 12653.2 176512 14035.4 166618 14571.4 158644 15400.9 159585 14283.2 163095 14485.3 159044 14196.3 155511 15559.1 153745 13767.4 150569 14634 150605 14381.1 179612 12509.9 194690 12122.3 189917 13122.3 184128 13908.7 175335 13456.5 179566 12441.6 181140 12953 177876 13057.2 175041 14350.1 169292 13830.2 166070 13755.5 166972 13574.4 206348 12802.6 215706 11737.3 202108 13850.2 195411 15081.8 193111 13653.3 195198 14019.1 198770 13962 194163 13768.7 190420 14747.1 189733 13858.1 186029 13188 191531 13693.1 232571 12970 243477 11392.8 227247 13985.2 217859 14994.7 208679 13584.7 213188 14257.8 216234 13553.4 213586 14007.3 209465 16535.8 204045 14721.4 200237 13664.6 203666 16405.9 241476 13829.4 260307 13735.6 243324 15870.5 244460 15962.4 233575 15744.1 237217 16083.7 235243 14863.9 230354 15533.1 227184 17473.1 221678 15925.5 217142 15573.7 219452 17495 256446 14155.8 265845 14913.9 248624 17250.4 241114 15879.8 229245 17647.8 231805 17749.9 219277 17111.8 219313 16934.8 212610 20280 214771 16238.2 211142 17896.1 211457 18089.3 240048 15660 240636 16162.4 230580 17850.1 208795 18520.4 197922 18524.7 194596 16843.7

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 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation werkloosheid[t] = + 54343.5099974573 + 9.75359749825224invoer[t] + 7560.87276566773M1[t] + 801.444384644583M2[t] -21987.3490714503M3[t] -6532.03042540236M4[t] -14220.7530266572M5[t] -17939.9916119470M6[t] + 34614.6454133251M7[t] + 48098.8934247995M8[t] + 14910.8927647497M9[t] + 2232.34191230902M10[t] -5342.73470906037M11[t] + 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) 54343.5099974573 24161.849651 2.2491 0.027606 0.013803 invoer 9.75359749825224 1.502485 6.4916 0 0 M1 7560.87276566773 11871.645598 0.6369 0.526248 0.263124 M2 801.444384644583 11819.579176 0.0678 0.94613 0.473065 M3 -21987.3490714503 11897.416206 -1.8481 0.068756 0.034378 M4 -6532.03042540236 11823.311059 -0.5525 0.582361 0.291181 M5 -14220.7530266572 11782.881907 -1.2069 0.231477 0.115738 M6 -17939.9916119470 11773.647729 -1.5237 0.132015 0.066008 M7 34614.6454133251 12036.327107 2.8758 0.005316 0.002658 M8 48098.8934247995 12095.073762 3.9767 0.000166 8.3e-05 M9 14910.8927647497 11770.924363 1.2668 0.209382 0.104691 M10 2232.34191230902 11791.293684 0.1893 0.850381 0.425191 M11 -5342.73470906037 11781.412188 -0.4535 0.65158 0.32579 Multiple Linear Regression - Regression Statistics Multiple R 0.722333110912724 R-squared 0.521765123120854 Adjusted R-squared 0.440936693225787 F-TEST (value) 6.45521784597598 F-TEST (DF numerator) 12 F-TEST (DF denominator) 71 p-value 1.31136665526554e-07 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 22021.0854580189 Sum Squared Residuals 34429902537.2053 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 180144 174602.325056681 5541.67494331886 2 173666 183717.851963813 -10051.8519638128 3 165688 176710.37925989 -11022.3792598900 4 161570 166324.516694069 -4754.51669406849 5 156145 181337.292269996 -25192.2922699958 6 153730 165676.724267596 -11946.7242675957 7 182698 206360.257777745 -23662.2577777450 8 200765 225856.623287142 -25091.6232871421 9 176512 206150.045089177 -29638.0450891766 10 166618 198699.422495799 -32081.422495799 11 158644 199214.95499923 -40570.9549992299 12 159585 193656.093784494 -34071.0937844938 13 163095 203188.168604558 -40093.1686045583 14 159044 193609.95054654 -34565.9505465402 15 155511 184113.359761064 -28602.3597610635 16 153745 182093.157769493 -28348.1577694929 17 150569 182856.902760223 -32287.9027602235 18 150605 176670.979367626 -26065.9793676257 19 179612 210974.684754168 -31362.6847541682 20 194690 220678.43837532 -25988.4383753200 21 189917 197244.035213522 -7327.03521352245 22 184128 192235.713433707 -8107.71343370731 23 175335 180250.060023628 -4915.06002362825 24 179566 175693.868631712 3872.13136828757 25 181140 188242.731157986 -7102.73115798636 26 177876 182499.627636281 -4623.62763628108 27 175041 172321.260385677 2719.73961432347 