Apple Inc - Multiple regression model 3

*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 10:47:59 +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/t1292323565t2bmzwfmbgutcaa.htm/, Retrieved Tue, 14 Dec 2010 11:46:17 +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/t1292323565t2bmzwfmbgutcaa.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 12 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation APPLE[t] = -148.389379308089 -7.06273178125015e-07VOLUME[t] -18.2064906878529REV.GROWTH[t] + 6.83287017871576MICROSOFT[t] + 0.0189694137186282NASDAQ[t] + 82.2743167281702INFLATION[t] -0.611219563554504CONS.CONF[t] + 1.65811032276012t + 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) -148.389379308089 17.626702 -8.4184 0 0 VOLUME -7.06273178125015e-07 0 -2.7448 0.007083 0.003542 REV.GROWTH -18.2064906878529 11.098972 -1.6404 0.10381 0.051905 MICROSOFT 6.83287017871576 1.127517 6.0601 0 0 NASDAQ 0.0189694137186282 0.014423 1.3152 0.191204 0.095602 INFLATION 82.2743167281702 218.096923 0.3772 0.706731 0.353365 CONS.CONF -0.611219563554504 0.17777 -3.4383 0.00083 0.000415 t 1.65811032276012 0.175297 9.4589 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.953327788951627 R-squared 0.908833873187398 Adjusted R-squared 0.902979167795763 F-TEST (value) 155.231358777832 F-TEST (DF numerator) 7 F-TEST (DF denominator) 109 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 23.6651650789451 Sum Squared Residuals 61044.364165296 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 10.81 -7.25629719972853 18.0662971997285 2 9.12 -11.8938172664146 21.0138172664146 3 11.03 -35.9471560679178 46.9771560679178 4 12.74 8.00592163519933 4.73407836480067 5 9.98 15.6435116393184 -5.66351163931843 6 11.62 25.9664908898376 -14.3464908898376 7 9.4 7.30583886604361 2.09416113395639 8 9.27 -14.0289816713552 23.2989816713552 9 7.76 -27.9641625344846 35.7241625344846 10 8.78 -6.28813727547428 15.0681372754743 11 10.65 18.348086752952 -7.698086752952 12 10.95 20.5019083917910 -9.55190839179096 13 12.36 13.7159467430930 -1.35594674309297 14 10.85 -1.66783320853261 12.5178332085326 15 11.84 2.39855802678801 9.441441973212 16 12.14 -18.9773771404354 31.1173771404354 17 11.65 -23.4477382787103 35.0977382787103 18 8.86 -15.9944636090714 24.8544636090714 19 7.63 -28.338082547479 35.968082547479 20 7.38 -18.3397337339018 25.7197337339018 21 7.25 -34.6075664130422 41.8575664130422 22 8.03 3.76377701645388 4.26622298354612 23 7.75 18.3074055892636 -10.5574055892636 24 7.16 3.84585997289421 3.31414002710579 25 7.18 -5.50795310910591 12.6879531091059 26 7.51 7.55899679398066 -0.0489967939806604 27 7.07 14.7729373320439 -7.70293733204385 28 7.11 7.11171336524198 -0.00171336524197807 29 8.98 0.727710762624938 8.25228923737506 30 9.53 14.5055971361330 -4.97559713613304 31 10.54 25.7610561328307 -15.2210561328307 32 11.31 27.4719510025171 -16.1619510025171 33 10.36 37.4505056793357 -27.0905056793357 34 11.44 26.1257305101077 -14.6857305101077 35 10.45 20.3567994669585 -9.90679946695851 36 10.69 32.8196379052649 -22.1296379052649 37 11.28 31.8784521117184 -20.5984521117184 38 11.96 34.33606389021 -22.37606389021 39 13.52 20.5235594591247 -7.00355945912474 40 12.89 26.8274749188411 -13.9374749188411 41 14.03 35.2727695470326 -21.2427695470326 42 16.27 41.7302900552444 -25.4602900552444 43 16.17 35.10920895656 -18.93920895656 44 17.25 36.0721675869396 -18.8221675869396 45 19.38 41.7793624298572 -22.3993624298572 46 26.2 32.2492387747191 -6.04923877471913 47 33.53 45.3859719600091 -11.8559719600091 48 32.2 45.1794136375662 -12.9794136375662 49 38.45 25.0629517745891 13.3870482254109 50 44.86 25.7593296484485 19.1006703515515 51 41.67 33.0273529483209 8.6426470516791 52 36.06 36.4939573251818 -0.433957325181782 53 39.76 48.6278629707337 -8.8678629707337 54 36.81 44.4741283007009 -7.66412830070093 55 42.65 58.8100399117758 -16.1600399117758 56 46.89 73.2396907361254 -26.3496907361254 57 53.61 71.7884825284871 -18.1784825284871 58 57.59 64.2220932111743 -6.63209321117433 59 67.82 78.7661913964846 -10.9461913964846 60 71.89 67.3788372262097 4.51116277379034 