Paper - 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: Wed, 22 Dec 2010 08:53:37 +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/t12930079167okiun7fqxsyown.htm/, Retrieved Wed, 22 Dec 2010 09:52:00 +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/t12930079167okiun7fqxsyown.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

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 7 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation Apple[t] = -132.456012888064 -35.1959474240156Omzetgroei[t] + 75.106292792514Omzetgroei_iPhone[t] + 98.1776908801455Omzetgroei_iPad[t] -3.56318540919011e-07Volume[t] + 5.89684838683875Microsoft[t] -0.135120938035962Consumentenvertrouwen[t] + 1.48782043084434t + 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) -132.456012888064 10.302002 -12.8573 0 0 Omzetgroei -35.1959474240156 6.039471 -5.8277 0 0 Omzetgroei_iPhone 75.106292792514 11.216498 6.6961 0 0 Omzetgroei_iPad 98.1776908801455 11.614409 8.4531 0 0 Volume -3.56318540919011e-07 0 -2.5224 0.013099 0.006549 Microsoft 5.89684838683875 0.553177 10.66 0 0 Consumentenvertrouwen -0.135120938035962 0.100211 -1.3484 0.180335 0.090168 t 1.48782043084434 0.07991 18.6187 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.984436739872282 R-squared 0.969115694810368 Adjusted R-squared 0.967132299064244 F-TEST (value) 488.614386062204 F-TEST (DF numerator) 7 F-TEST (DF denominator) 109 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 13.7740515450263 Sum Squared Residuals 20679.9700601897 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 10.81 -1.87384787361592 12.6838478736159 2 9.12 -0.696341417347963 9.81634141734796 3 11.03 -11.8245020789032 22.8545020789032 4 12.74 17.9034929800672 -5.1634929800672 5 9.98 24.8126323705191 -14.8326323705191 6 11.62 34.5795289163873 -22.9595289163873 7 9.4 21.028405793062 -11.628405793062 8 9.27 3.67377657012857 5.59622342987143 9 7.76 -8.44349654928002 16.20349654928 10 8.78 -9.011193528412 17.791193528412 11 10.65 7.74749944753186 2.90250055246814 12 10.95 13.1056580441262 -2.15565804412618 13 12.36 17.082120012113 -4.72212001211301 14 10.85 6.15877675742573 4.69122324257427 15 11.84 12.285916814422 -0.445916814422048 16 12.14 -2.72903374441993 14.8690337444199 17 11.65 -4.45924946072696 16.109249460727 18 8.86 4.48802916914964 4.37197083085036 19 7.63 -8.77652639443607 16.4065263944361 20 7.38 -2.51836880982148 9.89836880982148 21 7.25 -13.9681848380092 21.2181848380092 22 8.03 9.46892508104644 -1.43892508104644 23 7.75 20.9943146338078 -13.2443146338078 24 7.16 9.06385482292435 -1.90385482292435 25 7.18 2.73327412420405 4.44672587579595 26 7.51 7.33281343304369 0.177186566956308 27 7.07 11.9873686018732 -4.91736860187317 28 7.11 11.5321677602838 -4.42216776028384 29 8.98 6.33389411342526 2.64610588657474 30 9.53 15.6739777983254 -6.14397779832542 31 10.54 18.5069985170053 -7.96699851700535 32 11.31 20.3135238975293 -9.00352389752929 33 10.36 27.8665127632704 -17.5065127632705 34 11.44 14.8468899485709 -3.40688994857093 35 10.45 13.4450060471332 -2.99500604713323 36 10.69 23.0695494673374 -12.3795494673374 37 11.28 25.7864293629237 -14.5064293629237 38 11.96 24.5271266268066 -12.5671266268066 39 13.52 15.369862599483 -1.84986259948302 40 12.89 22.5933172097354 -9.70331720973538 41 14.03 26.7658654525098 -12.7358654525098 42 16.27 36.2851935486963 -20.0151935486963 43 16.17 33.8681959221929 -17.6981959221929 44 17.25 32.2249937945656 -14.9749937945656 45 19.38 35.5785166937051 -16.1985166937051 46 26.2 20.7717413321251 5.42825866787494 47 33.53 30.4927264118225 3.03727358817749 48 32.2 31.9490090665818 0.250990933418225 49 38.45 24.1559001184319 14.2940998815681 50 44.86 22.8203020450774 22.0396979549226 51 41.67 25.1353743599591 16.5346256400409 52 36.06 28.42437616147 7.63562383853002 53 39.76 36.3332619848631 3.42673801513693 54 36.81 33.5146815864095 3.29531841359055 55 42.65 45.9255925532733 -3.27559255327335 56 46.89 58.4195012266221 -11.5295012266221 57 53.61 51.1138399492462 2.49616005075381 58 57.59 45.7793615174686 11.8106384825314 59 67.82 59.9355956195352 7.88440438046478 60 71.89 52.6450461671659 