Regressie Prof bach vanaf 1-7-2007

*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: Wed, 10 Dec 2008 06:54:00 -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/10/t1228917442ndz061cp7sr0jkt.htm/, Retrieved Wed, 10 Dec 2008 13:57:22 +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/10/t1228917442ndz061cp7sr0jkt.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 «

13363 0 12530 0 11420 0 10948 0 10173 0 10602 0 16094 0 19631 0 17140 0 14345 0 12632 0 12894 0 11808 0 10673 0 9939 0 9890 0 9283 0 10131 0 15864 0 19283 0 16203 0 13919 0 11937 0 11795 0 11268 0 10522 0 9929 0 9725 0 9372 0 10068 0 16230 0 19115 0 18351 0 16265 0 14103 0 14115 0 13327 0 12618 0 12129 0 11775 0 11493 0 12470 0 20792 0 22337 0 21325 0 18581 0 16475 0 16581 0 15745 0 14453 0 13712 0 13766 0 13336 0 15346 0 24446 0 26178 0 24628 0 21282 0 18850 0 18822 0 18060 0 17536 0 16417 0 15842 0 15188 0 16905 0 25430 0 27962 0 26607 0 23364 0 20827 0 20506 0 19181 0 18016 0 17354 0 16256 0 15770 0 17538 0 26899 0 28915 0 25247 0 22856 0 19980 0 19856 0 16994 0 16839 0 15618 0 15883 0 15513 0 17106 0 25272 0 26731 0 22891 0 19583 0 16939 0 16757 0 15435 0 14786 0 13680 0 13208 0 12707 0 14277 0 22436 1 23229 1 18241 1 16145 1 13994 1 14780 1 13100 1 12329 1 12463 1 11532 1 10784 1 13106 1 19491 1 20418 1 16094 1 14491 1 13067 1

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 6 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation NWWZPB[t] = + 12013.6749808669 -7096.88669095463Dummy[t] -1067.34863116660M1[t] -1948.72976424651M2[t] -2796.31089732643M3[t] -3263.39203040634M4[t] -3867.47316348625M5[t] -2557.95429656616M6[t] + 5608.75323944939M7[t] + 7609.77210636948M8[t] + 4819.09097328957M9[t] + 2146.00984020965M10[t] -140.171292870259M11[t] + 83.481133079912t + 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) 12013.6749808669 756.170917 15.8875 0 0 Dummy -7096.88669095463 660.983306 -10.7369 0 0 M1 -1067.34863116660 915.549309 -1.1658 0.246336 0.123168 M2 -1948.72976424651 915.361475 -2.1289 0.035599 0.0178 M3 -2796.31089732643 915.222563 -3.0553 0.00285 0.001425 M4 -3263.39203040634 915.132596 -3.566 0.000547 0.000274 M5 -3867.47316348625 915.091588 -4.2263 5.1e-05 2.5e-05 M6 -2557.95429656616 915.099546 -2.7953 0.006167 0.003083 M7 5608.75323944939 916.71975 6.1183 0 0 M8 7609.77210636948 916.533981 8.3028 0 0 M9 4819.09097328957 916.397072 5.2587 1e-06 0 M10 2146.00984020965 916.309045 2.342 0.021067 0.010533 M11 -140.171292870259 916.269916 -0.153 0.878707 0.439354 t 83.481133079912 6.693958 12.4711 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.915533895859757 R-squared 0.838202314468144 Adjusted R-squared 0.818170220068962 F-TEST (value) 41.842969475142 F-TEST (DF numerator) 13 F-TEST (DF denominator) 105 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1991.58481829619 Sum Squared Residuals 416473059.289126 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 13363 11029.8074827802 2333.19251721979 2 12530 10231.9074827802 2298.09251721979 3 11420 9467.80748278021 1952.19251721979 4 10948 9084.20748278021 1863.79251721979 5 10173 8563.60748278021 1609.39251721979 6 10602 9956.60748278021 645.392517219787 7 16094 18206.7961518757 -2112.79615187568 8 19631 20291.2961518757 -660.296151875674 9 17140 17584.0961518757 -444.096151875674 10 14345 14994.4961518757 -649.49615187567 11 12632 12791.7961518757 -159.796151875674 12 12894 13015.4485778258 -121.448577825846 13 11808 12031.5810797392 -223.581079739156 14 10673 11233.6810797392 -560.681079739159 15 9939 10469.5810797392 -530.581079739157 16 9890 10085.9810797392 -195.981079739158 17 9283 9565.38107973916 -282.381079739158 18 10131 10958.3810797392 -827.381079739157 19 15864 19208.5697488346 -3344.56974883462 20 19283 21293.0697488346 -2010.06974883462 21 16203 18585.8697488346 -2382.86974883462 22 13919 15996.2697488346 -2077.26974883462 23 11937 13793.5697488346 -1856.56974883462 24 11795 14017.2221747848 -2222.22217478479 25 11268 13033.3546766981 -1765.35467669810 26 10522 12235.4546766981 -1713.4546766981 27 9929 11471.3546766981 -1542.3546766981 28 9725 11087.7546766981 -1362.75467669810 29 9372 10567.1546766981 -1195.15467669810 30 10068 11960.1546766981 -1892.15467669810 31 16230 20210.3433457936 -3980.34334579356 32 19115 22294.8433457936 -3179.84334579356 33 18351 19587.6433457936 -1236.64334579357 34 16265 16998.0433457936 -733.043345793566 35 14103 14795.3433457936 -692.343345793565 36 14115 15018.9957717437 -903.995771743737 37 13327 14035.1282736570 -708.128273657047 38 12618 13237.2282736570 -619.228273657046 39 12129 12473.1282736570 -344.128273657047 40 11775 12089.5282736570 -314.528273657046 41 11493 11568.9282736570 -75.9282736570467 42 12470 12961.9282736570 -491.928273657046 43 20792 21212.1169427525 -420.11694275251 44 22337 23296.6169427525 -959.616942752508 45 21325 20589.4169427525 735.58305724749 46 18581 17999.8169427525 581.18305724749 47 16475 15797.1169427525 677.88305724749 48 16581 16020.7693687027 560.230631297319 49 15745 15036.901870616 708.098129384009 50 14453 14239.001870616 213.998129384010 51 13712 13474.901870616 237.098129384009 52 13766 13091.301870616 674.69812938401 53 13336 12570.701870616 765.298129384009 54 15346 13963.701870616 1382.29812938401 55 24446 22213.8905397115 2232.10946028855 56 26178 24298.3905397115 1879.60946028854 57 24628 21591.1905397115 3036.80946028854 58 21282 19001.5905397115 2280.40946028855 59 18850 16798.8905397115 2051.10946028855 60 18822 17022.5429656616 1799.45703433838 61 18060 16038.6754675749 2021.32453242506 62 17536 15240.7754675749 2295.22453242507 63 16417 14476.6754675749 1940.32453242506 64 15842 14093.0754675749 1748.92453242506 65 15188 13572.4754675749 1615.52453242506 66 16905 14965.4754675749 1939.52453242506 67 25430 23215.6641366704 2214.3358633296 68 27962 25300.1641366704 2661.8358633296 69 26607 22592.9641366704 4014.0358633296 70 23364 20003.3641366704 3360.6358633296 71 20827 17800.6641366704 3026.3358633296 72 20506 18024.3165626206 2481.68343737943 73 19181 17040.4490645339 2140.55093546612 74 18016 16242.5490645339 1773.45093546612 75 17354 15478.4490645339 1875.55093546612 76 16256 15094.8490645339 1161.15093546612 77 15770 14574.2490645339 1195.75093546612 78 17538 15967.2490645339 1570.75093546612 79 26899 24217.4377336293 2681.56226637066 80 28915 26301.9377336293 2613.06226637065 81 25247 23594.7377336293 1652.26226637066 82 22856 21005.1377336293 1850.86226637066 83 19980 18802.4377336293 1177.56226637066 84 19856 19026.0901595795 829.909840420486 85 16994 18042.2226614928 -1048.22266149282 86 16839 17244.3226614928 -405.322661492825 87 15618 16480.2226614928 -862.222661492824 88 15883 16096.6226614928 -213.622661492824 89 15513 15576.0226614928 -63.0226614928238 90 17106 16969.0226614928 136.977338507177 91 25272 25219.2113305883 