Time Series Analysis WS8 - Seizoenaliteit

*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, 26 Nov 2010 07:33:09 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/26/t1290756762zc5xqcmjkam1k75.htm/, Retrieved Fri, 26 Nov 2010 08:32:52 +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/2010/Nov/26/t1290756762zc5xqcmjkam1k75.htm/}, year = {2010}, } @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 = {2010}, 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 «

43880 25222 43110 21333 44496 19778 44164 25943 40399 21698 36763 20077 37903 25673 35532 19094 35533 19306 32110 15443 33374 15179 35462 18288 33508 18264 36080 16406 34560 15678 38737 19657 38144 18821 37594 19493 36424 21078 36843 19296 37246 19985 38661 16972 40454 16951 44928 23126 48441 24890 48140 21042 45998 20842 47369 23904 49554 22578 47510 25452 44873 21928 45344 25227 42413 26210 36912 17436 43452 21258 42142 25638 44382 23516 43636 23891 44167 24617 44423 26174 42868 23339 43908 23660 42013 26500 38846 22469 35087 23163 33026 16170 34646 18267 37135 20561 37985 20372 43121 19017 43722 18242 43630 20937 42234 22065 39351 16731 39327 21943 35704 19254 30466 16397 28155 13644 29257 14375 29998 14814 32529 16061 34787 14784 33855 12824 34556 18282 31348 14936 30805 15701 28353 16394 24514 13085 21106 11431 21346 9334 23335 10921 24379 11725 26290 13077 30084 11794 29429 11047 30632 16797 27349 11482 27264 12657 27474 15277 etc...

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 9 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation OPENVAC[t] = + 10623.1942523224 + 1.29571959381036OntvangenJobs[t] + 1477.02153033243M1[t] + 5607.42821315978M2[t] + 6426.25646009834M3[t] + 3206.56291386158M4[t] + 3787.27999096013M5[t] + 3228.35979642518M6[t] -280.371854110935M7[t] + 75.6015046914418M8[t] -1565.32470150871M9[t] + 1889.99201110606M10[t] + 1852.99431323577M11[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) 10623.1942523224 1392.166236 7.6307 0 0 OntvangenJobs 1.29571959381036 0.056976 22.7413 0 0 M1 1477.02153033243 1462.880473 1.0097 0.314757 0.157378 M2 5607.42821315978 1462.230037 3.8348 0.000205 0.000102 M3 6426.25646009834 1464.196018 4.3889 2.5e-05 1.3e-05 M4 3206.56291386158 1465.07752 2.1887 0.030625 0.015312 M5 3787.27999096013 1461.999086 2.5905 0.010813 0.005407 M6 3228.35979642518 1461.585722 2.2088 0.029153 0.014577 M7 -280.371854110935 1467.7304 -0.191 0.848841 0.42442 M8 75.6015046914418 1461.858345 0.0517 0.958844 0.479422 M9 -1565.32470150871 1461.58349 -1.071 0.286402 0.143201 M10 1889.99201110606 1512.653164 1.2495 0.214014 0.107007 M11 1852.99431323577 1502.786067 1.233 0.220054 0.110027 Multiple Linear Regression - Regression Statistics Multiple R 0.91590742978012 R-squared 0.838886419926424 Adjusted R-squared 0.822219497849847 F-TEST (value) 50.3324138717472 F-TEST (DF numerator) 12 F-TEST (DF denominator) 116 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3345.10446353355 Sum Squared Residuals 1298007969.14644 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 43880 44780.8553777397 -900.85537773967 2 43110 43872.2085602386 -762.208560238618 3 44496 42676.1928388021 1819.80716119792 4 44164 47444.6105884062 -3280.61058840621 5 40399 42524.9979897798 -2125.99798977977 6 36763 39865.7163336782 -3102.71633367822 7 37903 43607.8315301049 -5704.83153010488 8 35532 35439.2656812289 92.7343187711064 9 35533 34073.0320289165 1459.96797108346 10 32110 32522.9839506419 -412.983950641875 11 33374 32143.9162800057 1230.08371999434 12 35462 34319.3141839263 1142.68581607370 13 33508 35765.2384440073 -2257.23844400729 14 36080 37488.198121535 -1408.19812153497 15 34560 37363.7425041796 -2803.7425041796 16 