Paper - Multiple Regression Model 7

*The author of this computation has been verified*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Wed, 22 Dec 2010 08:59:12 +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/22/t1293008334wq2r87zpf4ig2si.htm/, Retrieved Wed, 22 Dec 2010 09:58:54 +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/22/t1293008334wq2r87zpf4ig2si.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 -0,2643 0 0 24563400 24.45 9.12 -0,2643 0 0 14163200 23.62 11.03 -0,2643 0 0 18184800 21.90 12.74 -0,1918 0 0 20810300 27.12 9.98 -0,1918 0 0 12843000 27.70 11.62 -0,1918 0 0 13866700 29.23 9.40 -0,2246 0 0 15119200 26.50 9.27 -0,2246 0 0 8301600 22.84 7.76 -0,2246 0 0 14039600 20.49 8.78 0,3654 0 0 12139700 23.28 10.65 0,3654 0 0 9649000 25.71 10.95 0,3654 0 0 8513600 26.52 12.36 0,0447 0 0 15278600 25.51 10.85 0,0447 0 0 15590900 23.36 11.84 0,0447 0 0 9691100 24.15 12.14 -0,0312 0 0 10882700 20.92 11.65 -0,0312 0 0 10294800 20.38 8.86 -0,0312 0 0 16031900 21.90 7.63 -0,0048 0 0 13683600 19.21 7.38 -0,0048 0 0 8677200 19.65 7.25 -0,0048 0 0 9874100 17.51 8.03 0,0705 0 0 10725500 21.41 7.75 0,0705 0 0 8348400 23.09 7.16 0,0705 0 0 8046200 20.70 7.18 -0,0134 0 0 10862300 19.00 7.51 -0,0134 0 0 8100300 19.04 7.07 -0,0134 0 0 7287500 19.45 7.11 0,0812 0 0 14002500 20.54 8.98 0,0812 0 0 19037900 19.77 9.53 0,0812 0 0 10774600 20.60 10.54 0,1885 0 0 8960600 21.21 11.31 0,1885 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 13 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Apple[t] = -135.571134934426 -37.2940667049397Omzetgroei[t] + 82.8825039379301Omzetgroei_iPhone[t] + 94.3952766372327Omzetgroei_iPad[t] -4.24484261111306e-07Volume[t] + 5.42889693785601Microsoft[t] + 1.55788173789371t + 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) -135.571134934426 10.076876 -13.4537 0 0 Omzetgroei -37.2940667049397 5.857235 -6.3672 0 0 Omzetgroei_iPhone 82.8825039379301 9.656002 8.5835 0 0 Omzetgroei_iPad 94.3952766372327 11.31239 8.3444 0 0 Volume -4.24484261111306e-07 0 -3.2062 0.001761 0.000881 Microsoft 5.42889693785601 0.432367 12.5562 0 0 t 1.55788173789371 0.060936 25.5659 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.984175061462104 R-squared 0.968600551603937 Adjusted R-squared 0.966887854418697 F-TEST (value) 565.541042486335 F-TEST (DF numerator) 6 F-TEST (DF denominator) 110 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 13.825177056523 Sum Squared Residuals 21024.9072708631 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 10.81 -1.84667793521922 12.6566779352192 2 9.12 -0.380059443336055 9.50005944333605 3 11.03 -9.86698634304 20.89698634304 4 12.74 16.2114341468063 -3.47143414680626 5 9.98 24.3000695622086 -14.3200695622086 6 11.62 33.7296190769224 -22.1096190769224 7 9.4 21.1581910253492 -11.7581910253492 8 9.27 5.7402738692424 3.52972613075760 9 7.76 -7.89544288708221 15.6554428870822 10 8.78 -12.3879604007992 21.1679604007992 11 10.65 3.41940384523453 7.23059615476547 12 10.95 9.85665153285738 1.09334846714262 13 12.36 15.0199185293727 -2.65991852937268 14 10.85 4.77310541613089 6.0768945838691 15 11.84 13.1241879786353 -1.28418797863533 16 12.14 -0.528463175381171 12.6684631753812 17 11.65 -1.65263148682238 13.3026314868224 18 8.86 5.72186494219079 3.13813505780921 19 7.63 -7.31173305339089 14.9417330533909 20 7.38 -1.2399986580129 8.6199986580129 21 7.25 -11.8080215792552 19.0580215792552 