Apple Inc - Multiple regression model 2

*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: Tue, 14 Dec 2010 10:39:25 +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/14/t1292323266cmebnfvizy21qjl.htm/, Retrieved Tue, 14 Dec 2010 11:41:26 +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/14/t1292323266cmebnfvizy21qjl.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:

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10.81 24563400 -0,2643 24.45 2772.73 0,0373 115.7 9.12 14163200 -0,2643 23.62 2151.83 0,0353 109.2 11.03 18184800 -0,2643 21.90 1840.26 0,0292 116.9 12.74 20810300 -0,1918 27.12 2116.24 0,0327 109.9 9.98 12843000 -0,1918 27.70 2110.49 0,0362 116.1 11.62 13866700 -0,1918 29.23 2160.54 0,0325 118.9 9.40 15119200 -0,2246 26.50 2027.13 0,0272 116.3 9.27 8301600 -0,2246 22.84 1805.43 0,0272 114.0 7.76 14039600 -0,2246 20.49 1498.80 0,0265 97.0 8.78 12139700 0,3654 23.28 1690.20 0,0213 85.3 10.65 9649000 0,3654 25.71 1930.58 0,019 84.9 10.95 8513600 0,3654 26.52 1950.40 0,0155 94.6 12.36 15278600 0,0447 25.51 1934.03 0,0114 97.8 10.85 15590900 0,0447 23.36 1731.49 0,0114 95.0 11.84 9691100 0,0447 24.15 1845.35 0,0148 110.7 12.14 10882700 -0,0312 20.92 1688.23 0,0164 108.5 11.65 10294800 -0,0312 20.38 1615.73 0,0118 110.3 8.86 16031900 -0,0312 21.90 1463.21 0,0107 106.3 7.63 13683600 -0,0048 19.21 1328.26 0,0146 97.4 7.38 8677200 -0,0048 19.65 1314.85 0,018 94.5 7.25 9874100 - 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 10 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation APPLE[t] = -89.980306402187 + 4.83923660683464e-07VOLUME[t] + 25.9301820373231REV.GROWTH[t] + 6.43325933193156MICROSOFT[t] + 0.0903195954538196NASDAQ[t] -939.517745569975INFLATION[t] -1.93823831639427CONS.CONF[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) -89.980306402187 22.176341 -4.0575 9.3e-05 4.7e-05 VOLUME 4.83923660683464e-07 0 1.6051 0.111338 0.055669 REV.GROWTH 25.9301820373231 13.52697 1.9169 0.057842 0.028921 MICROSOFT 6.43325933193156 1.513454 4.2507 4.5e-05 2.2e-05 NASDAQ 0.0903195954538196 0.016513 5.4696 0 0 INFLATION -939.517745569975 254.495487 -3.6917 0.000348 0.000174 CONS.CONF -1.93823831639427 0.146652 -13.2166 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.913237166920447 R-squared 0.834002123044885 Adjusted R-squared 0.824947693392788 F-TEST (value) 92.1098462399246 F-TEST (DF numerator) 6 F-TEST (DF denominator) 110 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 31.7878356811424 Sum Squared Residuals 111151.314702044 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 10.81 63.4800143839998 -52.6700143839998 2 9.12 11.5056540130827 -2.38565401308266 3 11.03 -34.9476577878407 45.9776577878407 4 12.74 36.9901937522829 -24.2501937522829 5 9.98 21.0411918380409 -11.0611918380409 6 11.62 33.9491153925063 -22.3291153925063 7 9.4 14.1112482751667 -4.71124827516666 8 9.27 -28.2995850131832 37.5695850131832 9 7.76 -34.7279742316239 42.4879742316239 10 8.78 42.4502712921912 -33.6702712921912 11 10.65 81.5249933036784 -70.8749933036784 12 10.95 72.4640212605682 -61.5140212605682 13 12.36 57.0954918872676 -44.7354918872676 14 10.85 30.5488501055335 -19.6988501055335 15 11.84 9.43515940050314 2.40484059949686 16 12.14 -24.1658445587469 36.3058445587469 17 11.65 -33.6395213283955 45.2895213283955 18 8.86 -26.0737706230650 34.9337706230650 