28 169292 182705.683692383 -13413.6836923831 29 166070 174288.367358009 -8218.3673580089 30 166972 168802.752265786 -1830.75226578559 31 206348 213829.562741907 -7481.56274190661 32 215706 216923.303338493 -1217.30333849286 33 202108 204343.678832500 -2235.67883250028 34 195411 203677.658658907 -8266.658658907 35 193111 182169.568011284 10941.4319887157 36 195198 191080.168685205 4117.83131479466 37 198770 198084.111033723 685.888966277126 38 194163 189439.312256288 4723.68774371244 39 190420 176193.438592483 14226.5614075173 40 189733 182977.809062584 6755.19093741563 41 186029 168753.200777751 17275.7992222493 42 191531 169960.504288828 21570.4957111719 43 232571 215462.314963114 17108.6850368859 44 243477 213563.189000345 29913.8109996551 45 227247 205660.414494764 21586.5855052357 46 217859 202828.120316809 15030.8796831908 47 208679 181500.471222904 27178.5287770958 48 213188 193408.352408038 19779.6475919619 49 216234 194098.791095937 22135.208904063 50 213586 191766.520619371 21819.4793806295 51 209465 193639.698437606 15825.3015623935 52 204045 191398.089782826 12646.9102171745 53 200237 173401.765345418 26835.2346545822 54 203666 196420.063582087 7245.93641791316 55 241476 223844.556653112 17631.443346888 56 260307 236413.917219250 23893.0827807497 57 243324 224048.871858219 19275.1281417807 58 244460 212266.676615868 32193.3233841321 59 233575 202562.38966063 31012.6103393699 60 237217 211217.446080097 25999.5539199031 61 235243 206880.880617397 28362.1193826034 62 230354 206648.559682204 23705.4403177962 63 227184 202781.745372718 24402.2546272817 64 221678 203142.396530471 18535.6034695289 65 217142 192022.358329331 25119.6416706689 66 219452 207042.706617433 12409.2933825667 67 256446 227028.130876542 29417.8691234585 68 265845 247906.581151441 17938.4188485591 69 248624 237507.861046058 11116.1389539424 70 241114 211461.029462512 29652.9705374877 71 229245 221130.313218053 8114.68678194712 72 231805 227468.890231685 4336.10976831517 73 219277 228805.992433718 -9528.99243371779 74 219313 220320.177295504 -1007.17729550398 75 212610 230159.118190563 -17549.1181905625 76 214771 206192.346468175 8578.65353182545 77 211142 214674.113159272 -3532.11315927213 78 211457 212839.269610645 -1382.26961064466 79 240048 241699.492233413 -1651.49223341258 80 240636 260083.947628009 -19447.9476280089 81 230580 243357.093465759 -12777.0934657594 82 208795 237216.379016397 -28421.3790163972 83 197922 229683.242864270 -31761.2428642703 84 194596 218630.180178769 -24034.1801787687 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0134904417990647 0.0269808835981294 0.986509558200935 17 0.00390111696181964 0.00780223392363927 0.99609888303818 18 0.00111016163562929 0.00222032327125858 0.99888983836437 19 0.000266151815616289 0.000532303631232578 0.999733848184384 20 0.000311941539407165 0.00062388307881433 0.999688058460593 21 0.000177012468511322 0.000354024937022644 0.999822987531489 22 0.000318723614001264 0.000637447228002527 0.999681276385999 23 0.000106871730448428 0.000213743460896855 0.999893128269552 24 4.36302625700658e-05 8.72605251401316e-05 0.99995636973743 25 4.08131139786237e-05 8.16262279572474e-05 0.999959186886021 26 2.40982649024485e-05 4.8196529804897e-05 0.999975901735098 27 1.57011176918853e-05 3.14022353837705e-05 0.999984298882308 28 0.000164225348983108 0.000328450697966215 0.999835774651017 29 0.000130155276872729 0.000260310553745458 0.999869844723127 30 0.000212677104081978 0.000425354208163955 0.999787322895918 31 0.00591420860133483 0.0118284172026697 0.994085791398665 32 0.00778514220130603 0.0155702844026121 0.992214857798694 33 0.0189888293009999 0.0379776586019999 0.981011170699 34 0.0600661260184122 0.120132252036824 0.939933873981588 35 0.083080517030531 0.166161034061062 0.916919482969469 36 0.171378355694770 0.342756711389539 0.82862164430523 37 0.302704302858652 0.605408605717304 0.697295697141348 38 0.411191825457168 0.822383650914335 0.588808174542832 39 0.491036363574597 