Apple Inc - Multiple regression model 6

*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 11:31:30 +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/t12923263768fzn4v41pviqxik.htm/, Retrieved Tue, 14 Dec 2010 12:32:56 +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/t12923263768fzn4v41pviqxik.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 24.45 115.7 9.12 -0,2643 0 0 14163200 23.62 109.2 11.03 -0,2643 0 0 18184800 21.90 116.9 12.74 -0,1918 0 0 20810300 27.12 109.9 9.98 -0,1918 0 0 12843000 27.70 116.1 11.62 -0,1918 0 0 13866700 29.23 118.9 9.40 -0,2246 0 0 15119200 26.50 116.3 9.27 -0,2246 0 0 8301600 22.84 114.0 7.76 -0,2246 0 0 14039600 20.49 97.0 8.78 0,3654 0 0 12139700 23.28 85.3 10.65 0,3654 0 0 9649000 25.71 84.9 10.95 0,3654 0 0 8513600 26.52 94.6 12.36 0,0447 0 0 15278600 25.51 97.8 10.85 0,0447 0 0 15590900 23.36 95.0 11.84 0,0447 0 0 9691100 24.15 110.7 12.14 -0,0312 0 0 10882700 20.92 108.5 11.65 -0,0312 0 0 10294800 20.38 110.3 8.86 -0,0312 0 0 16031900 21.90 106.3 7.63 -0,0048 0 0 13683600 19.21 97.4 7.38 -0,0048 0 0 8677200 19.65 94.5 7.25 -0,0048 0 0 9874100 17.51 93.7 8.03 0,0705 0 0 10725500 21.41 79.6 7.75 0,0705 0 0 8348400 23.09 84.9 7.16 0,0705 0 0 8046200 20.70 80.7 7.18 -0,0134 0 0 10862300 19.00 78.8 7.51 -0,0134 0 0 8100300 19.04 64.8 7.07 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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation APPLE[t] = -127.447605621375 -31.2727304377682REV.GROWTH[t] + 63.5049184403004IPHONE[t] + 105.207064497490IPAD[t] -3.07859516993699e-07VOLUME[t] + 6.18588427968051MICROSOFT[t] -0.254623501864903CONS.CONF[t] + 1.43045995687761t + 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) -127.447605621375 11.410672 -11.1692 0 0 REV.GROWTH -31.2727304377682 6.348574 -4.9259 3e-06 2e-06 IPHONE 63.5049184403004 12.18411 5.2121 1e-06 0 IPAD 105.207064497490 12.285426 8.5636 0 0 VOLUME -3.07859516993699e-07 0 -2.0578 0.041991 0.020995 MICROSOFT 6.18588427968051 0.59431 10.4085 0 0 CONS.CONF -0.254623501864903 0.101085 -2.5189 0.013223 0.006611 t 1.43045995687761 0.085539 16.7229 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.982398930477056 R-squared 0.965107658602464 Adjusted R-squared 0.96286686603565 F-TEST (value) 430.699241373587 F-TEST (DF numerator) 7 F-TEST (DF denominator) 109 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 14.6405651902178 Sum Squared Residuals 23363.730250703 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 10.81 -3.52890799709945 14.3389079970995 2 9.12 -2.37587868159683 11.4958786815968 3 11.03 -14.7838284836713 25.8138284836713 4 12.74 17.6437538075877 -4.9037538075877 5 9.98 23.5361700648615 -13.5561700648615 6 11.62 33.4029313768821 -21.7829313768821 7 9.4 19.2480998684049 -9.8480998684049 8 9.27 0.722720458997381 8.54727954100262 9 7.76 -9.82154601818074 17.5815460181807 10 8.78 -6.01938261112203 14.7993826111220 11 10.65 11.3114112451014 -0.661411245101382 12 10.95 15.6321331960253 -4.68213319602528 13 12.36 17.9465498433878 -5.5865498433878 14 10.85 6.6941598770169 4.1558401229831 15 11.84 10.8301890139226 1.00981098607745 16 12.14 -5.15283070868816 17.2928307086882 17 11.65 -7.34007995615426 18.9900799561543 18 8.86 2.74519727835277 6.11480272164722 19 7.63 -10.3008758903133 17.9308758903133 20 7.38 -3.86895080909082 11.2489508090908 