19 7.63 -42.4335069508654 50.0635069508654 20 7.38 -40.8102432520915 48.1902432520915 21 7.25 -62.6197529125355 69.8697529125355 22 8.03 1.52067878542204 6.50932121457796 23 7.75 12.7687055953806 -5.01870559538057 24 7.16 -9.24364539203321 16.4036453920332 25 7.18 -20.6959034411282 27.8759034411282 26 7.51 3.29020725818311 4.21979274181689 27 7.07 12.0783801337905 -5.00838013379053 28 7.11 5.44180135523165 1.66819864476835 29 8.98 11.2748084026594 -2.29480840265939 30 9.53 14.7683661434446 -5.23836614344457 31 10.54 43.3313394064382 -32.7913394064382 32 11.31 40.5690983092261 -29.2590983092261 33 10.36 53.552593126796 -43.1925931267960 34 11.44 57.7479522065992 -46.3079522065992 35 10.45 38.7731323125545 -28.3231323125545 36 10.69 51.6322218871421 -40.9422218871421 37 11.28 50.0132250120638 -38.7332250120638 38 11.96 56.3373762179714 -44.3773762179714 39 13.52 48.4795103016563 -34.9595103016563 40 12.89 33.0876916181363 -20.1976916181363 41 14.03 29.2587301418427 -15.2287301418427 42 16.27 28.3678374591685 -12.0978374591685 43 16.17 13.333403834477 2.83659616552300 44 17.25 18.3298860569358 -1.07988605693577 45 19.38 30.4221067355482 -11.0421067355482 46 26.2 57.2402589849444 -31.0402589849445 47 33.53 76.9824084082835 -43.4524084082835 48 32.2 63.9848012919705 -31.7848012919705 49 38.45 59.1329257978746 -20.6829257978746 50 44.86 49.5359912730844 -4.67599127308441 51 41.67 32.3290280448933 9.34097195510665 52 36.06 44.9666776093238 -8.90667760932377 53 39.76 51.7828038305373 -12.0228038305373 54 36.81 40.0092481477978 -3.1992481477978 55 42.65 50.3321754750179 -7.68217547501791 56 46.89 48.2428363319817 -1.35283633198171 57 53.61 66.8130248777108 -13.2030248777108 58 57.59 79.0867975329776 -21.4967975329776 59 67.82 79.7618761867402 -11.9418761867402 60 71.89 57.6877138209828 14.2022861790172 61 75.51 67.9357474027165 7.57425259728351 62 68.49 68.8500772439185 -0.360077243918469 63 62.72 68.8917828512036 -6.1717828512036 64 70.39 41.3731175935756 29.0168824064244 65 59.77 18.3678205543539 41.4021794456461 66 57.27 19.6705149587124 37.5994850412876 67 67.96 19.0828420205863 48.8771579794137 68 67.85 51.9877827691538 15.8622172308462 69 76.98 75.7904920449815 1.18950795501854 70 81.08 96.332104612129 -15.252104612129 71 91.66 100.464309674999 -8.80430967499932 72 84.84 90.958360075194 -6.11836007519393 73 85.73 113.248966120043 -27.5189661200432 74 84.61 77.4266745110507 7.1833254889493 75 92.91 78.2123077920361 14.6976922079639 76 99.8 106.377959999548 -6.57795999954765 77 121.19 115.739578875215 5.45042112478458 78 122.04 119.926218242445 2.11378175755469 79 131.76 105.72062990219 26.0393700978099 80 138.48 122.901524790815 15.5784752091853 81 153.47 141.870469722208 11.5995302777921 82 189.95 202.450532300888 -12.5005323008877 83 182.22 177.416135447814 4.80386455218604 84 198.08 178.307007702892 19.7729922971080 85 135.36 155.673040274325 -20.3130402743250 86 125.02 130.099143705891 -5.07914370589138 87 143.5 156.717388194759 -13.2173881947587 88 173.95 171.403114883223 2.54688511677712 89 188.75 185.380640171393 3.36935982860716 90 167.44 165.759679996508 1.68032000349192 91 158.95 158.874560434792 0.0754395652082485 92 169.53 157.445049762352 12.0849502376480 93 113.66 137.184993901735 -23.5249939017346 94 107.59 128.423202074343 -20.8332020743435 95 92.67 101.475730679276 -8.80573067927569 96 85.35 116.479146965348 -31.1291469653484 97 90.13 160.438728724576 -70.3087287245765 98 89.31 100.118072676435 -10.8080726764352 99 105.12 129.532018657467 -24.4120186574667 100 125.83 136.166858248656 -10.3368582486562 101 135.81 122.443849359452 13.3661506405480 102 142.43 159.677592460384 -17.2475924603841 103 163.39 173.790429882175 -10.4004298821750 104 168.21 162.925145935586 5.28485406441429 105 185.35 181.890527342872 3.45947265712799 106 188.5 195.029606453597 -6.52960645359697 107 199.91 189.67290082876 10.2370991712401 108 210.73 195.149117213710 15.5808827862898 109 192.06 174.90356687833 17.1564331216701 110 204.62 206.550677419071 -1.93067741907123 111 235 211.059324459506 23.9406755404943 112 261.09 219.332758434926 41.7572415650737 113 256.88 169.694647538562 87.185352461438 114 251.53 161.306541557169 90.2234584428309 115 257.25 197.870733823665 59.3792661763347 116 243.1 162.262863568598 80.8371364314019 117 283.75 203.208970955788 80.5410290442125 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 7.05349581737511e-05 0.000141069916347502 0.999929465041826 11 6.63995828475428e-06 1.32799165695086e-05 0.999993360041715 12 2.76088454987290e-07 5.52176909974579e-07 0.999999723911545 13 1.14486101669958e-08 2.28972203339916e-08 0.99999998855139 14 4.13320242669417e-10 8.26640485338834e-10 0.99999999958668 15 1.6726092722201e-11 3.3452185444402e-11 0.999999999983274 16 1.79886977839267e-12 3.59773955678535e-12 0.999999999998201 17 6.65433771979008e-14 1.33086754395802e-13 0.999999999999933 18 4.50794069982022e-14 9.01588139964043e-14 0.999999999999955 19 3.99647490794641e-15 7.99294981589282e-15 0.999999999999996 20 2.36928540484906e-16 4.73857080969813e-16 1 21 2.20091861630273e-17 4.40183723260546e-17 1 22 1.34868451530872e-18 2.69736903061743e-18 1 23 5.74435555674214e-20 1.14887111134843e-19 1 24 3.10886208461637e-21 6.21772416923273e-21 1 25 2.95582226390476e-22 5.91164452780952e-22 1 26 4.59614843187513e-23 9.19229686375025e-23 1 27 2.85599311165209e-24 5.71198622330418e-24 1 28 2.53858941345153e-25 5.07717882690307e-25 1 29 1.57739409768047e-26 3.15478819536094e-26 1 30 1.10673737853708e-27 2.21347475707415e-27 1 31 8.22608419017279e-29 1.64521683803456e-28 1 32 7.3511586312905e-30 1.4702317262581e-29 1 33 3.56766113091449e-31 7.13532226182898e-31 1 34 2.94491678632262e-32 5.88983357264523e-32 1 35 1.771758676625e-33 3.54351735325e-33 1 36 1.18515599584402e-34 2.37031199168804e-34 1 37 7.2185337360531e-36 1.44370674721062e-35 1 38 7.00264021917986e-37 1.40052804383597e-36 1 39 7.00060265889873e-37 1.40012053177975e-36 1 40 2.84106504476170e-37 5.68213008952339e-37 1 41 5.74872660567821e-37 1.14974532113564e-36 1 42 1.65168076224988e-36 3.30336152449977e-36 1 43 3.74809572961926e-37 7.49619145923852e-37 1 44 4.33804458528691e-37 8.67608917057383e-37 1 45 5.1123918592687e-36 1.02247837185374e-35 1 46 5.50004239488702e-36 1.10000847897740e-35 1 47 6.75022643355475e-34 1.35004528671095e-33 1 48 1.44401768367416e-33 2.88803536734832e-33 1 49 2.09299480306535e-34 4.18598960613069e-34 1 50 4.19163535555154e-32 8.38327071110308e-32 1 51 2.21146628356117e-29 4.42293256712233e-29 1 52 2.71893474635203e-30 5.43786949270405e-30 1 53 3.7120659059579e-29 7.4241318119158e-29 1 54 5.61632809586159e-29 1.12326561917232e-28 1 55 4.0739089548457e-26 8.1478179096914e-26 