38737 39299.7172217143 -562.717221714272 17 38144 38797.2127183874 -653.21271838736 18 37594 39109.016090893 -1515.01609089297 19 36424 37653.9999965463 -1229.99999654628 20 36843 35701.0010391786 1141.99896082141 21 37246 34952.8256331138 2293.17436688622 22 38661 34504.1392095779 4156.86079042208 23 40454 34439.9314002376 6014.06859976239 24 44928 40588.0055787808 4339.99442121916 25 48441 44350.6764725948 4090.32352740525 26 48140 43495.1541584398 4644.84584156019 27 45998 44054.8384866163 1943.16151338369 28 47369 44802.6383366269 2566.36166337312 29 49554 43665.2312323329 5888.76876766711 30 47510 46830.2091504089 679.790849591079 31 44873 38755.3616512851 6117.63834871492 32 45344 43385.9139500679 1958.08604993215 33 42413 43018.6801045833 -605.680104583284 34 36912 35105.3531011059 1806.64689889407 35 43452 40020.5956907788 3431.40430922116 36 42142 43842.8531984325 -1700.85319843246 37 44382 42570.3577506993 1811.64224930069 38 43636 47186.6592812055 -3550.65928120553 39 44167 48946.1799532504 -4779.17995325042 40 44423 47743.9218145764 -3320.9218145764 41 42868 44651.2738432226 -1783.27384322258 42 43908 44508.2796383008 -600.279638300752 43 42013 44679.3916341861 -2666.39163418606 44 38846 39812.3193103389 -966.319310338866 45 35087 39070.6225022431 -3983.62250224311 46 33026 33464.972095342 -438.972095342012 47 34646 36145.0983856921 -1499.09838569205 48 37135 37264.4848206573 -129.484820657254 49 37985 38496.6153477595 -511.615347759532 50 43121 40871.3219809738 2249.67801902617 51 43722 40685.9675427094 3036.03245729063 52 43630 40958.2383017915 2671.76169820846 53 42234 43000.5270807082 -766.527080708174 54 39351 35530.2385727888 3820.76142721125 55 39327 38774.7974451922 552.20255480776 56 35704 35646.5808162386 57.4191837614471 57 30466 30303.7837305222 162.216269477803 58 28155 30191.9844013770 -2036.98440137704 59 29257 31102.1577265821 -1845.15772658212 60 29998 29817.9843150291 180.015684970898 61 32529 32910.7681788431 -381.768178843061 62 34787 35386.5409403746 -599.540940374568 63 33855 33665.7587834448 189.241216555175 64 34556 37518.1027802250 -2962.10278022502 65 31348 33763.3420964341 -2415.3420964341 66 30805 34195.6473911641 -3390.64739116408 67 28353 31584.8494191385 -3231.84941913854 68 24514 27653.2866420224 -3139.28664202243 69 21106 23869.2402276599 -2763.24022765994 70 21346 24607.4329520544 -3261.43295205438 71 23335 26626.7422495611 -3291.74224956113 72 24379 25815.5064897489 -1436.50648974889 73 26290 29044.3409109129 -2754.34091091294 74 30084 31512.3393548816 -1428.33935488158 75 29429 31363.2650652438 -1934.26506524381 76 30632 35593.9591834166 -4961.95918341663 77 27349 29287.9266194131 -1938.92661941311 78 27264 30251.4769476053 -2987.47694760534 79 27474 30137.5306328524 -2663.53063285236 80 24482 26746.2829263552 -2264.28292635517 81 21453 24601.3217981628 -3148.32179816279 82 18788 23390.7522534664 -4602.75225346644 83 19282 24044.3730990971 -4762.37309909708 84 19713 23498.7598560160 -3785.75985601596 85 21917 26959.5280844721 -5042.52808447207 86 23812 28609.9274647464 -4797.92746474637 87 23785 29003.7596849151 -5218.75968491514 88 24696 28109.882809568 -3413.88280956798 89 24562 27451.8919549838 -2889.89195498383 90 23580 26729.7110916288 -3149.71109162877 91 24939 24827.6717374175 111.328262582500 92 23899 26173.574865891 -2274.57486589099 93 21454 23041.2754072151 -1587.27540721512 94 19761 22982.6005814162 -3221.60058141618 95 19815 23623.2642311087 -3808.26423110871 96 20780 25603.008476364 -4823.00847636399 97 23462 25639.1898183793 -2177.18981837931 98 25005 26851.6359759457 -1846.63597594571 99 24725 26899.5110645671 -2174.51106456711 100 26198 26166.3034188524 31.6965811475595 