61 75.51 75.2996189284124 0.210381071587573 62 68.49 73.388425385918 -4.89842538591805 63 62.72 75.4018417627668 -12.6818417627668 64 70.39 56.8285475247992 13.5614524752008 65 59.77 58.8596137559758 0.910386244024246 66 57.27 61.9524316798376 -4.68243167983759 67 67.96 62.8315910583938 5.12840894160617 68 67.85 83.9814792821416 -16.1314792821416 69 76.98 89.8955146736437 -12.9155146736437 70 81.08 109.265356797849 -28.1853567978494 71 91.66 116.701207254884 -25.0412072548836 72 84.84 113.857358290331 -29.0173582903314 73 85.73 109.96614092045 -24.2361409204501 74 84.61 109.945971681826 -25.3359716818258 75 92.91 112.423071766595 -19.5130717665951 76 99.8 130.308730431090 -30.5087304310903 77 121.19 133.981578404424 -12.7915784044244 78 122.04 121.849847856193 0.190152143807101 79 131.76 111.721525241619 20.0384747583807 80 138.48 120.123245557877 18.3567544421226 81 153.47 131.945434721764 21.5245652782355 82 189.95 187.238258055067 2.71174194493264 83 182.22 164.040710945616 18.1792890543843 84 198.08 186.919658987854 11.1603410121462 85 135.36 143.895211762662 -8.5352117626616 86 125.02 127.024258859856 -2.00425885985605 87 143.5 145.916186263863 -2.41618626386300 88 173.95 158.356273936138 15.593726063862 89 188.75 169.170903623261 19.579096376739 90 167.44 164.906415959316 2.53358404068357 91 158.95 149.027997297698 9.92200270230243 92 169.53 164.99220641348 4.53779358651994 93 113.66 140.975753105341 -27.3157531053406 94 107.59 114.694156140423 -7.10415614042257 95 92.67 109.629811992367 -16.9598119923669 96 85.35 117.941860679225 -32.5918606792251 97 90.13 123.718469444614 -33.5884694446144 98 89.31 109.429071953966 -20.1190719539663 99 105.12 128.092141566345 -22.9721415663454 100 125.83 137.798060082971 -11.9680600829715 101 135.81 138.888140749212 -3.07814074921229 102 142.43 161.471995142760 -19.0419951427598 103 163.39 170.969958455698 -7.57995845569842 104 168.21 180.631328480647 -12.4213284806473 105 185.35 189.909342094546 -4.55934209454584 106 188.5 199.505779171235 -11.0057791712348 107 199.91 220.124438930834 -20.2144389308344 108 210.73 227.941368013672 -17.2113680136724 109 192.06 198.723587232262 -6.66358723226224 110 204.62 218.857212908644 -14.2372129086437 111 235 225.424100221957 9.57589977804273 112 261.09 229.312975872687 31.7770241273128 113 256.88 184.458348804666 72.4216511953336 114 251.53 172.698121670036 78.8318783299642 115 257.25 197.816228826709 59.4337711732909 116 243.1 188.297365141005 54.802634858995 117 283.75 200.922235375498 82.8277646245023 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.00027663772149865 0.0005532754429973 0.999723362278501 12 1.32118051307657e-05 2.64236102615314e-05 0.99998678819487 13 6.18289109599182e-07 1.23657821919836e-06 0.99999938171089 14 2.52697611101983e-08 5.05395222203965e-08 0.99999997473024 15 2.62517295006229e-09 5.25034590012458e-09 0.999999997374827 16 3.52460288704446e-10 7.04920577408892e-10 0.99999999964754 17 1.90491888485377e-11 3.80983776970755e-11 0.99999999998095 18 1.64813992186741e-11 3.29627984373482e-11 0.999999999983519 19 1.57505323327944e-12 3.15010646655888e-12 0.999999999998425 20 1.13852461647358e-13 2.27704923294717e-13 0.999999999999886 21 1.22924062596919e-14 2.45848125193837e-14 0.999999999999988 22 1.62993357055265e-15 3.2598671411053e-15 0.999999999999998 23 9.85205552349464e-17 1.97041110469893e-16 1 24 8.05964884453112e-18 1.61192976890622e-17 1 25 1.19283679518757e-18 2.38567359037513e-18 1 26 3.06554929894651e-19 6.13109859789302e-19 1 27 3.65319242510013e-20 7.30638485020025e-20 1 28 7.62891516151882e-21 1.52578303230376e-20 1 29 1.98339526256844e-21 3.96679052513689e-21 1 30 5.45257125979069e-22 1.09051425195814e-21 1 31 9.93543838435353e-23 1.98708767687071e-22 1 32 1.81163750547319e-23 3.62327501094637e-23 1 33 2.36978147368201e-24 4.73956294736403e-24 1 34 1.95322666949359e-25 3.90645333898717e-25 1 35 2.72452494956233e-26 5.44904989912465e-26 1 36 2.55197001591057e-27 5.10394003182115e-27 1 37 1.87001595046548e-28 3.74003190093096e-28 1 38 1.26037464609739e-29 2.52074929219479e-29 1 39 7.98055063650405e-30 1.59611012730081e-29 