22 8.03 7.75290909348487 0.277090906515132 23 7.75 19.4403792240644 -11.6903792240644 24 7.16 8.15147642419003 -0.991476424190035 25 7.18 2.41381543655743 4.76618456344257 26 7.51 5.36127858115479 2.14872141884521 27 7.07 9.49002887100075 -2.42002887100075 28 7.11 10.5869777475078 -3.47697774750779 29 8.98 5.8271607948525 3.1528392051475 30 9.53 15.3986677860078 -5.86866778600776 31 10.54 17.0365377482095 -6.49653774820952 32 11.31 19.5870103737277 -8.27701037372772 33 10.36 25.9698675883417 -15.6098675883417 34 11.44 13.7406253192194 -2.30062531921941 35 10.45 14.1530836966933 -3.70308369669331 36 10.69 23.1368037282697 -12.4468037282697 37 11.28 26.2303743534748 -14.9503743534748 38 11.96 24.6963341445026 -12.7363341445026 39 13.52 15.6315737801965 -2.1115737801965 40 12.89 23.2421146563013 -10.3521146563013 41 14.03 27.8867405032036 -13.8567405032036 42 16.27 37.5734376135357 -21.3034376135357 43 16.17 35.3512234844053 -19.1812234844053 44 17.25 33.4727940591584 -16.2227940591584 45 19.38 36.4844624980662 -17.1044624980662 46 26.2 19.316496152646 6.883503847354 47 33.53 28.1604220454955 5.36957795450454 48 32.2 31.43421705302 0.76578294698 49 38.45 22.7703877754533 15.6796122245467 50 44.86 22.3523243917955 22.5076756082045 51 41.67 26.1068036791131 15.5631963208869 52 36.06 27.5082911167019 8.55170888329805 53 39.76 36.7871109091136 2.97288909088642 54 36.81 35.1023260685505 1.70767393144954 55 42.65 47.2903618455625 -4.64036184556252 56 46.89 59.5843350884178 -12.6943350884178 57 53.61 50.1631043131735 3.44689568682654 58 57.59 43.6521952787594 13.9378047212406 59 67.82 59.4101634304881 8.4098365695119 60 71.89 53.6301329708807 18.2598670291193 61 75.51 69.0358564045775 6.47414359542254 62 68.49 65.9725305167898 2.51746948321016 63 62.72 69.370004997769 -6.65000499776898 64 70.39 58.7171974560556 11.6728025439444 65 59.77 58.2384828088655 1.53151719113453 66 57.27 61.8225710069717 -4.55257100697166 67 67.96 63.3791261722705 4.58087382772945 68 67.85 75.3805654033086 -7.53056540330859 69 76.98 83.6188873036833 -6.63888730368326 70 81.08 98.3356890132253 -17.2556890132253 71 91.66 103.283791827428 -11.6237918274284 72 84.84 104.389944081900 -19.5499440818997 73 85.73 104.268435379488 -18.5384353794884 74 84.61 102.388313691616 -17.778313691616 75 92.91 102.725535484698 -9.81553548469795 76 99.8 113.225786787996 -13.4257867879957 77 121.19 116.882370561592 4.30762943840814 78 122.04 130.111062870974 -8.07106287097412 79 131.76 132.467610116814 -0.707610116813713 80 138.48 135.187640930615 3.29235906938450 81 153.47 139.936448384227 13.5335516157732 82 189.95 184.638233836221 5.31176616377888 83 182.22 166.583013069025 15.6369869309755 84 198.08 184.622074743113 13.4579252568868 85 135.36 160.058171638852 -24.6981716388521 86 125.02 141.143819410509 -16.1238194105086 87 143.5 150.621058883214 -7.12105888321359 88 173.95 147.501750927209 26.4482490727914 89 188.75 151.199183794940 37.5508162050602 90 167.44 147.884565957664 19.5554340423359 91 158.95 158.308455075103 0.64154492489666 92 169.53 172.613448181149 -3.08344818114924 93 113.66 162.40727701238 -48.7472770123799 94 107.59 104.063049811504 3.52695018849571 95 92.67 104.729954864742 -12.0599548647424 96 85.35 107.006126630859 -21.6561266308588 97 90.13 96.5042308956197 -6.37423089561974 98 89.31 96.2510887168013 -6.94108871680126 99 105.12 110.034459121488 -4.91445912148766 100 125.83 