19.2449538328341 61 75.51 68.9830073442615 6.52699265573855 62 68.49 65.6853844396575 2.80461556034251 63 62.72 68.4841289094483 -5.76412890944833 64 70.39 56.0592236434632 14.3307763565368 65 59.77 54.784936184807 4.985063815193 66 57.27 58.6423140454912 -1.37231404549121 67 67.96 60.6048300215046 7.3551699784954 68 67.85 73.9215533774861 -6.07155337748609 69 76.98 82.283267139184 -5.30326713918406 70 81.08 96.9091518675447 -15.8291518675447 71 91.66 102.14017856041 -10.4801785604105 72 84.84 103.24342438359 -18.4034243835898 73 85.73 104.62941104787 -18.8994110478695 74 84.61 99.8807987398922 -15.2707987398922 75 92.91 100.374211535294 -7.46421153529386 76 99.8 111.97622807706 -12.1762280770598 77 121.19 115.986867157956 5.20313284204406 78 122.04 127.687049988085 -5.64704998808459 79 131.76 128.502876773083 3.25712322691724 80 138.48 131.616522245755 6.86347775424504 81 153.47 137.531519293286 15.9384807067142 82 189.95 185.1954588106 4.75454118940041 83 182.22 167.334363465039 14.8856365349614 84 198.08 184.797127804066 13.2828721959338 85 135.36 161.019072281618 -25.6590722816176 86 125.02 140.204125236087 -15.184125236087 87 143.5 151.258864524392 -7.75886452439173 88 173.95 149.234118861979 24.7158811380214 89 188.75 153.086450336237 35.6635496637631 90 167.44 150.371435611329 17.0685643886708 91 158.95 157.466612563758 1.4833874362416 92 169.53 170.879558491093 -1.3495584910935 93 113.66 161.336946005114 -47.6769460051138 94 107.59 109.05551455419 -1.46551455419034 95 92.67 106.45246830808 -13.7824683080803 96 85.35 108.403079380345 -23.0530793803452 97 90.13 101.440734342265 -11.3107343422648 98 89.31 97.4450804694233 -8.13508046942326 99 105.12 111.846032938571 -6.72603293857052 100 125.83 129.924009834042 -4.09400983404163 101 135.81 135.041733301068 0.768266698932307 102 142.43 152.449590568733 -10.0195905687326 103 163.39 144.696770920484 18.6932290795164 104 168.21 153.915531410027 14.294468589973 105 185.35 160.440941696091 24.9090583039088 106 188.5 182.989264219611 5.51073578038926 107 199.91 196.917192380859 2.99280761914123 108 210.73 203.046774386636 7.68322561336415 109 192.06 193.296248672735 -1.23624867273481 110 204.62 203.182952371273 1.43704762872715 111 235 208.064762318805 26.935237681195 112 261.09 280.453843601117 -19.3638436011166 113 256.88 250.241209268361 6.63879073163879 114 251.53 238.560290149792 12.9697098502083 115 257.25 264.520483116307 -7.270483116307 116 243.1 256.769499297147 -13.6694992971473 117 283.75 263.03499922719 20.7150007728095 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.00209111603511316 0.00418223207022633 0.997908883964887 12 0.000205292640611097 0.000410585281222195 0.999794707359389 13 3.02374952689576e-05 6.04749905379151e-05 0.999969762504731 14 2.65306017886598e-06 5.30612035773195e-06 0.999997346939821 15 2.3152069786842e-07 4.63041395736839e-07 0.999999768479302 16 2.65278055673481e-08 5.30556111346962e-08 0.999999973472194 17 2.24333896712527e-09 4.48667793425054e-09 0.99999999775666 18 5.47241072341187e-09 1.09448214468237e-08 0.99999999452759 19 1.728813211714e-09 3.45762642342801e-09 0.999999998271187 20 2.51815580105652e-10 5.03631160211304e-10 0.999999999748184 21 4.53834352371379e-11 9.07668704742758e-11 0.999999999954617 22 5.31257183569923e-12 1.06251436713985e-11 0.999999999994687 23 5.63850237258657e-13 1.12770047451731e-12 0.999999999999436 24 5.5886391675264e-14 1.11772783350528e-13 0.999999999999944 25 6.91767410792069e-15 1.38353482158414e-14 0.999999999999993 26 2.87836360279027e-15 5.75672720558054e-15 0.999999999999997 27 4.44042493306443e-16 8.88084986612887e-16 1 28 8.36819064195867e-17 1.67363812839173e-16 1 29 1.06980664579051e-17 2.13961329158102e-17 1 30 1.56678830747061e-18 3.13357661494121e-18 1 31 4.33485563868307e-19 8.66971127736614e-19 1 32 1.28015662842586e-19 2.56031325685173e-19 1 33 1.4326657924169e-20 2.8653315848338e-20 1 34 1.68412646263596e-21 3.36825292527192e-21 1 35 2.2251029540008e-22 4.4502059080016e-22 1 36 2.54966286416401e-23 5.09932572832803e-23 1 37 2.72730125739697e-24 5.45460251479393e-24 1 38 3.46036371629405e-25 6.92072743258809e-25 1 39 