Paper: Multiple regression uitgebreide variabelen

*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, 15 Dec 2010 15:12:47 +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/15/t1292425923t6vq9kqejuu1kf4.htm/, Retrieved Wed, 15 Dec 2010 16:12:06 +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/15/t1292425923t6vq9kqejuu1kf4.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 «

13 13 14 13 3 1 13 6 4 6 4 6 12 12 8 13 5 1 18 6 2 7 2 6 15 10 12 16 6 0 13 5 4 4 4 6 12 9 7 12 6 1 17 4 2 6 5 4 10 10 10 11 5 0 13 4 2 6 5 4 9 12 11 11 4 1 13 3 3 3 2 6 12 12 14 14 4 1 13 6 4 6 4 6 11 6 6 9 4 1 18 3 2 5 5 5 11 5 16 14 6 1 13 6 2 2 5 6 11 12 11 12 6 1 13 6 2 6 3 6 15 11 16 11 5 1 13 4 2 6 5 4 7 14 12 12 4 0 13 5 5 5 4 5 11 14 7 13 6 1 14 4 4 6 3 5 11 12 13 11 4 1 13 6 5 5 2 6 10 12 11 12 6 1 17 6 2 6 2 6 14 11 15 16 6 1 14 6 3 4 5 5 10 11 7 9 4 1 12 4 6 5 2 4 6 7 9 11 4 1 13 5 6 3 3 6 11 9 7 13 2 1 17 2 6 3 3 3 15 11 14 15 7 1 13 6 2 6 2 6 14 12 15 13 6 1 13 6 2 6 2 6 9 11 15 15 7 1 13 5 7 5 3 5 13 8 14 14 5 1 14 4 6 5 2 4 16 12 8 14 4 0 13 6 4 6 4 6 13 10 8 8 4 0 12 4 6 5 2 4 12 10 14 13 7 0 16 6 5 4 6 5 14 12 14 15 7 1 14 6 2 7 2 6 11 8 8 13 4 1 17 6 6 7 5 7 9 12 11 11 4 0 13 6 4 6 4 6 16 11 16 15 6 1 14 6 2 6 2 6 12 12 10 15 6 1 16 6 2 6 2 6 10 7 8 9 5 1 14 6 6 7 2 6 13 11 14 13 6 0 13 1 7 2 5 1 16 11 16 16 7 1 11 6 4 6 4 6 5 15 8 12 3 1 13 6 2 6 2 5 8 11 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 Popularity[t] = + 1.42973727530118 + 0.0679559785674146FindingFriends[t] + 0.257687676023935KnowingPeople[t] + 0.366618607059788Liked[t] + 0.675903357645973Celebrity[t] -0.0596577206056682Geslacht[t] -0.128099410571831Happiness[t] -0.647557306757711UsingHands[t] -0.196214101838047Quiet[t] + 0.454124178125426EyeContact[t] + 0.198962264726567CrossArms[t] + 0.194097726936484SmilingTalking[t] + e[t] Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STAT H0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 1.42973727530118 2.880068 0.4964 0.620613 0.310307 FindingFriends 0.0679559785674146 0.11467 0.5926 0.554686 0.277343 KnowingPeople 0.257687676023935 0.080316 3.2084 0.001761 0.000881 Liked 0.366618607059788 0.113976 3.2166 0.001716 0.000858 Celebrity 0.675903357645973 0.181303 3.728 0.00031 0.000155 Geslacht -0.0596577206056682 0.50957 -0.1171 0.90702 0.45351 Happiness -0.128099410571831 0.13393 -0.9565 0.340994 0.170497 UsingHands -0.647557306757711 0.34204 -1.8932 0.06103 0.030515 Quiet -0.196214101838047 0.136818 -1.4341 0.154454 0.077227 EyeContact 0.454124178125426 0.209967 2.1628 0.03278 0.01639 CrossArms 0.198962264726567 0.166568 1.1945 0.23493 0.117465 SmilingTalking 0.194097726936484 0.338096 0.5741 0.567112 0.283556 Multiple Linear Regression - Regression Statistics Multiple R 0.727346569060866 R-squared 0.529033031524613 Adjusted R-squared 0.480615866541162 F-TEST (value) 10.9265594486054 F-TEST (DF numerator) 11 F-TEST (DF denominator) 107 p-value 2.81441536742477e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.05898166248144 Sum Squared Residuals 453.616387048529 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 13 11.0045746090676 1.99542539093237 2 12 10.5504300891378 1.44956991086223 3 15 13.2118638865473 1.78813611345268 4 12 11.5759404142663 0.424059585733664 