52.7886694117137 92 26731 27303.7113305883 -572.711330588288 93 22891 24596.5113305883 -1705.51133058829 94 19583 22006.9113305883 -2423.91133058829 95 16939 19804.2113305883 -2865.21133058829 96 16757 20027.8637565385 -3270.86375653846 97 15435 19043.9962584518 -3608.99625845177 98 14786 18246.0962584518 -3460.09625845177 99 13680 17481.9962584518 -3801.99625845177 100 13208 17098.3962584518 -3890.39625845177 101 12707 16577.7962584518 -3870.79625845177 102 14277 17970.7962584518 -3693.79625845177 103 22436 19124.0982365926 3311.9017634074 104 23229 21208.5982365926 2020.4017634074 105 18241 18501.3982365926 -260.398236592601 106 16145 15911.7982365926 233.201763407399 107 13994 13709.0982365926 284.901763407399 108 14780 13932.7506625428 847.249337457228 109 13100 12948.8831644561 151.116835543917 110 12329 12150.9831644561 178.016835543918 111 12463 11386.8831644561 1076.11683554392 112 11532 11003.2831644561 528.716835543918 113 10784 10482.6831644561 301.316835543918 114 13106 11875.6831644561 1230.31683554392 115 19491 20125.8718335515 -634.871833551547 116 20418 22210.3718335515 -1792.37183355154 117 16094 19503.1718335515 -3409.17183355154 118 14491 16913.5718335515 -2422.57183355155 119 13067 14710.8718335515 -1643.87183355155 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00486096830709992 0.00972193661419984 0.9951390316929 18 0.00256413701460361 0.00512827402920722 0.997435862985396 19 0.00151481370350873 0.00302962740701746 0.998485186296491 20 0.000533831559975432 0.00106766311995086 0.999466168440025 21 0.000112253669698707 0.000224507339397414 0.999887746330301 22 3.28764127045154e-05 6.57528254090308e-05 0.999967123587295 23 6.95596345394575e-06 1.39119269078915e-05 0.999993044036546 24 1.44843737696234e-06 2.89687475392468e-06 0.999998551562623 25 2.66598915131778e-07 5.33197830263555e-07 0.999999733401085 26 5.65845555656118e-08 1.13169111131224e-07 0.999999943415444 27 2.02946688290276e-08 4.05893376580552e-08 0.999999979705331 28 6.89875376882125e-09 1.37975075376425e-08 0.999999993101246 29 4.89490309384618e-09 9.78980618769237e-09 0.999999995105097 30 3.33309108725183e-09 6.66618217450366e-09 0.999999996666909 31 1.91546219744602e-08 3.83092439489204e-08 0.999999980845378 32 1.37144544591753e-08 2.74289089183506e-08 0.999999986285546 33 1.99760676710620e-06 3.99521353421239e-06 0.999998002393233 34 4.78009425052581e-05 9.56018850105161e-05 0.999952199057495 35 0.000158637377909835 0.00031727475581967 0.99984136262209 36 0.000345314772388806 0.000690629544777612 0.999654685227611 37 0.000359949840630035 0.000719899681260069 0.99964005015937 38 0.000396381217786054 0.000792762435572109 0.999603618782214 39 0.000492162585677177 0.000984325171354354 0.999507837414323 40 0.000505775308933208 0.00101155061786642 0.999494224691067 41 0.000584369569415236 0.00116873913883047 0.999415630430585 42 0.000986669711499048 0.00197333942299810 0.9990133302885 43 0.0200569596785933 0.0401139193571866 0.979943040321407 44 0.0476100591403053 0.0952201182806105 0.952389940859695 45 0.0993221581198008 0.198644316239602 0.9006778418802 46 0.151386130095855 0.30277226019171 0.848613869904145 47 0.199996560983109 0.399993121966218 0.800003439016891 48 0.263578825532751 0.527157651065502 0.736421174467249 49 0.276702605140213 0.553405210280427 0.723297394859787 50 0.313612252521261 0.627224505042521 0.68638774747874 51 0.368754017410809 0.737508034821619 