130.397175013295 -4.56717501329474 101 135.81 137.351886988535 -1.54188698853532 102 142.43 152.537077115678 -10.1070771156777 103 163.39 143.609541498539 19.7804585014609 104 168.21 153.557081288762 14.652918711238 105 185.35 159.279311080191 26.0706889198091 106 188.5 181.532071854196 6.96792814580446 107 199.91 195.390755871801 4.51924412819882 108 210.73 201.285451050902 9.44454894909804 109 192.06 193.188295120410 -1.12829512041048 110 204.62 202.152421621502 2.46757837849789 111 235 207.729715548465 27.2702844515350 112 261.09 278.580727939982 -17.4907279399820 113 256.88 250.440566821516 6.4394331784842 114 251.53 239.363406876082 12.1665931239181 115 257.25 263.808754455495 -6.55875445549526 116 243.1 258.209884478188 -15.1098844781881 117 283.75 263.106967017295 20.6430329827046 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.00183744251006878 0.00367488502013756 0.998162557489931 11 0.000221701776074290 0.000443403552148581 0.999778298223926 12 2.33451928969247e-05 4.66903857938493e-05 0.999976654807103 13 3.57336492022593e-06 7.14672984045185e-06 0.99999642663508 14 3.06394047470637e-07 6.12788094941273e-07 0.999999693605953 15 4.99561525194126e-08 9.99123050388253e-08 0.999999950043847 16 1.14323401640995e-08 2.2864680328199e-08 0.99999998856766 17 1.29516620423317e-09 2.59033240846633e-09 0.999999998704834 18 1.491772056436e-09 2.983544112872e-09 0.999999998508228 19 6.20292343774259e-10 1.24058468754852e-09 0.999999999379708 20 1.52498801236632e-10 3.04997602473264e-10 0.999999999847501 21 3.0768729007166e-11 6.1537458014332e-11 0.999999999969231 22 5.92714862149371e-12 1.18542972429874e-11 0.999999999994073 23 1.24953074855443e-12 2.49906149710886e-12 0.99999999999875 24 2.04536285972470e-13 4.09072571944941e-13 0.999999999999795 25 3.06688200595873e-14 6.13376401191747e-14 0.99999999999997 26 4.02178632379643e-15 8.04357264759286e-15 0.999999999999996 27 5.12363086958004e-16 1.02472617391601e-15 1 28 8.25869525370473e-17 1.65173905074095e-16 1 29 1.31391125362607e-17 2.62782250725215e-17 1 30 2.64437799253747e-18 5.28875598507493e-18 1 31 8.21112760528427e-19 1.64222552105685e-18 1 32 3.93703941330233e-19 7.87407882660466e-19 1 33 4.98655682266382e-20 9.97311364532763e-20 1 34 1.13171744512182e-20 2.26343489024365e-20 1 35 1.57490029718757e-21 3.14980059437513e-21 1 36 1.75390705503886e-22 3.50781411007772e-22 1 37 1.88841144413662e-23 3.77682288827324e-23 1 38 3.87563321155669e-24 7.75126642311337e-24 1 39 1.7293354905297e-24 3.4586709810594e-24 1 40 2.69867380319165e-25 5.3973476063833e-25 1 41 2.15078301058695e-25 4.30156602117390e-25 1 42 1.41470135371147e-25 2.82940270742293e-25 1 43 3.15113342762535e-26 6.3022668552507e-26 1 44 3.51971520483203e-26 7.03943040966405e-26 1 45 1.69748712789882e-25 3.39497425579764e-25 1 46 7.60230415310389e-25 1.52046083062078e-24 1 47 2.74856515629569e-23 5.49713031259138e-23 1 48 1.05799555590122e-22 2.11599111180244e-22 1 49 2.20560275968689e-23 4.41120551937378e-23 1 50 3.81051658080217e-21 7.62103316160435e-21 1 51 2.59551611897551e-17 5.19103223795103e-17 1 52 7.79378756131742e-18 1.55875751226348e-17 1 53 1.38105003906226e-16 2.76210007812453e-16 1 54 5.58918046771283e-16 1.11783609354257e-15 1 55 3.89120820240851e-14 7.78241640481702e-14 0.999999999999961 56 3.23708955538501e-12 6.47417911077001e-12 0.999999999996763 57 1.49382775109723e-10 2.98765550219446e-10 0.999999999850617 