1.13035323634225e-25 2.26070647268451e-25 1 40 1.15257098492381e-26 2.30514196984762e-26 1 41 3.57112633878659e-27 7.14225267757317e-27 1 42 9.52634497205153e-28 1.90526899441031e-27 1 43 1.09493672444003e-28 2.18987344888005e-28 1 44 7.73174922328567e-29 1.54634984465713e-28 1 45 7.56084926846733e-28 1.51216985369347e-27 1 46 3.07245039373297e-26 6.14490078746595e-26 1 47 1.49542544649407e-23 2.99085089298815e-23 1 48 4.6197762902312e-23 9.2395525804624e-23 1 49 9.84412755972917e-24 1.96882551194583e-23 1 50 3.06863371054934e-21 6.13726742109869e-21 1 51 8.4681150814369e-18 1.69362301628738e-17 1 52 2.47017604839935e-18 4.9403520967987e-18 1 53 2.39227307859078e-17 4.78454615718156e-17 1 54 3.19854275333454e-17 6.39708550666908e-17 1 55 2.57437644306072e-15 5.14875288612145e-15 0.999999999999997 56 3.55116421357981e-13 7.10232842715963e-13 0.999999999999645 57 1.38994548078205e-10 2.77989096156409e-10 0.999999999861005 58 1.78444915496861e-09 3.56889830993723e-09 0.99999999821555 59 1.14882186064481e-07 2.29764372128962e-07 0.999999885117814 60 1.25132287586865e-05 2.5026457517373e-05 0.999987486771241 61 1.7893358715915e-05 3.57867174318299e-05 0.999982106641284 62 1.44516297491027e-05 2.89032594982055e-05 0.999985548370251 63 8.31815018728423e-06 1.66363003745685e-05 0.999991681849813 64 1.39073266315129e-05 2.78146532630257e-05 0.999986092673368 65 1.08144442923534e-05 2.16288885847068e-05 0.999989185555708 66 6.62336561012487e-06 1.32467312202497e-05 0.99999337663439 67 1.65957092046844e-05 3.31914184093688e-05 0.999983404290795 68 1.35900279246481e-05 2.71800558492963e-05 0.999986409972075 69 1.51875253832336e-05 3.03750507664671e-05 0.999984812474617 70 1.17096876209271e-05 2.34193752418541e-05 0.99998829031238 71 1.27538319660135e-05 2.55076639320271e-05 0.999987246168034 72 8.16930707626375e-06 1.63386141525275e-05 0.999991830692924 73 8.88677228794262e-06 1.77735445758852e-05 0.999991113227712 74 6.41404627127584e-06 1.28280925425517e-05 0.999993585953729 75 5.70305796957726e-06 1.14061159391545e-05 0.99999429694203 76 7.93710856729225e-06 1.58742171345845e-05 0.999992062891433 77 3.73708379731194e-05 7.47416759462387e-05 0.999962629162027 78 3.04993427967307e-05 6.09986855934615e-05 0.999969500657203 79 1.63366997018461e-05 3.26733994036922e-05 0.999983663300298 80 1.0081146194301e-05 2.0162292388602e-05 0.999989918853806 81 1.43336764199264e-05 2.86673528398528e-05 0.99998566632358 82 1.87256085065181e-05 3.74512170130363e-05 0.999981274391494 83 1.42259309288872e-05 2.84518618577743e-05 0.999985774069071 84 1.76769322474065e-05 3.5353864494813e-05 0.999982323067753 85 0.00117580586602677 0.00235161173205353 0.998824194133973 86 0.00222010606793827 0.00444021213587653 0.997779893932062 87 0.00184779376905836 0.00369558753811672 0.998152206230942 88 0.00491746228319545 0.0098349245663909 0.995082537716805 89 0.0462276632692842 0.0924553265385685 0.953772336730716 90 0.0892309530682657 0.178461906136531 0.910769046931734 91 0.189282494885806 0.378564989771612 0.810717505114194 92 0.849180561202015 0.30163887759597 0.150819438797985 93 0.95481792192712 0.0903641561457604 0.0451820780728802 94 0.952099144020024 0.0958017119599514 0.0479008559799757 95 0.931897141329354 0.136205717341292 0.068102858670646 96 0.921192433675705 0.157615132648589 0.0788075663242945 97 0.887371395891733 0.225257208216534 0.112628604108267 98 0.83870812198709 0.322583756025821 0.16129187801291 99 0.794762466821001 0.410475066357998 0.205237533178999 100 0.726834585130509 0.546330829738982 0.273165414869491 101 0.743148844091728 0.513702311816544 0.256851155908272 102 0.699784187370617 0.600431625258765 0.300215812629383 103 0.645273929406105 0.70945214118779 0.354726070593895 104 0.533479714051405 0.93304057189719 0.466520285948595 105 0.449479729362279 0.898959458724559 0.550520270637721 106 0.351892083673561 0.703784167347121 0.64810791632644 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 78 0.8125 NOK 5% type I error level 78 0.8125 NOK 10% type I error level 81 0.84375 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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