21 7.25 -15.8410614651273 23.0910614651273 22 8.03 10.6875903640670 -2.65759036406703 23 7.75 21.8926442087696 -14.1426442087696 24 7.16 9.7012945910789 -2.54129459107890 25 7.18 2.85635482396577 4.32364517603423 26 7.51 8.94928716407582 -1.43928716407582 27 7.07 14.0319077973756 -6.9619077973756 28 7.11 12.1886840265273 -5.0786840265273 29 8.98 6.6437961713321 2.3362038286679 30 9.53 15.7779379773051 -6.24793797730507 31 10.54 19.8397332947637 -9.2997332947637 32 11.31 20.9957139825741 -9.68571398257413 33 10.36 29.4382477747903 -19.0782477747903 34 11.44 16.2156299359501 -4.7756299359501 35 10.45 13.2784400100617 -2.82844001006168 36 10.69 23.3112816912528 -12.6212816912528 37 11.28 25.4657791781929 -14.1857791781930 38 11.96 24.6191535860914 -12.6591535860914 39 13.52 15.4448586505344 -1.92485865053439 40 12.89 22.2606125802211 -9.37061258022111 41 14.03 26.0841719071735 -12.0541719071735 42 16.27 35.1675202936904 -18.8975202936904 43 16.17 32.7013162963232 -16.5313162963232 44 17.25 31.4207303475529 -14.1707303475529 45 19.38 35.0443972499164 -15.6643972499164 46 26.2 22.8848860454895 3.31511395451047 47 33.53 33.1612244447935 0.368775555206526 48 32.2 33.0845786737927 -0.884578673792687 49 38.45 25.7643971001291 12.6856028998709 50 44.86 23.8263941330568 21.0336058669432 51 41.67 25.1827287537761 16.4872712462239 52 36.06 30.0091705588186 6.05082944118137 53 39.76 36.7826357675184 2.97736423248156 54 36.81 33.1157651827117 3.69423481728828 55 42.65 45.2637148966472 -2.61371489664724 56 46.89 57.7743583255711 -10.8843583255711 57 53.61 52.4525593932198 1.15744060678020 58 57.59 48.2019671476843 9.3880328523157 59 67.82 60.8228179415662 6.99718205843374 60 71.89 52.3830217578948 19.5069782421052 61 75.51 68.4886969517907 7.0213030482093 62 68.49 65.160640794458 3.32935920554197 63 62.72 67.4001322308557 -4.68013223085572 64 70.39 53.5266426166795 16.8633573833205 65 59.77 51.8555183802917 7.9144816197083 66 57.27 55.8500004459759 1.41999955402413 67 67.96 58.2115104043816 9.74848959561838 68 67.85 72.5453326358316 -4.69533263583160 69 76.98 80.7872560193313 -3.80725601933125 70 81.08 95.1196855081471 -14.0396855081471 71 91.66 100.509494642391 -8.8494946423906 72 84.84 101.472422097319 -16.6324220973192 73 85.73 117.321247612044 -31.5912476120443 74 84.61 110.607139940152 -25.9971399401515 75 92.91 111.286586907601 -18.3765869076007 76 99.8 127.600608604569 -27.800608604569 77 121.19 131.769344260362 -10.5793442603624 78 122.04 123.505275038774 -1.46527503877421 79 131.76 122.745569565414 9.0144304345864 80 138.48 126.285730425114 12.1942695748859 81 153.47 133.135127577878 20.3348724221221 82 189.95 182.338880576993 7.61111942300712 83 182.22 164.908258181345 17.3117418186554 84 198.08 181.806106516856 16.2738934831442 85 135.36 158.600532567402 -23.2405325674016 86 125.02 136.913798436335 -11.8937984363352 87 143.5 149.279735341508 -5.77973534150823 88 173.95 148.651987313169 25.2980126868310 89 188.75 152.730378957901 36.0196210420988 90 167.44 150.627090531793 16.8129094682072 91 158.95 153.985976269473 4.964023730527 92 169.53 166.533537595656 2.99646240434364 93 113.66 157.411974154532 -43.751974154532 94 107.59 112.346326078964 -4.75632607896362 95 92.67 107.350128115044 -14.6801281150444 96 85.35 109.241734896144 -23.8917348961441 97 90.13 105.699584728280 -15.5695847282803 98 89.31 98.5717858659368 -9.26178586593684 99 105.12 113.273341476923 -8.15334147692337 100 125.83 128.771285845827 -2.94128584582696 101 135.81 132.215022215934 3.59497778406606 102 142.43 151.202637790153 -8.77263779015328 103 163.39 145.161943758117 18.2280562418828 104 168.21 153.634935091603 14.5750649083966 105 185.35 160.704186121382 24.6458138786181 106 188.5 182.531107940535 5.96889205946534 107 199.91 196.378935616161 3.53106438383950 108 210.73 202.551797220621 8.17820277937923 109 192.06 191.044373971335 1.01562602866539 110 204.62 201.789564394526 2.83043560547424 111 235 206.004460059641 28.9955399403592 112 261.09 281.497277100175 -20.4072771001755 113 256.88 249.885062486601 6.99493751339883 114 251.53 238.084736740704 13.4452632592964 115 257.25 265.108249436415 -7.85824943641501 116 243.1 255.863746085701 -12.7637460857006 117 283.75 263.158280425202 20.5917195747977 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.00172421722144613 0.00344843444289225 0.998275782778554 12 0.000159014550034780 0.000318029100069559 0.999840985449965 13 2.19781136117100e-05 4.39562272234199e-05 0.999978021886388 14 1.80747137492613e-06 3.61494274985226e-06 0.999998192528625 15 1.48095854095045e-07 2.96191708190091e-07 0.999999851904146 16 1.62403183391325e-08 3.24806366782649e-08 0.999999983759682 17 1.3352375949971e-09 2.6704751899942e-09 0.999999998664762 18 3.13486898889623e-09 6.26973797779245e-09 0.99999999686513 19 9.65149427177646e-10 1.93029885435529e-09 0.99999999903485 20 1.37234468248636e-10 2.74468936497273e-10 0.999999999862766 21 2.50893382217956e-11 5.01786764435912e-11 0.99999999997491 22 2.80766423512544e-12 5.61532847025089e-12 0.999999999997192 23 2.83281012592729e-13 5.66562025185459e-13 0.999999999999717 24 2.69761858333651e-14 5.39523716667302e-14 0.999999999999973 25 3.28126023589668e-15 6.56252047179336e-15 0.999999999999997 26 1.31474501457829e-15 2.62949002915658e-15 0.999999999999999 27 1.9454741347776e-16 3.8909482695552e-16 1 28 3.56554857552281e-17 7.13109715104562e-17 1 29 4.54223351745534e-18 9.08446703491068e-18 1 30 6.63375737407224e-19 1.32675147481445e-18 1 31 1.78327103826010e-19 3.56654207652019e-19 1 32 5.16697198107829e-20 1.03339439621566e-19 1 33 5.53281867035672e-21 1.10656373407134e-20 1 34 6.29894793638778e-22 1.25978958727756e-21 1 35 8.28006689157946e-23 1.65601337831589e-22 1 36 9.13322426216869e-24 1.82664485243374e-23 1 37 9.39441983003671e-25 1.87888396600734e-24 1 38 1.15967496837846e-25 2.31934993675693e-25 1 39 4.01945609348265e-26 8.0389121869653e-26 1 40 4.23126343457939e-27 8.46252686915878e-27 1 41 1.36030972393050e-27 2.72061944786100e-27 1 42 3.3214290653706e-28 6.6428581307412e-28 1 43 3.36045420458172e-29 6.72090840916343e-29 1 44 2.08562635539501e-29 4.17125271079001e-29 1 45 1.44923066830914e-28 2.89846133661828e-28 1 46 5.68034692032284e-27 1.13606938406457e-26 1 47 2.7182989075141e-24 5.4365978150282e-24 1 48 8.20410853305554e-24 1.64082170661111e-23 1 49 1.40402239460520e-24 2.80804478921041e-24 1 50 2.92331202474042e-22 5.84662404948083e-22 1 51 7.24764891310876e-19 1.44952978262175e-18 1 52 1.74916767799813e-19 3.49833535599627e-19 1 53 1.67359343272545e-18 3.3471868654509e-18 1 54 2.12142414982258e-18 4.24284829964517e-18 1 55 1.73938793648101e-16 3.47877587296201e-16 1 56 2.31020939931033e-14 