58 9.03685216239956e-10 1.80737043247991e-09 0.999999999096315 59 5.63542187985485e-08 1.12708437597097e-07 0.999999943645781 60 9.28718361348122e-06 1.85743672269624e-05 0.999990712816387 61 1.35288877163936e-05 2.70577754327873e-05 0.999986471112284 62 1.19435497790641e-05 2.38870995581281e-05 0.99998805645022 63 7.19605565097034e-06 1.43921113019407e-05 0.99999280394435 64 1.17923595068115e-05 2.3584719013623e-05 0.999988207640493 65 9.34631721159982e-06 1.86926344231996e-05 0.999990653682788 66 5.54126474165325e-06 1.10825294833065e-05 0.999994458735258 67 1.24272568588675e-05 2.48545137177350e-05 0.999987572743141 68 1.06868432795311e-05 2.13736865590622e-05 0.99998931315672 69 1.22541230389465e-05 2.4508246077893e-05 0.999987745876961 70 8.78298240488041e-06 1.75659648097608e-05 0.999991217017595 71 8.91214064254e-06 1.782428128508e-05 0.999991087859357 72 5.50865263933336e-06 1.10173052786667e-05 0.99999449134736 73 6.98837987428219e-06 1.39767597485644e-05 0.999993011620126 74 5.39700697586155e-06 1.07940139517231e-05 0.999994602993024 75 5.04801482752983e-06 1.00960296550597e-05 0.999994951985173 76 6.62599306604586e-06 1.32519861320917e-05 0.999993374006934 77 2.92018662947762e-05 5.84037325895524e-05 0.999970798133705 78 2.87037939978037e-05 5.74075879956074e-05 0.999971296206002 79 1.65305372166246e-05 3.30610744332492e-05 0.999983469462783 80 1.05129880064772e-05 2.10259760129543e-05 0.999989487011994 81 1.10570455164677e-05 2.21140910329355e-05 0.999988942954483 82 1.38435489755978e-05 2.76870979511957e-05 0.999986156451024 83 9.68483644631961e-06 1.93696728926392e-05 0.999990315163554 84 1.24526463326877e-05 2.49052926653753e-05 0.999987547353667 85 0.0013646328552896 0.0027292657105792 0.99863536714471 86 0.0030291421046581 0.0060582842093162 0.996970857895342 87 0.00271337754516137 0.00542675509032275 0.997286622454839 88 0.00597407406837167 0.0119481481367433 0.994025925931628 89 0.0566824672887657 0.113364934577531 0.943317532711234 90 0.114284051818924 0.228568103637848 0.885715948181076 91 0.232054378645432 0.464108757290863 0.767945621354568 92 0.868290833755197 0.263418332489606 0.131709166244803 93 0.96799608903622 0.0640078219275609 0.0320039109637805 94 0.964812783652517 0.070374432694965 0.0351872163474825 95 0.950786445848101 0.0984271083037974 0.0492135541518987 96 0.938005463879672 0.123989072240657 0.0619945361203283 97 0.904898228057825 0.190203543884351 0.0951017719421755 98 0.87326221647838 0.25347556704324 0.12673778352162 99 0.85068771586579 0.298624568268419 0.149312284134210 100 0.797050191324409 0.405899617351182 0.202949808675591 101 0.804009903576664 0.391980192846672 0.195990096423336 102 0.766350529209496 0.467298941581008 0.233649470790504 103 0.743422009420212 0.513155981159577 0.256577990579788 104 0.652044394741462 0.695911210517075 0.347955605258538 105 0.591396326010698 0.817207347978604 0.408603673989302 106 0.509771215925727 0.980457568148546 0.490228784074273 107 0.409437985865344 0.818875971730689 0.590562014134655 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 78 0.795918367346939 NOK 5% type I error level 79 0.806122448979592 NOK 10% type I error level 82 0.836734693877551 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

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

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

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