Multiple Regression WS10

*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, 10 Dec 2010 09:09:44 +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/Dec/10/t1291972124b8njc03sjv8xq0e.htm/, Retrieved Fri, 10 Dec 2010 10:08:44 +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/Dec/10/t1291972124b8njc03sjv8xq0e.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 «

40399 44164 44496 43110 43880 195722 198563 229139 229527 211868 36763 40399 44164 44496 43110 202196 195722 198563 229139 229527 37903 36763 40399 44164 44496 205816 202196 195722 198563 229139 35532 37903 36763 40399 44164 212588 205816 202196 195722 198563 35533 35532 37903 36763 40399 214320 212588 205816 202196 195722 32110 35533 35532 37903 36763 220375 214320 212588 205816 202196 33374 32110 35533 35532 37903 204442 220375 214320 212588 205816 35462 33374 32110 35533 35532 206903 204442 220375 214320 212588 33508 35462 33374 32110 35533 214126 206903 204442 220375 214320 36080 33508 35462 33374 32110 226899 214126 206903 204442 220375 34560 36080 33508 35462 33374 223532 226899 214126 206903 204442 38737 34560 36080 33508 35462 195309 223532 226899 214126 206903 38144 38737 34560 36080 33508 186005 195309 223532 226899 214126 37594 38144 38737 34560 36080 188906 186005 195309 223532 226899 36424 37594 38144 38737 34560 191563 188906 186005 195309 223532 36843 36424 37594 38144 38737 1892 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 7 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation OPENVAC[t] = + 2916.22308407620 + 1.20727819032451Y1[t] -0.0769374964679657Y2[t] -0.295020374564640Y3[t] + 0.106069047767341Y4[t] -0.0190621316848944NWWZ[t] + 0.00830310174893315X1[t] + 0.0416925619993690X2[t] -0.0618940493708573X3[t] + 0.0255099935589280X4[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) 2916.22308407620 2006.581725 1.4533 0.148856 0.074428 Y1 1.20727819032451 0.096339 12.5315 0 0 Y2 -0.0769374964679657 0.152589 -0.5042 0.615075 0.307538 Y3 -0.295020374564640 0.151992 -1.941 0.054702 0.027351 Y4 0.106069047767341 0.100092 1.0597 0.291495 0.145747 NWWZ -0.0190621316848944 0.019078 -0.9992 0.319804 0.159902 X1 0.00830310174893315 0.029625 0.2803 0.779768 0.389884 X2 0.0416925619993690 0.031109 1.3402 0.18282 0.09141 X3 -0.0618940493708573 0.028573 -2.1662 0.03236 0.01618 X4 0.0255099935589280 0.018046 1.4136 0.160184 0.080092 Multiple Linear Regression - Regression Statistics Multiple R 0.967504886669294 R-squared 0.936065705728964 Adjusted R-squared 0.931062152264274 F-TEST (value) 187.080184579781 F-TEST (DF numerator) 9 F-TEST (DF denominator) 115 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2052.68504885078 Sum Squared Residuals 484554329.624184 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 40399 43416.6257715925 -3017.62577159253 2 36763 37458.9019670443 -695.901967044333 3 37903 35352.7419772328 2550.25802276717 4 35532 37651.0541878872 -2119.05418788717 5 35533 35075.1970698027 457.802930197315 6 32110 34659.2299017477 -2549.22990174765 7 33374 31446.4551582699 1927.54484173008 8 35462 33122.8235281537 2339.17647184635 9 33508 35444.2076110100 -1936.20761100996 10 36080 33248.2794211605 2831.72057883955 11 34560 35934.414637528 -1374.41463752797 12 38737 35357.7029998715 3379.2970001285 13 38144 38747.7204399171 -603.72043991706 14 37594 37656.6730424655 -62.6730424655093 15 36424 36891.2441405552 -467.244140555227 16 36843 36182.4951281615 660.504871838538 17 37246 36605.818769037 640.181230962978 18 38661 37295.3752052028 1365.62479479720 19 40454 38964.2386959052 1489.76130409485 20 44928 40569.4455859193 4358.55441408066 21 48441 45363.7902928645 3077.20970713551 