101 27543 27901.506654036 -358.506654036024 102 26471 27923.0688375281 -1452.06883752812 103 26558 25292.8350715954 1265.16492840458 104 25317 25495.9135183282 -178.913518328176 105 22896 22543.7190831919 352.280916808062 106 22248 20284.912387103 1963.08761289699 107 23406 23303.2214914376 102.778508562449 108 25073 24317.6546393041 755.345360695885 109 27691 24616.8670588629 3074.13294113707 110 30599 28285.9975662938 2313.00243370622 111 31948 28858.6390904084 3089.36090959162 112 32946 27424.4471444423 5521.5528555577 113 34012 30461.8485714053 3550.1514285947 114 32936 26812.6371456326 6123.36285436737 115 32974 28520.4725797770 4453.52742022297 116 30951 26833.0961391405 4117.90386085953 117 29812 25036.6835816831 4775.31641831692 118 29010 22961.8690679152 6048.13093208479 119 31068 26639.6994454992 4428.30055450077 120 32447 26989.4284417411 5457.57155825892 121 34844 29794.5625557291 5049.43744427086 122 35676 30490.0165953652 5185.98340463479 123 35387 28554.1449858629 6832.85501413705 124 36488 28777.1784003803 7710.82159961968 125 35652 32159.2412392969 3492.75876070313 126 33488 27913.9988003714 5574.00119962856 127 32914 29917.2583019046 2996.7416980954 128 29781 28325.76511121 1455.23488878999 129 27951 24905.8159027082 3045.18409729177 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.251929868357231 0.503859736714463 0.748070131642769 17 0.145453410158640 0.290906820317279 0.85454658984136 18 0.0808806929377354 0.161761385875471 0.919119307062265 19 0.104998781257710 0.209997562515420 0.89500121874229 20 0.0578996819808462 0.115799363961692 0.942100318019154 21 0.0303857502701164 0.0607715005402328 0.969614249729884 22 0.0447149958996101 0.0894299917992203 0.95528500410039 23 0.0647839890860962 0.129567978172192 0.935216010913904 24 0.0530336409079935 0.106067281815987 0.946966359092007 25 0.0920450078913464 0.184090015782693 0.907954992108654 26 0.139181498952617 0.278362997905234 0.860818501047383 27 0.102347492229984 0.204694984459967 0.897652507770016 28 0.110292837829215 0.22058567565843 0.889707162170785 29 0.206662987051759 0.413325974103518 0.793337012948241 30 0.154682751622094 0.309365503244187 0.845317248377906 31 0.484656986498147 0.969313972996295 0.515343013501853 32 0.423139183141089 0.846278366282177 0.576860816858911 33 0.42050887451844 0.84101774903688 0.57949112548156 34 0.362265029163994 0.724530058327987 0.637734970836006 35 0.337205713551269 0.674411427102539 0.66279428644873 36 0.362516075900248 0.725032151800495 0.637483924099752 37 0.318612000398157 0.637224000796314 0.681387999601843 38 0.339663153778728 0.679326307557455 0.660336846221272 39 0.383307227320109 0.766614454640217 0.616692772679891 40 0.356740538308651 0.713481076617303 0.643259461691349 41 0.320813388969754 0.641626777939508 0.679186611030246 42 0.268071854291955 0.53614370858391 0.731928145708045 43 0.236508910282524 0.473017820565047 0.763491089717476 44 0.199366049969845 0.39873209993969 0.800633950030155 45 0.224056344053328 0.448112688106656 0.775943655946672 46 0.192087559662861 0.384175119325721 0.80791244033714 47 0.202025938720712 0.404051877441425 0.797974061279287 48 0.166663426830869 0.333326853661738 0.833336573169131 49 0.135187888807210 0.270375777614419 0.86481211119279 50 0.117489571777537 0.234979143555074 0.882510428222463 51 0.116670131020473 0.233340262040947 0.883329868979527 52 0.111106899998238 0.222213799996476 0.888893100001762 53 0.087016932540174 0.174033865080348 0.912983067459826 54 0.0932110817454267 0.186422163490853 0.906788918254573 55 0.0732929246405772 0.146585849281154 0.926707075359423 56 0.0571489465482247 0.114297893096449 0.942851053451775 57 0.0431295461999284 