0.631245982589191 52 0.417066225668761 0.834132451337523 0.582933774331239 53 0.481868777826315 0.96373755565263 0.518131222173685 54 0.616581560022137 0.766836879955727 0.383418439977863 55 0.884476205326852 0.231047589346297 0.115523794673148 56 0.955803081429355 0.0883938371412891 0.0441969185706446 57 0.971717723674694 0.0565645526506115 0.0282822763253057 58 0.979711922052626 0.0405761558947471 0.0202880779473736 59 0.985303281751708 0.0293934364965843 0.0146967182482921 60 0.990831096995237 0.0183378060095256 0.0091689030047628 61 0.990322882417483 0.0193542351650342 0.00967711758251708 62 0.990146946073667 0.0197061078526659 0.00985305392633293 63 0.991800510611275 0.0163989787774492 0.00819948938872459 64 0.99398228010429 0.0120354397914216 0.00601771989571078 65 0.99691370456023 0.00617259087953928 0.00308629543976964 66 0.999249890590902 0.00150021881819631 0.000750109409098156 67 0.99994443079018 0.000111138419640026 5.55692098200131e-05 68 0.99998419100443 3.16179911409395e-05 1.58089955704697e-05 69 0.999985660013148 2.86799737031082e-05 1.43399868515541e-05 70 0.99997745269624 4.50946075205582e-05 2.25473037602791e-05 71 0.999961515764022 7.6968471956443e-05 3.84842359782215e-05 72 0.999940187969957 0.000119624060085567 5.98120300427837e-05 73 0.999882756417788 0.000234487164423392 0.000117243582211696 74 0.999820894548636 0.000358210902727367 0.000179105451363684 75 0.999728579050468 0.000542841899063554 0.000271420949531777 76 0.999825097998438 0.000349804003123109 0.000174902001561555 77 0.9999208104124 0.000158379175199604 7.91895875998022e-05 78 0.999986863535236 2.62729295269546e-05 1.31364647634773e-05 79 0.999980059500226 3.98809995475258e-05 1.99404997737629e-05 80 0.999960684047477 7.86319050468587e-05 3.93159525234294e-05 81 0.999960403172794 7.91936544119184e-05 3.95968272059592e-05 82 0.999965527068617 6.89458627653212e-05 3.44729313826606e-05 83 0.999938822927656 0.000122354144688251 6.11770723441255e-05 84 0.999891969939312 0.000216060121375740 0.000108030060687870 85 0.999904789957454 0.00019042008509188 9.521004254594e-05 86 0.999845348561606 0.000309302876788267 0.000154651438394134 87 0.999873568516877 0.000252862966245434 0.000126431483122717 88 0.99975698117681 0.000486037646378173 0.000243018823189087 89 0.999505009983863 0.000989980032273658 0.000494990016136829 90 0.999027228011466 0.00194554397706877 0.000972771988534383 91 0.998150983119239 0.00369803376152243 0.00184901688076122 92 0.99822526222911 0.00354947554177889 0.00177473777088944 93 0.99993787151327 0.000124256973461928 6.21284867309642e-05 94 0.999974166731089 5.16665378229629e-05 2.58332689114814e-05 95 0.999950884206613 9.8231586774798e-05 4.9115793387399e-05 96 0.999864866934758 0.000270266130483497 0.000135133065241749 97 0.999724554014814 0.000550891970371158 0.000275445985185579 98 0.999566832779332 0.000866334441336232 0.000433167220668116 99 0.99864463821873 0.00271072356254151 0.00135536178127076 100 0.99532877953922 0.00934244092155953 0.00467122046077977 101 0.986627470465112 0.0267450590697767 0.0133725295348884 102 0.9534849200433 0.0930301599133983 0.0465150799566992 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 62 0.72093023255814 NOK 5% type I error level 71 0.825581395348837 NOK 10% type I error level 75 0.872093023255814 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

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