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 07:04:00 -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/t1228658738kiswn4cuns06mhr.htm/, Retrieved Sun, 07 Dec 2008 14:05:47 +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/t1228658738kiswn4cuns06mhr.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 5 seconds R Server 'George Udny Yule' @ 72.249.76.132 R Framework error message Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values. Multiple Linear Regression - Estimated Regression Equation Invoer[t] = + 7986.79591415483 + 872.617388223272Werkloosheid[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) 7986.79591415483 1729.168522 4.6189 1.4e-05 7e-06 Werkloosheid 872.617388223272 221.784201 3.9345 0.000174 8.7e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.398505447141509 R-squared 0.158806591401454 Adjusted R-squared 0.148548135199033 F-TEST (value) 15.4805546046949 F-TEST (DF numerator) 1 F-TEST (DF denominator) 82 p-value 0.000173749291904057 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1685.99065390924 Sum Squared Residuals 233090287.775684 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 11554.5 14531.4263258293 -2976.92632582934 2 13182.1 14269.6411093624 -1087.54110936238 3 14800.1 14007.8558928954 792.244107104598 4 12150.7 13833.3324152507 -1682.63241525075 5 14478.2 13571.5471987838 906.652801216235 6 13253.9 13484.2854599614 -230.385459961439 7 12036.8 13920.5941540731 -1883.79415407308 8 12653.2 14356.9028481847 -1703.70284818471 9 14035.4 14182.3793705401 -146.979370540056 10 14571.4 14182.3793705401 389.020629459944 11 15400.9 13920.5941540731 1480.30584592693 12 14283.2 13658.8089376061 624.391062393908 13 14485.3 13484.2854599614 1001.01454003856 14 14196.3 13309.7619823168 886.538017683215 15 15559.1 13309.7619823168 2249.33801768322 16 13767.4 13484.2854599614 283.114540038561 17 14634 13484.2854599614 1149.71454003856 18 14381.1 13222.5002434945 1158.59975650554 19 12509.9 13397.0237211391 -887.123721139112 20 12122.3 13571.5471987838 -1449.24719878377 21 13122.3 13920.5941540731 -798.294154073075 22 13908.7 14531.4263258294 -622.726325829364 23 13456.5 14531.4263258294 -1074.92632582937 24 12441.6 14618.6880646517 -2177.08806465169 25 12953 14618.6880646517 -1665.68806465169 26 13057.2 14444.1645870070 -1386.96458700704 27 14350.1 14356.9028481847 -6.80284818471017 28 13830.2 14182.3793705401 -352.179370540055 29 13755.5 14007.8558928954 -252.355892895402 30 13574.4 13920.5941540731 -346.194154073075 31 12802.6 14531.4263258294 -1728.82632582936 32 11737.3 14618.6880646517 -2881.38806465169 33 13850.2 14793.2115422963 -943.011542296346 34 15081.8 14967.735019941 114.064980058998 35 13653.3 15054.9967587633 -1401.69675876333 36 14019.1 15142.2584975857 -1123.15849758565 37 13962 15229.5202364080 -1267.52023640798 38 13768.7 15142.2584975857 -1373.55849758565 39 14747.1 14967.735019941 -220.635019941001 40 13858.1 14880.4732811187 -1022.37328111867 41 13188 14618.6880646517 -1430.68806465169 42 13693.1 14618.6880646517 -925.588064651692 43 12970 15142.2584975857 -2172.25849758565 44 11392.8 15229.5202364080 -3836.72023640798 45 13985.2 15316.7819752303 -1331.58197523031 46 14994.7 15316.7819752303 -322.081975230309 47 13584.7 15316.7819752303 -1732.08197523031 48 14257.8 15491.3054528750 -1233.50545287496 49 13553.4 15753.0906693419 -2199.69066934195 50 14007.3 15665.8289305196 -1658.52893051962 51 16535.8 15229.5202364080 1306.27976359202 52 14721.4 14531.4263258294 189.973674170635 53 13664.6 14269.6411093624 -605.041109362383 54 16805.9 14531.4263258294 2274.47367417064 55 13829.4 15665.8289305196 -1836.42893051962 56 13735.6 16102.1376246313 -2366.53762463125 57 15870.5 16102.1376246313 -231.637624631255 58 15962.4 15578.5671916973 383.832808302709 59 15744.1 15142.2584975857 601.841502414346 60 16083.7 