22 48140 49266.1249332942 -1126.12493329416 23 45998 47383.0213269791 -1385.02132697914 24 47369 45083.8298794291 2285.17012057089 25 49554 46580.6711588737 2973.32884112627 26 47510 48663.4924361199 -1153.49243611992 27 44873 46872.1237603823 -1999.12376038229 28 45344 43396.8484344880 1947.15156551195 29 42413 44298.3247197018 -1885.32471970177 30 36912 41471.8652169968 -4559.86521699679 31 43452 34891.682845119 8560.31715488099 32 42142 43733.6434903281 -1591.64349032808 33 44382 42686.6950249164 1695.30497508356 34 43636 43309.39243646 326.60756354 35 44167 43301.1296895678 865.870310432216 36 44423 44300.2625006992 122.737499300848 37 42868 43753.0939557405 -885.093955740535 38 43908 40597.4538395836 3310.54616041637 39 42013 43978.84058619 -1965.84058619004 40 38846 41392.1656887526 -2546.16568875256 41 35087 37017.791381644 -1930.79138164398 42 33026 33577.3314554256 -551.331455425634 43 34646 32090.3441209996 2555.65587900044 44 37135 34441.5610123150 2693.43898768496 45 37985 37570.8246549117 414.175345088343 46 43121 37946.0129174278 5174.98708257222 47 43722 43933.5722035703 -211.572203570289 48 43630 44165.625836671 -535.625836671008 49 42234 42260.6748667514 -26.6748667514262 50 39351 39978.9543501525 -627.954350152471 51 39327 38509.470399822 817.529600178023 52 35704 38400.1228517099 -2696.12285170990 53 30466 34464.0669654113 -3998.06696541131 54 28155 28387.0565314575 -232.056531457502 55 29257 26747.6648745963 2509.33512540367 56 29998 29864.9582644656 133.041735534424 57 32529 29873.3016325018 2655.69836749819 58 34787 33008.6483336249 1778.3516663751 59 33855 35273.5694529508 -1418.56945295083 60 34556 34489.6469680959 66.3530319041103 61 31348 33480.6622956257 -2132.66229562572 62 30805 29441.2727111412 1363.72728885883 63 28353 30613.2074945979 -2260.20749459794 64 24514 28089.1841309380 -3575.18413093796 65 21106 23286.0262818105 -2180.02628181046 66 21346 20042.8350566098 1303.16494339020 67 23335 21701.1901531722 1633.80984682783 68 24379 24305.0684263475 73.931573652472 69 26290 24942.8790102659 1347.12098973408 70 30084 27004.7598185844 3079.24018141564 71 29429 31027.0994862747 -1598.09948627472 72 30632 31111.81975047 -479.819750469983 73 27349 29584.1452670640 -2235.14526706404 74 27264 26045.0668343114 1218.93316568857 75 27474 27415.8222327852 58.1777672147815 76 24482 28144.9581758419 -3662.95817584193 77 21453 24080.0249398751 -2627.02493987509 78 18788 20595.9473655206 -1807.94736552062 79 19282 18570.8307214822 711.169278517825 80 19713 19550.329341445 162.670658555016 81 21917 20462.9604278171 1454.0395721829 82 23812 23014.9130903398 797.086909660217 83 23785 25109.2281343581 -1324.22813435808 84 24696 25347.8728647611 -651.872864761088 85 24562 24668.8620498617 -106.862049861737 86 23580 23907.6542605707 -327.654260570681 87 24939 24510.9756013969 428.024398603075 88 23899 25745.7757619179 -1846.77576191786 89 21454 24263.4356575387 -2809.43565753869 90 19761 21200.928372505 -1439.92837250498 91 19815 19784.1451619030 30.8548380970458 92 20780 20190.8470845858 589.152915414245 93 23462 21734.8823317012 1727.11766829877 94 25005 24633.3948383678 371.605161632174 95 24725 26385.8202111246 -1660.82021112457 96 26198 26124.1738129710 73.8261870290377 97 27543 26382.5950911317 1160.40490886832 98 26471 27354.8210694773 -883.8210694773 99 26558 27570.8488477490 -1012.84884774904 100 25317 26697.1700996389 -1380.17009963886 101 22896 25629.7474073438 -2733.74740734385 102 22248 22564.7013919538 -316.701391953801 103 23406 22518.8537379387 887.146262061253 104 25073 24151.2808331886 921.719166811442 105 27691 25976.043109844 1714.95689015598 