multiple regression gewest

*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: Sat, 11 Dec 2010 14:29:17 +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/11/t1292077681va7o2y4wdzq1683.htm/, Retrieved Sat, 11 Dec 2010 15:28:04 +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/11/t1292077681va7o2y4wdzq1683.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 «

33024 31086 19828 18932 32526 30839 19967 18927 31455 30051 19814 19124 31524 29976 20053 19066 31856 30463 20719 19971 32696 31422 21174 20165 32584 31588 20648 19705 33498 31900 20659 19718 34175 32878 20733 19938 34172 33010 21069 20039 34379 32954 20566 19721 34988 33076 20839 19777 36158 35057 21615 20505 37411 35906 22739 21763 38015 36100 23222 22404 37577 35824 23031 22038 36354 34579 23014 22038 36030 34484 22868 21874 35636 33920 22182 21269 35669 34059 22177 21127 34635 33812 21216 20609 35496 34594 21031 20565 36376 36083 20968 19791 37635 36563 21049 20672 38875 37416 21033 20938 38372 37953 21078 20675 38897 37517 20702 19992 38018 37467 20309 19801 37325 36963 20449 20050 36893 36019 20737 20427 36117 35232 20849 20815 37599 36857 21966 21666 39037 37978 23100 22720 40809 40160 23975 23650 42508 42165 24350 24244 44021 43069 24020 23669 44088 43021 24005 23881 44510 43376 23602 23857 45786 43978 24120 23999 47349 45911 24847 24780 48696 47107 25702 25426 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 6 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation MVG[t] = -351.343556830821 + 1.08525691228605VVG[t] + 1.34991956196839MWG[t] -1.4473742694076VWG[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) -351.343556830821 116.053031 -3.0274 0.003091 0.001545 VVG 1.08525691228605 0.024216 44.8163 0 0 MWG 1.34991956196839 0.026425 51.0843 0 0 VWG -1.4473742694076 0.054987 -26.3221 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.99950480804335 R-squared 0.999009861301776 Adjusted R-squared 0.998982100403695 F-TEST (value) 35986.2227217913 F-TEST (DF numerator) 3 F-TEST (DF denominator) 107 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 529.364893669255 Sum Squared Residuals 29984309.3994924 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 33024 32749.468224778 274.531775222003 2 32526 32676.2854579039 -150.285457903912 3 31455 31329.4325869681 125.567413031927 4 31524 31654.6168014827 -130.616801482703 5 31856 31772.3096322231 83.6903677769173 6 32696 33146.493803536 -450.493803535949 7 32584 33282.3809253076 -698.380925307554 8 33498 33617.0143316202 -119.014331620154 9 34175 34459.8673001519 -284.8673001519 10 34172 34910.5093841849 -738.50938418487 11 34379 34630.9904750984 -251.990475098367 12 34988 35050.8668997278 -62.8668997278104 13 36158 37194.6099549252 -1036.60995492521 14 37411 37812.5058301938 -401.505830193784 15 38015 37747.2899129177 267.71008708226 16 37577 37719.663351394 -142.663351394008 17 36354 36345.5698630444 8.43013695558487 18 36030 36282.7515805127 -252.751580512701 19 35636 35620.2832954647 15.7167045353505 20 35669 35969.9115547184 -300.911554718448 21 34635 35154.3202698853 -519.320269885304 22 35496 35816.9405241828 -320.940524182776 23 36376 38468.1108186942 -2092.11081869418 24 37635 37823.2408897628 -188.240889762824 25 38875 38342.3647672889 532.635232711092 26 38372 39366.5535423293 -994.553542329292 27 38897 39374.3683992779 -477.36839927785 28 38018 39066.0356512668 -1048.03565126682 29 37325 38347.6587130677 -1022.65871306773 30 36893 37166.2929221499 -273.292922149936 31 36117 35901.8055065911 215.194493408874 32 37599 37941.4926365088 -342.492636508786 33 39037 39163.341938498 -126.341938497995 34 40809 41366.4940672794 -557.494067279429 35 42508 43188.913696123 -680.913696122991 36 44021 44556.7526942894 -535.75269428938 37 44088 44177.5682239557 -89.5682239557128 38 44510 44053.5538268098 456.446173190221 39 45786 45200.6096748497 585.390325150271 40 47349 47149.4035034424 199.596496557651 41 48696 48666.5482179821 29.4517820178684 42 50598 50564.4721086701 33.5278913299034 43 50066 49522.3539082912 543.6460917088 44 49367 48644.588634794 722.411365205996 45 48784 48655.5251265387 128.474873461321 46 47841 48446.9199321674 -605.919932167405 47 48300 47794.4297493811 505.570250618879 48 47518 46326.3958181333 1191.60418186674 49 46504 45859.8098226337 644.190177366305 50 45147 44788.495547488 358.504452511944 