1 40 3.58711932288152e-30 7.17423864576304e-30 1 41 5.95626010672981e-30 1.19125202134596e-29 1 42 9.45641009499601e-30 1.89128201899920e-29 1 43 4.52361170611147e-30 9.04722341222295e-30 1 44 2.12069982473014e-29 4.24139964946028e-29 1 45 1.00046488107864e-27 2.00092976215728e-27 1 46 1.68171575568683e-26 3.36343151137367e-26 1 47 3.26126345211141e-24 6.52252690422283e-24 1 48 5.5963568993283e-24 1.11927137986566e-23 1 49 1.42366175256984e-24 2.84732350513969e-24 1 50 9.2074053761228e-22 1.84148107522456e-21 1 51 1.66390484630104e-18 3.32780969260208e-18 1 52 7.71090430554334e-19 1.54218086110867e-18 1 53 8.61408178715721e-18 1.72281635743144e-17 1 54 1.13945183337120e-17 2.27890366674241e-17 1 55 1.58869958305457e-15 3.17739916610915e-15 0.999999999999998 56 8.12692986791222e-14 1.62538597358244e-13 0.999999999999919 57 1.09244669772712e-11 2.18489339545423e-11 0.999999999989076 58 1.75361428704096e-10 3.50722857408192e-10 0.999999999824638 59 6.80901079946783e-08 1.36180215989357e-07 0.999999931909892 60 8.1482859241526e-06 1.62965718483052e-05 0.999991851714076 61 4.95780565604735e-05 9.9156113120947e-05 0.99995042194344 62 8.15463841357615e-05 0.000163092768271523 0.999918453615864 63 5.13341323143487e-05 0.000102668264628697 0.999948665867686 64 5.16580006943093e-05 0.000103316001388619 0.999948341999306 65 3.92578576286463e-05 7.85157152572926e-05 0.999960742142371 66 2.67171750013388e-05 5.34343500026776e-05 0.999973282824999 67 0.000133574397831221 0.000267148795662442 0.999866425602169 68 0.000259886032146504 0.000519772064293007 0.999740113967853 69 0.000588593631742839 0.00117718726348568 0.999411406368257 70 0.000702568829928276 0.00140513765985655 0.999297431170072 71 0.00109786777604698 0.00219573555209396 0.998902132223953 72 0.000772207890773714 0.00154441578154743 0.999227792109226 73 0.000870301131224275 0.00174060226244855 0.999129698868776 74 0.000548902296640388 0.00109780459328078 0.99945109770336 75 0.000492466571749745 0.00098493314349949 0.99950753342825 76 0.000592931765124049 0.00118586353024810 0.999407068234876 77 0.00215017485955428 0.00430034971910855 0.997849825140446 78 0.00666031708653628 0.0133206341730726 0.993339682913464 79 0.0187664262382322 0.0375328524764643 0.981233573761768 80 0.0678631639292491 0.135726327858498 0.93213683607075 81 0.369416524229525 0.73883304845905 0.630583475770475 82 0.59206434717558 0.81587130564884 0.40793565282442 83 0.669134932680944 0.661730134638112 0.330865067319056 84 0.973241352869706 0.0535172942605871 0.0267586471302936 85 0.969529782712866 0.0609404345742687 0.0304702172871344 86 0.96612510056239 0.067749798875218 0.033874899437609 87 0.959062738516025 0.0818745229679498 0.0409372614839749 88 0.967913954566471 0.0641720908670581 0.0320860454335290 89 0.972522940552008 0.0549541188959833 0.0274770594479916 90 0.986905380380195 0.0261892392396093 0.0130946196198046 91 0.982182236930125 0.0356355261397502 0.0178177630698751 92 0.997710690231105 0.00457861953778968 0.00228930976889484 93 0.996545440135187 0.00690911972962623 0.00345455986481312 94 0.995731214324004 0.00853757135199298 0.00426878567599649 95 0.994266267702411 0.0114674645951771 0.00573373229758854 96 0.991721645529233 0.0165567089415344 0.00827835447076718 97 0.984440351583452 0.0311192968330952 0.0155596484165476 98 0.973677681059196 0.0526446378816076 0.0263223189408038 99 0.96386822456361 0.072263550872781 0.0361317754363905 100 0.958327434381192 0.0833451312376168 0.0416725656188084 101 0.934119733111594 0.131760533776811 0.0658802668884057 102 0.884963201976872 0.230073596046256 0.115036798023128 103 0.824710689210805 0.35057862157839 0.175289310789195 104 0.729268926311728 0.541462147376544 0.270731073688272 105 0.87212375223441 0.255752495531179 0.127876247765589 106 0.971231455859265 0.0575370882814706 0.0287685441407353 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 70 0.729166666666667 NOK 5% type I error level 77 0.802083333333333 NOK 10% type I error level 87 0.90625 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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