106 30599 29143.4830609818 1455.51693901824 107 31948 32000.0952743487 -52.0952743486932 108 32946 33302.1774809776 -356.177480977608 109 34012 32806.4767195555 1205.52328044449 110 32936 33711.8992424468 -775.899242446798 111 32974 33581.4363516315 -607.436351631457 112 30951 32843.4550054186 -1892.45500541863 113 29812 30656.9684964223 -844.968496422295 114 29010 29531.7407406632 -521.740740663159 115 31068 29236.0614016933 1831.93859830666 116 32447 31437.0327763678 1009.96722363224 117 34844 33218.2903269012 1625.70967309881 118 35676 35234.8157557156 441.184244284417 119 35387 36307.0378494799 -920.03784947986 120 36488 36337.6141961388 150.385803861180 121 35652 35409.3136557818 242.686344218198 122 33488 34934.6546951543 -1446.65469515425 123 32914 33430.2448256227 -516.244825622688 124 29781 32487.0279540819 -2706.02795408193 125 27951 29138.8169170118 -1187.81691701180 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 13 0.26396105538585 0.5279221107717 0.73603894461415 14 0.169935920964145 0.339871841928290 0.830064079035855 15 0.1220414442955 0.244082888591 0.8779585557045 16 0.0915569663803196 0.183113932760639 0.90844303361968 17 0.0887004358553361 0.177400871710672 0.911299564144664 18 0.0691735527789837 0.138347105557967 0.930826447221016 19 0.0456963840022256 0.0913927680044512 0.954303615997774 20 0.282987371850506 0.565974743701012 0.717012628149494 21 0.469459280875637 0.938918561751274 0.530540719124363 22 0.418729690015385 0.83745938003077 0.581270309984615 23 0.339586990345283 0.679173980690567 0.660413009654717 24 0.295449580014708 0.590899160029415 0.704550419985292 25 0.330310949561031 0.660621899122063 0.669689050438969 26 0.268467270515653 0.536934541031307 0.731532729484346 27 0.441924834765926 0.883849669531852 0.558075165234074 28 0.383886591386948 0.767773182773896 0.616113408613052 29 0.36401243225999 0.72802486451998 0.63598756774001 30 0.68526663324998 0.629466733500041 0.314733366750021 31 0.979666421037737 0.0406671579245256 0.0203335789622628 32 0.973999684806378 0.0520006303872442 0.0260003151936221 33 0.972508193256625 0.0549836134867493 0.0274918067433746 34 0.96330810862539 0.073383782749221 0.0366918913746105 35 0.968982683845746 0.0620346323085073 0.0310173161542536 36 0.959056459189792 0.0818870816204156 0.0409435408102078 37 0.950725112860462 0.0985497742790757 0.0492748871395378 38 0.971812687875747 0.0563746242485059 0.0281873121242530 39 0.966281964559772 0.0674360708804553 0.0337180354402276 40 0.974051426185437 0.0518971476291259 0.0259485738145630 41 0.979845495750663 0.0403090084986746 0.0201545042493373 42 0.976462280123743 0.0470754397525132 0.0235377198762566 43 0.979037651173844 0.0419246976523115 0.0209623488261557 44 0.985062456899752 0.0298750862004965 0.0149375431002483 45 0.982817696951763 0.0343646060964737 0.0171823030482368 46 0.99940457586399 0.00119084827201893 0.000595424136009464 47 0.999207639189122 0.00158472162175539 0.000792360810877696 48 0.998774076744931 0.00245184651013738 0.00122592325506869 49 0.99826822732815 0.00346354534370062 0.00173177267185031 50 0.997500435323756 0.00499912935248732 0.00249956467624366 51 0.997760119202982 0.00447976159403599 0.00223988079701800 52 0.997517078440004 0.00496584311999237 0.00248292155999618 53 0.998994088402842 0.0020118231943166 0.0010059115971583 54 0.998634859258037 0.00273028148392597 0.00136514074196299 55 0.999342420992295 0.00131515801540917 0.000657579007704587 56 0.998969170878137 0.00206165824372581 0.00103082912186291 57 0.999330222649929 0.00133955470014200 0.000669777350071002 58 0.999376459752946 0.00124708049410809 0.000623540247054047 59 