51 44404 43843.6841804956 560.315819504371 52 43455 42661.1416098873 793.858390112672 53 42299 41349.6653626651 949.334637334933 54 42105 41081.1683908663 1023.83160913367 55 40152 38815.2958281501 1336.70417184995 56 39519 39485.989927979 33.0100720209537 57 39633 38929.6940477566 703.305952243433 58 39376 38719.5422334163 656.457766583661 59 38850 38628.7467252689 221.253274731108 60 39657 38310.3007256983 1346.6992743017 61 34804 34090.3221761978 713.677823802196 62 34372 34210.8215729304 161.178427069602 63 32678 32336.819269479 341.180730520952 64 28420 28094.6131052027 325.386894797291 65 25420 25334.4128273079 85.5871726920797 66 27683 27946.0860851201 -263.086085120114 67 29904 30173.9881070548 -269.988107054818 68 30546 30116.2530607939 429.746939206129 69 29142 29495.027067201 -353.027067200997 70 27724 27897.1701121426 -173.170112142579 71 27069 27062.6735385311 6.32646146889145 72 26665 27252.354240425 -587.35424042499 73 26004 26517.14068941 -513.140689410028 74 25767 26272.8165667968 -505.816566796782 75 24915 25192.1963130666 -277.196313066641 76 23689 23784.6165741159 -95.6165741158532 77 20915 21378.3579261974 -463.357926197426 78 19414 19493.3825672318 -79.3825672318416 79 17824 17905.5371791963 -81.5371791962547 80 16348 17018.6562595456 -670.656259545596 81 15571 15149.1178545471 421.882145452894 82 13929 13968.8847570182 -39.8847570181557 83 12480 12920.7433554902 -440.743355490184 84 10837 10622.4811464187 214.518853581315 85 9473 9245.92255719018 227.077442809823 86 8051 7868.79447317318 182.205526826818 87 5278 5411.83373418216 -133.833734182159 88 3008 3157.59257210614 -149.592572106138 89 2404 2545.46089455325 -141.460894553252 90 2298 2767.27254522524 -469.272545225242 91 2260 2400.55900913542 -140.559009135421 92 1938 2180.01170533972 -242.01170533972 93 1371 2065.25753403841 -694.257534038408 94 1009 1336.35647262124 -327.356472621243 95 686 738.35016004123 -52.3501600412297 96 493 502.544883263154 -9.54488326315354 97 285 191.583760941839 93.4162390581607 98 192 62.383523760209 129.616476239791 99 129 -85.8818921774391 214.881892177439 100 60 -212.310329513305 272.310329513305 101 54 -226.72570628055 280.72570628055 102 26 -274.344301349018 300.344301349018 103 11 -337.281536148059 348.281536148059 104 3 -325.764629114054 328.764629114054 105 0 -339.773323948175 339.773323948175 106 2 -348.089976412109 350.089976412109 107 1 -354.963635242947 355.963635242947 108 0 -349.897277720479 349.897277720479 109 0 -349.173043006238 349.173043006238 110 0 -351.343556830809 351.343556830809 111 0 -350.258299918524 350.258299918524 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.182755549285556 0.365511098571113 0.817244450714444 8 0.100451328722154 0.200902657444308 0.899548671277846 9 0.0448263408588657 0.0896526817177314 0.955173659141134 10 0.0213890270461417 0.0427780540922834 0.978610972953858 11 0.0083027524093041 0.0166055048186082 0.991697247590696 12 0.0144929080948065 0.0289858161896131 0.985507091905193 13 0.00930586645323366 0.0186117329064673 0.990694133546766 14 0.0199572252639985 0.0399144505279969 0.980042774736002 15 0.0383364397065924 0.0766728794131848 0.961663560293408 16 0.0257640660115046 0.0515281320230091 0.974235933988495 17 0.0160827276668748 0.0321654553337496 0.983917272333125 18 0.00910453473315556 0.0182090694663111 0.990895465266844 19 0.00517274915707267 0.0103454983141453 0.994827250842927 20 0.00285594379168034 0.00571188758336068 0.99714405620832 21 0.0060846752784665 0.012169350556933 0.993915324721534 22 0.00383645390745665 0.0076729078149133 0.996163546092543 23 0.0455755542443395 0.0911511084886789 0.95442444575566 24 0.0423984476282716 0.0847968952565431 0.957601552371728 25 0.055842362426217 0.111684724852434 0.944157637573783 26 0.0767278664036399 0.15345573280728 0.92327213359636 27 0.116870216977246 0.233740433954492 0.883129783022754 28 0.137354506554965 0.274709013109929 0.862645493445035 29 0.211332679316812 0.422665358633623 0.788667320683188 30 0.188440325222672 0.376880650445343 0.811559674777328 31 0.156555276939174 0.313110553878348 0.843444723060826 32 0.180656613282742 0.361313226565484 0.819343386717258 33 0.173824875408977 0.347649750817955 0.826175124591023 34 0.272192041136291 0.544384082272582 0.727807958863709 35 0.437871772851259 0.875743545702519 0.562128227148741 36 