15229.5202364080 854.179763592017 61 14863.9 15404.0437140526 -540.143714052637 62 15533.1 15491.3054528750 41.7945471250365 63 17473.1 15491.3054528750 1981.79454712503 64 15925.5 15142.2584975857 783.241502414345 65 15573.7 15054.9967587633 518.703241236673 66 17495 14967.735019941 2527.264980059 67 14155.8 15491.3054528750 -1335.50545287496 68 14913.9 15578.5671916973 -664.667191697291 69 17250.4 15665.8289305196 1584.57106948038 70 15879.8 15404.0437140526 475.756285947362 71 17647.8 15316.7819752303 2331.01802476969 72 17749.9 15404.0437140526 2345.85628594736 73 17111.8 15578.5671916973 1533.23280830271 74 16934.8 15578.5671916973 1356.23280830271 75 20280 15491.3054528750 4788.69454712504 76 16238.2 15404.0437140526 834.156285947364 77 17896.1 15229.5202364080 2666.57976359202 78 18089.3 15054.9967587633 3034.30324123667 79 15660 15142.2584975857 517.741502414345 80 16162.4 15054.9967587633 1107.40324123667 81 17850.1 15054.9967587633 2795.10324123667 82 18520.4 14880.4732811187 3639.92671888133 83 18524.7 14880.4732811187 3644.22671888133 84 16843.7 14880.4732811187 1963.22671888133 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.390693585182351 0.781387170364701 0.609306414817649 6 0.284380962416424 0.568761924832848 0.715619037583576 7 0.237631168463707 0.475262336927414 0.762368831536293 8 0.146091715794470 0.292183431588940 0.85390828420553 9 0.124597015872794 0.249194031745588 0.875402984127206 10 0.132829297817412 0.265658595634824 0.867170702182588 11 0.176089627563892 0.352179255127784 0.823910372436108 12 0.117578216641586 0.235156433283172 0.882421783358414 13 0.0754344437934896 0.150868887586979 0.92456555620651 14 0.0466605829809941 0.0933211659619882 0.953339417019006 15 0.0396270652651526 0.0792541305303052 0.960372934734847 16 0.0254082299900565 0.0508164599801131 0.974591770009943 17 0.015584235197155 0.03116847039431 0.984415764802845 18 0.00966027757692278 0.0193205551538456 0.990339722423077 19 0.0141975721027636 0.0283951442055273 0.985802427897236 20 0.0196670372374184 0.0393340744748367 0.980332962762582 21 0.0121746734653281 0.0243493469306562 0.987825326534672 22 0.0089788650809036 0.0179577301618072 0.991021134919096 23 0.0054952409613442 0.0109904819226884 0.994504759038656 24 0.00394703238295878 0.00789406476591756 0.99605296761704 25 0.00244209812661821 0.00488419625323642 0.997557901873382 26 0.00145095585431739 0.00290191170863479 0.998549044145683 27 0.00114989569119880 0.00229979138239761 0.9988501043088 28 0.000650222946894661 0.00130044589378932 0.999349777053105 29 0.000344069812526996 0.000688139625053991 0.999655930187473 30 0.000178642019662663 0.000357284039325326 0.999821357980337 31 0.000117833048353411 0.000235666096706822 0.999882166951647 32 0.000203220168749931 0.000406440337499863 0.99979677983125 33 0.000172490838884585 0.000344981677769170 0.999827509161115 34 0.000396543320341424 0.000793086640682848 0.999603456679659 35 0.000290015650931075 0.000580031301862149 0.999709984349069 36 0.000226106183006464 0.000452212366012928 0.999773893816994 37 0.000168436326561981 0.000336872653123963 0.999831563673438 38 0.000119689798507838 0.000239379597015676 0.999880310201492 39 0.000109962679985161 0.000219925359970322 0.999890037320015 40 7.69386108410007e-05 0.000153877221682001 0.99992306138916 41 7.6054214395826e-05 0.000152108428791652 0.999923945785604 42 6.71717850652602e-05 0.000134343570130520 0.999932828214935 43 8.64643373727668e-05 0.000172928674745534 0.999913535662627 44 0.00107682940702133 0.00215365881404267 0.998923170592979 45 0.001107269642177 0.002214539284354 0.998892730357823 46 0.00137732699721705 0.00275465399443409 0.998622673002783 47 0.00166396184843735 0.0033279236968747 0.998336038151563 48 0.00166885259216173 0.00333770518432345 0.998331147407838 49 0.00203105003278613 0.00406210006557227 0.997968949967214 