1 56 9.31927035973957e-24 1.86385407194791e-23 1 57 4.51982954021832e-21 9.03965908043663e-21 1 58 4.13006081836404e-19 8.26012163672809e-19 1 59 1.09322897985720e-14 2.18645795971441e-14 0.99999999999999 60 8.90094371179643e-12 1.78018874235929e-11 0.999999999991099 61 5.71949717688745e-10 1.14389943537749e-09 0.99999999942805 62 3.29092525803138e-09 6.58185051606276e-09 0.999999996709075 63 6.2407973495898e-09 1.24815946991796e-08 0.999999993759203 64 1.67824884304046e-08 3.35649768608092e-08 0.999999983217511 65 2.44914226624294e-08 4.89828453248588e-08 0.999999975508577 66 2.98173036990034e-08 5.96346073980068e-08 0.999999970182696 67 2.34438292819046e-07 4.68876585638091e-07 0.999999765561707 68 6.60040604163995e-07 1.32008120832799e-06 0.999999339959396 69 7.59642382415393e-06 1.51928476483079e-05 0.999992403576176 70 0.000189661346692616 0.000379322693385233 0.999810338653307 71 0.00127152584473796 0.00254305168947592 0.998728474155262 72 0.00150937653321818 0.00301875306643637 0.998490623466782 73 0.00115311817542732 0.00230623635085464 0.998846881824573 74 0.00167262064054003 0.00334524128108005 0.99832737935946 75 0.00383185548531818 0.00766371097063636 0.996168144514682 76 0.00680152384226875 0.0136030476845375 0.99319847615773 77 0.0137595796261374 0.0275191592522748 0.986240420373863 78 0.0138674867508001 0.0277349735016001 0.9861325132492 79 0.0167901642270028 0.0335803284540055 0.983209835772997 80 0.0214530613749476 0.0429061227498952 0.978546938625052 81 0.0299787922685723 0.0599575845371446 0.970021207731428 82 0.0863928358771164 0.172785671754233 0.913607164122884 83 0.0892278697572793 0.178455739514559 0.91077213024272 84 0.133843647484963 0.267687294969926 0.866156352515037 85 0.160938948513826 0.321877897027652 0.839061051486174 86 0.165753039557685 0.331506079115371 0.834246960442315 87 0.198850844374693 0.397701688749385 0.801149155625307 88 0.192032498203792 0.384064996407584 0.807967501796208 89 0.203591715554157 0.407183431108314 0.796408284445843 90 0.170438927288803 0.340877854577607 0.829561072711197 91 0.217244816987485 0.434489633974969 0.782755183012515 92 0.406303622706189 0.812607245412378 0.593696377293811 93 0.99473998521644 0.0105200295671214 0.0052600147835607 94 0.99294791429726 0.0141041714054786 0.0070520857027393 95 0.98769309631714 0.0246138073657192 0.0123069036828596 96 0.989258105752522 0.0214837884949565 0.0107418942474783 97 0.98229318770767 0.0354136245846597 0.0177068122923299 98 0.974034597854374 0.0519308042912524 0.0259654021456262 99 0.97032385637526 0.0593522872494801 0.0296761436247400 100 0.958763419245923 0.0824731615081536 0.0412365807540768 101 0.980724733434898 0.0385505331302036 0.0192752665651018 102 0.986557993521558 0.0268840129568845 0.0134420064784422 103 0.975101207420888 0.0497975851582233 0.0248987925791117 104 0.962898319128095 0.0742033617438092 0.0371016808719046 105 0.978984772708879 0.0420304545822420 0.0210152272911210 106 0.99455198029591 0.0108960394081803 0.00544801970409015 107 0.987931874595532 0.0241362508089368 0.0120681254044684 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 66 0.673469387755102 NOK 5% type I error level 82 0.836734693877551 NOK 10% type I error level 87 0.887755102040816 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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