0.558221184263562 0.883557631472875 0.441778815736438 37 0.612755770077939 0.774488459844122 0.387244229922061 38 0.653257706477594 0.693484587044812 0.346742293522406 39 0.82703689157882 0.34592621684236 0.17296310842118 40 0.839634565974837 0.320730868050326 0.160365434025163 41 0.853587358856204 0.292825282287592 0.146412641143796 42 0.845456935509324 0.309086128981353 0.154543064490676 43 0.848564904724235 0.30287019055153 0.151435095275765 44 0.87114082718065 0.257718345638701 0.12885917281935 45 0.85666016228073 0.286679675438541 0.14333983771927 46 0.95507559292475 0.0898488141505 0.04492440707525 47 0.940974495573428 0.118051008853144 0.059025504426572 48 0.961029715945662 0.0779405681086753 0.0389702840543376 49 0.953674538681226 0.0926509226375473 0.0463254613187736 50 0.947044471706473 0.105911056587054 0.0529555282935272 51 0.934680771140754 0.130638457718491 0.0653192288592456 52 0.915560485165457 0.168879029669086 0.0844395148345431 53 0.90093049010053 0.198139019798939 0.0990695098994695 54 0.899384946523166 0.201230106953669 0.100615053476834 55 0.929379984264164 0.141240031471672 0.0706200157358361 56 0.967166262844574 0.065667474310853 0.0328337371554265 57 0.956163485173469 0.0876730296530626 0.0438365148265313 58 0.961783253642557 0.0764334927148865 0.0382167463574433 59 0.997627354439395 0.004745291121211 0.0023726455606055 60 0.998283685302016 0.00343262939596841 0.0017163146979842 61 0.99859903373365 0.00280193253269957 0.00140096626634978 62 0.997820219941787 0.00435956011642661 0.0021797800582133 63 0.998314394662664 0.00337121067467128 0.00168560533733564 64 0.99856834957143 0.00286330085714108 0.00143165042857054 65 0.997996906351562 0.00400618729687535 0.00200309364843767 66 0.999406116565595 0.00118776686881029 0.000593883434405145 67 0.999813683069865 0.000372633860270395 0.000186316930135197 68 0.999986957267347 2.60854653059891e-05 1.30427326529946e-05 69 0.99998703441677 2.59311664602551e-05 1.29655832301275e-05 70 0.999994540560705 1.09188785901976e-05 5.4594392950988e-06 71 0.99999997289009 5.42198182870976e-08 2.71099091435488e-08 72 0.999999994490635 1.10187308576981e-08 5.50936542884904e-09 73 0.999999999213561 1.57287715293645e-09 7.86438576468223e-10 74 0.999999999850087 2.9982669695332e-10 1.4991334847666e-10 75 0.999999999964294 7.1412888707531e-11 3.57064443537655e-11 76 0.999999999999987 2.66430679181423e-14 1.33215339590712e-14 77 0.999999999999994 1.19220319197378e-14 5.96101595986889e-15 78 1 1.68964081114166e-16 8.44820405570829e-17 79 1 4.39027249365828e-16 2.19513624682914e-16 80 1 1.68610418014427e-18 8.43052090072134e-19 81 1 1.60493923563001e-18 8.02469617815004e-19 82 1 5.77582918742823e-18 2.88791459371411e-18 83 1 3.24606163176517e-17 1.62303081588259e-17 84 1 2.41336966758559e-16 1.20668483379279e-16 85 1 1.56192328537736e-15 7.8096164268868e-16 86 1 2.7437963031234e-17 1.3718981515617e-17 87 1 1.99434484873553e-16 9.97172424367765e-17 88 1 1.48214323429178e-15 7.41071617145892e-16 89 0.999999999999997 6.23868365648438e-15 3.11934182824219e-15 90 0.999999999999976 4.70600043424628e-14 2.35300021712314e-14 91 1 3.16791550422452e-21 1.58395775211226e-21 92 1 9.06987347570455e-20 4.53493673785227e-20 93 1 1.69267057989323e-18 8.46335289946614e-19 94 1 2.37303995075023e-17 1.18651997537512e-17 95 1 1.90982443912188e-22 9.5491221956094e-23 96 1 8.3871040439931e-23 4.19355202199655e-23 97 1 8.56421868982582e-21 4.28210934491291e-21 98 1 1.0328139508292e-18 5.16406975414602e-19 99 1 5.8123006977893e-18 2.90615034889465e-18 100 1 3.2522464123824e-16 1.6261232061912e-16 101 0.999999999999972 5.65522513927536e-14 2.82761256963768e-14 102 0.999999999999976 4.77499530443323e-14 2.38749765221662e-14 103 0.999999999987442 2.51159742339512e-11 1.25579871169756e-11 104 0.999999991618517 1.67629649229508e-08 8.3814824614754e-09 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 48 0.489795918367347 NOK 5% type I error level 57 0.581632653061224 NOK 10% type I error level 68 0.693877551020408 NOK

Charts produced by software:

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Parameters (Session):

par1 = pearson ; par2 = equal ; par3 = 2 ; par4 = no ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = no ;

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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