50 0.00235708146574503 0.00471416293149006 0.997642918534255 51 0.00700047840071982 0.0140009568014396 0.99299952159928 52 0.0088317700967686 0.0176635401935372 0.991168229903231 53 0.0425706099241013 0.0851412198482027 0.957429390075899 54 0.0943260760080223 0.188652152016045 0.905673923991978 55 0.114277344654921 0.228554689309842 0.88572265534508 56 0.121026167398143 0.242052334796286 0.878973832601857 57 0.122958286473628 0.245916572947257 0.877041713526372 58 0.124642974269129 0.249285948538258 0.875357025730871 59 0.135903543811696 0.271807087623392 0.864096456188304 60 0.141861891054984 0.283723782109969 0.858138108945016 61 0.162826960777148 0.325653921554296 0.837173039222852 62 0.160009726873850 0.320019453747699 0.83999027312615 63 0.208154083584519 0.416308167169038 0.791845916415481 64 0.210279497898435 0.420558995796869 0.789720502101566 65 0.242681292213303 0.485362584426607 0.757318707786697 66 0.267928149408431 0.535856298816862 0.732071850591569 67 0.438357445982791 0.876714891965582 0.561642554017209 68 0.558046674146926 0.883906651706148 0.441953325853074 69 0.526584222915556 0.946831554168887 0.473415777084444 70 0.561465787514359 0.877068424971282 0.438534212485641 71 0.534692104752597 0.930615790494807 0.465307895247403 72 0.498523931697688 0.997047863395376 0.501476068302312 73 0.43404691108456 0.86809382216912 0.56595308891544 74 0.384192529347871 0.768385058695741 0.615807470652129 75 0.868827767933737 0.262344464132525 0.131172232066263 76 0.788014738217303 0.423970523565394 0.211985261782697 77 0.826210813020104 0.347578373959792 0.173789186979896 78 0.834314775793968 0.331370448412064 0.165685224206032 79 0.722848772071302 0.554302455857397 0.277151227928698 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 27 0.36 NOK 5% type I error level 36 0.48 NOK 10% type I error level 40 0.533333333333333 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228658738kiswn4cuns06mhr/10m8fu1228658633.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228658738kiswn4cuns06mhr/10m8fu1228658633.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228658738kiswn4cuns06mhr/1liu91228658633.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228658738kiswn4cuns06mhr/1liu91228658633.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228658738kiswn4cuns06mhr/2t2cm1228658633.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228658738kiswn4cuns06mhr/2t2cm1228658633.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228658738kiswn4cuns06mhr/3ymjl1228658633.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228658738kiswn4cuns06mhr/3ymjl1228658633.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228658738kiswn4cuns06mhr/4duak1228658633.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228658738kiswn4cuns06mhr/4duak1228658633.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228658738kiswn4cuns06mhr/5cg4n1228658633.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228658738kiswn4cuns06mhr/5cg4n1228658633.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228658738kiswn4cuns06mhr/6brv41228658633.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228658738kiswn4cuns06mhr/6brv41228658633.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228658738kiswn4cuns06mhr/7v19h1228658633.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228658738kiswn4cuns06mhr/7v19h1228658633.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228658738kiswn4cuns06mhr/8qjs71228658633.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228658738kiswn4cuns06mhr/8qjs71228658633.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228658738kiswn4cuns06mhr/9eoze1228658633.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228658738kiswn4cuns06mhr/9eoze1228658633.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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