0.99915136362185 0.00169727275629940 0.000848636378149701 60 0.999105833410853 0.00178833317829446 0.00089416658914723 61 0.999171962721738 0.00165607455652367 0.000828037278261835 62 0.999460654694405 0.00107869061118948 0.000539345305594742 63 0.999535348783286 0.000929302433428292 0.000464651216714146 64 0.999882083920874 0.000235832158252843 0.000117916079126422 65 0.999916595324605 0.000166809350790950 8.34046753954748e-05 66 0.999932741812482 0.000134516375035804 6.72581875179022e-05 67 0.999922062740187 0.000155874519625368 7.79372598126838e-05 68 0.999881241911406 0.000237516177187685 0.000118758088593843 69 0.99988204550608 0.000235908987840403 0.000117954493920201 70 0.99997555106421 4.88978715793147e-05 2.44489357896574e-05 71 0.999967980563166 6.40388736684042e-05 3.20194368342021e-05 72 0.999950754233856 9.84915322875772e-05 4.92457661437886e-05 73 0.999963988115047 7.20237699050586e-05 3.60118849525293e-05 74 0.999990686741118 1.86265177638103e-05 9.31325888190513e-06 75 0.99998484172608 3.03165478389833e-05 1.51582739194917e-05 76 0.99999946118442 1.07763116139596e-06 5.38815580697978e-07 77 0.999999490621832 1.01875633693122e-06 5.0937816846561e-07 78 0.99999921808218 1.56383563932901e-06 7.81917819664507e-07 79 0.999998881588902 2.23682219511868e-06 1.11841109755934e-06 80 0.999997772187298 4.45562540433131e-06 2.22781270216566e-06 81 0.999997158073451 5.68385309759361e-06 2.84192654879680e-06 82 0.999995472242184 9.05551563160702e-06 4.52775781580351e-06 83 0.99999180744228 1.63851154396464e-05 8.19255771982318e-06 84 0.999983650931475 3.26981370495917e-05 1.63490685247958e-05 85 0.999968991103825 6.20177923500867e-05 3.10088961750433e-05 86 0.999945755040294 0.000108489919411729 5.42449597058644e-05 87 0.99992176451106 0.000156470977881502 7.82354889407512e-05 88 0.99996712647743 6.57470451418735e-05 3.28735225709367e-05 89 0.999988109999 2.37800020006994e-05 1.18900010003497e-05 90 0.999976829809693 4.63403806148295e-05 2.31701903074148e-05 91 0.999952510927484 9.497814503213e-05 4.7489072516065e-05 92 0.999896378604274 0.000207242791452132 0.000103621395726066 93 0.9998509372477 0.000298125504599554 0.000149062752299777 94 0.999683341233495 0.000633317533009445 0.000316658766504723 95 0.999659739045886 0.0006805219082273 0.00034026095411365 96 0.99931981952228 0.00136036095544036 0.000680180477720181 97 0.998648555455595 0.00270288908881022 0.00135144454440511 98 0.997737932827712 0.00452413434457542 0.00226206717228771 99 0.99654000996348 0.00691998007303889 0.00345999003651945 100 0.994389177287684 0.0112216454246322 0.00561082271231611 101 0.999440583938905 0.00111883212219093 0.000559416061095465 102 0.998670820355731 0.00265835928853711 0.00132917964426855 103 0.997560396205637 0.00487920758872602 0.00243960379436301 104 0.996526314451948 0.00694737109610481 0.00347368554805240 105 0.9927072068061 0.0145855863877997 0.00729279319389984 106 0.984280612611973 0.031438774776054 0.015719387388027 107 0.974158736792042 0.0516825264159156 0.0258412632079578 108 0.992060004890779 0.0158799902184424 0.00793999510922121 109 0.985586469058196 0.0288270618836089 0.0144135309418044 110 0.98153769347169 0.0369246130566213 0.0184623065283106 111 0.966773609672855 0.0664527806542896 0.0332263903271448 112 0.916913178381475 0.166173643237051 0.0830868216185254 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 58 0.58 NOK 5% type I error level 70 0.7 NOK 10% type I error level 82 0.82 NOK

Charts produced by software:

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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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