0.982072727149193 0.508963636425403 40 0.627438765711425 0.745122468577151 0.372561234288575 41 0.689439020902822 0.621121958194356 0.310560979097178 42 0.788177580606988 0.423644838786025 0.211822419393012 43 0.890540370518977 0.218919258962046 0.109459629481023 44 0.917698194040607 0.164603611918786 0.0823018059593931 45 0.94418806227564 0.111623875448721 0.0558119377243605 46 0.959103685761836 0.0817926284763279 0.0408963142381639 47 0.964419678175213 0.0711606436495737 0.0355803218247868 48 0.970495479781789 0.0590090404364218 0.0295045202182109 49 0.976592678566008 0.0468146428679847 0.0234073214339924 50 0.982371373876708 0.0352572522465834 0.0176286261232917 51 0.987144051171815 0.0257118976563705 0.0128559488281853 52 0.989758985441343 0.0204820291173145 0.0102410145586573 53 0.996637809408039 0.00672438118392178 0.00336219059196089 54 0.99764038798056 0.00471922403888099 0.00235961201944050 55 0.997622888504716 0.00475422299056752 0.00237711149528376 56 0.996593744201007 0.00681251159798525 0.00340625579899263 57 0.99503611727637 0.00992776544725868 0.00496388272362934 58 0.99336239866484 0.0132752026703208 0.00663760133516038 59 0.988419662131088 0.0231606757378235 0.0115803378689117 60 0.984348120383212 0.0313037592335762 0.0156518796167881 61 0.972146958817378 0.0557060823652435 0.0278530411826217 62 0.949972545115582 0.100054909768837 0.0500274548844184 63 0.917468879896885 0.165062240206229 0.0825311201031147 64 0.860993269708883 0.278013460582233 0.139006730291117 65 0.816047439325898 0.367905121348204 0.183952560674102 66 0.704855485070668 0.590289029858664 0.295144514929332 67 0.570287782410076 0.859424435179848 0.429712217589924 68 0.420147692062089 0.840295384124178 0.579852307937911 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 19 0.358490566037736 NOK 5% type I error level 30 0.566037735849057 NOK 10% type I error level 34 0.641509433962264 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t12297183593l8hridc1uld4z4/10xxf71229718036.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t12297183593l8hridc1uld4z4/10xxf71229718036.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t12297183593l8hridc1uld4z4/1g30c1229718036.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t12297183593l8hridc1uld4z4/1g30c1229718036.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t12297183593l8hridc1uld4z4/20gwp1229718036.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t12297183593l8hridc1uld4z4/20gwp1229718036.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t12297183593l8hridc1uld4z4/3dmrg1229718036.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t12297183593l8hridc1uld4z4/3dmrg1229718036.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t12297183593l8hridc1uld4z4/43zbp1229718036.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t12297183593l8hridc1uld4z4/43zbp1229718036.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t12297183593l8hridc1uld4z4/5y3ut1229718036.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t12297183593l8hridc1uld4z4/5y3ut1229718036.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t12297183593l8hridc1uld4z4/6vikm1229718036.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t12297183593l8hridc1uld4z4/6vikm1229718036.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t12297183593l8hridc1uld4z4/72ihi1229718036.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t12297183593l8hridc1uld4z4/72ihi1229718036.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t12297183593l8hridc1uld4z4/8jud81229718036.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t12297183593l8hridc1uld4z4/8jud81229718036.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t12297183593l8hridc1uld4z4/9b8dr1229718036.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t12297183593l8hridc1uld4z4/9b8dr1229718036.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No 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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