0.0862590923998568 0.956870453800072 58 0.0395322483993453 0.0790644967986906 0.960467751600655 59 0.0401904526961702 0.0803809053923405 0.95980954730383 60 0.0312020604098191 0.0624041208196383 0.968797939590181 61 0.0234921835868878 0.0469843671737755 0.976507816413112 62 0.0172488857825828 0.0344977715651656 0.982751114217417 63 0.0121923885549553 0.0243847771099106 0.987807611445045 64 0.0101703892915113 0.0203407785830226 0.989829610708489 65 0.00831598904243073 0.0166319780848615 0.99168401095757 66 0.00773962113351338 0.0154792422670268 0.992260378866487 67 0.0070930209904705 0.014186041980941 0.99290697900953 68 0.00651428810384569 0.0130285762076914 0.993485711896154 69 0.00532694915066953 0.0106538983013391 0.99467305084933 70 0.00487382537448523 0.00974765074897045 0.995126174625515 71 0.00463625936456273 0.00927251872912545 0.995363740635437 72 0.00313709336163853 0.00627418672327707 0.996862906638361 73 0.00244104278524212 0.00488208557048424 0.997558957214758 74 0.00163676802205641 0.00327353604411281 0.998363231977944 75 0.00116124129075167 0.00232248258150333 0.998838758709248 76 0.00385211206207020 0.00770422412414039 0.99614788793793 77 0.00277171484360405 0.00554342968720811 0.997228285156396 78 0.00357143517541838 0.00714287035083676 0.996428564824582 79 0.00544024791588675 0.0108804958317735 0.994559752084113 80 0.00417764600912598 0.00835529201825196 0.995822353990874 81 0.00451548867239174 0.00903097734478348 0.995484511327608 82 0.00862170888414263 0.0172434177682853 0.991378291115857 83 0.0100307301327460 0.0200614602654919 0.989969269867254 84 0.00765865383384676 0.0153173076676935 0.992341346166153 85 0.0135803665051899 0.0271607330103798 0.98641963349481 86 0.0210643741506530 0.0421287483013059 0.978935625849347 87 0.0559848955926941 0.111969791185388 0.944015104407306 88 0.130231462167126 0.260462924334252 0.869768537832874 89 0.105521054459513 0.211042108919025 0.894478945540487 90 0.132642754696918 0.265285509393837 0.867357245303082 91 0.117685320041649 0.235370640083298 0.882314679958351 92 0.106708945845493 0.213417891690986 0.893291054154507 93 0.0956027178581663 0.191205435716333 0.904397282141834 94 0.241628881106434 0.483257762212868 0.758371118893566 95 0.265452349023986 0.530904698047972 0.734547650976014 96 0.513246118419028 0.973507763161944 0.486753881580972 97 0.560068132807835 0.87986373438433 0.439931867192165 98 0.541577791248301 0.916844417503399 0.458422208751699 99 0.66006182200819 0.679876355983619 0.339938177991810 100 0.774887634731928 0.450224730536144 0.225112365268072 101 0.724987125928259 0.550025748143483 0.275012874071741 102 0.97695908353234 0.0460818329353207 0.0230409164676604 103 0.966483140732136 0.0670337185357277 0.0335168592678639 104 0.950533113378576 0.0989337732428471 0.0494668866214236 105 0.93562643206542 0.12874713586916 0.06437356793458 106 0.925285679808008 0.149428640383984 0.0747143201919921 107 0.910097165879322 0.179805668241356 0.0899028341206778 108 0.933077085187067 0.133845829625866 0.0669229148129331 109 0.897164878106348 0.205670243787304 0.102835121893652 110 0.911085552036686 0.177828895926628 0.0889144479633139 111 0.951951045727488 0.0960979085450234 0.0480489542725117 112 0.975112416631316 0.0497751667373674 0.0248875833686837 113 0.957107076599342 0.0857858468013155 0.0428929234006577 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 11 0.112244897959184 NOK 5% type I error level 28 0.285714285714286 NOK 10% type I error level 38 0.387755102040816 NOK

Charts produced by software:

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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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