4.62041879862065e-14 0.999999999999977 57 1.05605933130825e-11 2.1121186626165e-11 0.99999999998944 58 1.22265760983376e-10 2.44531521966752e-10 0.999999999877734 59 9.81743092417652e-09 1.96348618483530e-08 0.99999999018257 60 4.13254266885094e-07 8.26508533770188e-07 0.999999586745733 61 4.45090412790688e-07 8.90180825581376e-07 0.999999554909587 62 2.56446266192424e-07 5.12892532384848e-07 0.999999743553734 63 1.23683676775800e-07 2.47367353551601e-07 0.999999876316323 64 1.85246051944528e-07 3.70492103889057e-07 0.999999814753948 65 1.61049310624433e-07 3.22098621248867e-07 0.99999983895069 66 1.00732571483350e-07 2.01465142966699e-07 0.999999899267428 67 1.36442823570598e-07 2.72885647141197e-07 0.999999863557176 68 8.00211787586437e-08 1.60042357517287e-07 0.999999919978821 69 5.78413163239539e-08 1.15682632647908e-07 0.999999942158684 70 3.56849524634906e-08 7.13699049269812e-08 0.999999964315048 71 3.8648562224383e-08 7.7297124448766e-08 0.999999961351438 72 1.73534869044673e-08 3.47069738089345e-08 0.999999982646513 73 2.52776722879697e-08 5.05553445759394e-08 0.999999974722328 74 5.04973047241453e-08 1.00994609448291e-07 0.999999949502695 75 9.67122352617275e-08 1.93424470523455e-07 0.999999903287765 76 1.33861910882730e-06 2.67723821765460e-06 0.999998661380891 77 1.57740988663506e-05 3.15481977327011e-05 0.999984225901134 78 2.46213946203428e-05 4.92427892406855e-05 0.99997537860538 79 2.4582303951525e-05 4.916460790305e-05 0.999975417696048 80 3.40899336086989e-05 6.81798672173979e-05 0.999965910066391 81 0.000127239228766631 0.000254478457533263 0.999872760771233 82 0.000490047126749211 0.000980094253498421 0.99950995287325 83 0.000538176018742943 0.00107635203748589 0.999461823981257 84 0.00110638083506556 0.00221276167013111 0.998893619164934 85 0.0102738434999139 0.0205476869998278 0.989726156500086 86 0.0131379923817859 0.0262759847635718 0.986862007618214 87 0.0108092467227036 0.0216184934454073 0.989190753277296 88 0.0226841812674819 0.0453683625349638 0.977315818732518 89 0.122861266769115 0.245722533538229 0.877138733230885 90 0.198969279508304 0.397938559016608 0.801030720491696 91 0.335282160719634 0.670564321439268 0.664717839280366 92 0.91991594501348 0.160168109973038 0.0800840549865192 93 0.973629244576231 0.0527415108475374 0.0263707554237687 94 0.97249616939097 0.0550076612180592 0.0275038306090296 95 0.958866891065904 0.0822662178681917 0.0411331089340959 96 0.950376940360434 0.0992461192791326 0.0496230596395663 97 0.92586406548765 0.148271869024699 0.0741359345123494 98 0.888347990958069 0.223304018083862 0.111652009041931 99 0.852041150810986 0.295917698378029 0.147958849189014 100 0.794687609788068 0.410624780423864 0.205312390211932 101 0.80640443493608 0.38719113012784 0.19359556506392 102 0.765948155527825 0.46810368894435 0.234051844472175 103 0.712724314586299 0.574551370827401 0.287275685413701 104 0.604754486864996 0.790491026270008 0.395245513135004 105 0.51448896180029 0.97102207639942 0.48551103819971 106 0.408821795702961 0.817643591405922 0.591178204297039 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 74 0.770833333333333 NOK 5% type I error level 78 0.8125 NOK 10% type I error level 82 0.854166666666667 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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