5 10 11.9464928190928 -1.94649281909279 6 9 10.4848107042366 -1.48481070423656 7 12 11.979140595206 0.020859404794037 8 11 8.92215371106578 2.07784628893422 9 11 12.1455245684748 -1.14552456847483 10 11 12.0181130072561 -1.01811300725606 11 15 13.5009171331981 1.49908286680186 12 7 10.7292190066368 -3.72921900663677 13 11 12.0703821396859 -1.07038213968595 14 11 9.57333428858606 1.42666571141394 15 10 11.3067531002422 -1.30675310024216 16 14 13.4186470948795 0.581352905120548 17 10 8.0648195081317 1.9351804918683 18 6 7.94486080504862 -1.94486080504862 19 11 7.79480900613957 3.20519099386043 20 15 14.3000169708592 0.699983029140782 21 14 13.216520053685 0.783479946314983 22 9 13.7749318041153 -4.77493180411528 23 13 11.9175628763982 1.08243712360175 24 16 10.492672259668 5.50732774033198 25 13 7.9475903191341 5.0524096808659 26 12 12.2790439372672 -0.279043937267188 27 14 14.6939977169802 -0.693997716980227 28 11 9.73693034155797 1.26306965844204 29 9 10.1658794665605 -1.16587946656046 30 16 14.0113895546893 1.98861044531072 31 12 12.2770206559694 -0.277020655969426 32 10 8.47171700299668 1.52828299700332 33 13 13.0171515912932 -0.0171515912932296 34 16 15.4437060768876 0.556293923112422 35 5 9.02814785028552 -4.02814785028552 36 8 10.4823883456822 -2.48238834568223 37 11 12.4819376897124 -1.48193768971243 38 16 14.5832616354049 1.41673836459509 39 17 12.8989370451371 4.10106295486291 40 9 7.86030189276624 1.13969810723376 41 9 11.9557965105435 -2.95579651054349 42 13 14.244930811429 -1.24493081142901 43 6 10.0061701161472 -4.00617011614722 44 12 12.1396686008041 -0.139668600804067 45 8 11.6243632806534 -3.62436328065335 46 14 12.1610995318298 1.83890046817019 47 12 12.2204693672247 -0.220469367224733 48 11 10.5211637675592 0.478836232440766 49 16 14.43681308678 1.56318691322003 50 8 9.88974947853361 -1.88974947853361 51 15 14.9120426550078 0.0879573449922338 52 7 9.07804701073926 -2.07804701073926 53 16 13.9699298447681 2.03007015523187 54 14 14.0174721880548 -0.0174721880547607 55 9 10.2443822917313 -1.24438229173134 56 14 12.0307376697682 1.96926233023179 57 11 13.8863042732833 -2.88630427328328 58 15 11.9994713107915 3.00052868920854 59 15 12.3343001312729 2.66569986872715 60 13 12.0307376697682 0.969262330231787 61 11 11.8085286204357 -0.808528620435679 62 11 13.686838705269 -2.68683870526898 63 12 12.8274550809735 -0.827455080973473 64 12 14.1240710013144 -2.12407100131443 65 12 11.9842663915227 0.0157336084772647 66 12 12.2136697216109 -0.213669721610937 67 14 10.4330145390624 3.56698546093765 68 6 7.46048032476614 -1.46048032476614 69 7 9.50174736952451 -2.50174736952451 70 14 13.7385408700261 0.261459129973863 71 10 11.0073354277487 -1.00733542774869 72 13 8.18807805703087 4.81192194296913 73 12 12.7978933403962 -0.797893340396246 74 9 9.28824111182083 -0.288241111820833 75 16 14.8478209179334 1.15217908206664 76 10 9.8895090223993 0.110490977600702 77 16 14.5482935176886 1.45170648231139 78 15 13.3437920983512 1.65620790164884 79 8 7.71111049822817 0.288889501771825 80 11 12.7498386456421 -1.74983864564207 81 13 12.1912656753663 0.808734324633683 82 16 15.9744843059633 0.025515694036669 83 14 15.05650612315 -1.05650612315003 84 9 10.0276436452936 -1.02764364529357 85 8 9.95083348958265 -1.95083348958265 86 8 10.9682434246756 -2.96824342467563 87 11 11.8776732775281 -0.877673277528131 88 12 13.6802688662808 -1.68026886628082 89 14 13.6384353865025 0.361564613497518 90 15 14.2247125302156 0.77528746978441 91 16 13.7177472980429 2.28225270195712 92 16 14.8889146591487 1.11108534085131 93 11 12.5885162028708 -1.58851620287077 94 14 13.5886349865218 0.411365013478156 95 14 10.6570757246941 3.34292427530594 96 12 11.0916503103213 0.908349689678733 97 13 13.1369529315639 -0.136952931563935 98 12 10.78700848361 1.21299151639001 99 16 15.5298220039576 0.470177996042445 100 12 13.216520053685 -1.21652005368502 101 11 10.8328313009612 0.167168699038784 102 4 5.97962920214694 -1.97962920214694 103 16 15.4159912399094 0.584008760090567 104 10 10.5781720296904 -0.578172029690445 105 13 12.7290196556716 0.270980344328352 106 14 14.0350926946725 -0.0350926946724795 107 7 9.93496221807539 -2.93496221807539 108 12 12.5136356699401 -0.513635669940068 109 12 11.3527716450039 0.647228354996094 110 13 13.3254509847209 -0.325450984720871 111 15 12.322583251833 2.67741674816699 112 12 10.5762749582734 1.4237250417266 113 8 11.163079291537 -3.16307929153696 114 10 14.1591215292839 -4.15912152928388 115 16 14.5285009550247 1.47149904497526 116 13 13.0569007304688 -0.0569007304688109 117 9 9.49971446490676 -0.499714464906765 118 14 13.5010345744874 0.498965425512618 119 14 12.5242577920339 1.47574220796612 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 15 0.481783818270671 0.963567636541342 0.518216181729329 16 0.44835435472038 0.896708709440761 0.55164564527962 17 0.313146763297656 0.626293526595311 0.686853236702344 18 0.450228848731832 0.900457697463664 0.549771151268168 19 0.407097000790626 0.814194001581253 0.592902999209374 20 0.352044575917678 0.704089151835356 0.647955424082322 21 0.256988797969983 0.513977595939966 0.743011202030017 22 0.460633357734787 0.921266715469574 0.539366642265213 23 0.376060355161937 0.752120710323874 0.623939644838063 24 0.568825918300519 0.862348163398963 0.431174081699481 25 0.835642569131507 0.328714861736986 0.164357430868493 26 0.884696971346113 0.230606057307774 0.115303028653887 27 0.845598297073765 0.308803405852471 0.154401702926235 28 0.80713363162629 0.385732736747418 0.192866368373709 29 0.821140365460781 0.357719269078438 0.178859634539219 30 0.79535723749563 0.40928552500874 0.20464276250437 31 0.759632819257856 0.480734361484288 0.240367180742144 32 0.730237340045308 0.539525319909384 0.269762659954692 33 0.693236351959075 0.61352729608185 0.306763648040925 34 0.664194232959259 0.671611534081482 0.335805767040741 35 0.88589391497257 0.22821217005486 0.11410608502743 36 0.889950424538261 0.220099150923477 0.110049575461739 37 0.862841334721906 0.274317330556189 0.137158665278094 38 0.843782129976691 0.312435740046618 0.156217870023309 39 0.881364014375628 0.237271971248744 0.118635985624372 40 0.910993968000905 0.178012063998189 0.0890060319990945 41 0.938929675515828 0.122140648968343 0.0610703244841716 42 0.934669660712187 0.130660678575626 0.0653303392878131 43 0.960486070403427 0.079027859193145 0.0395139295965725 44 0.94629020416475 0.107419591670499 0.0537097958352495 45 0.980167223723532 0.0396655525529351 0.0198327762764676 46 0.977803128723493 0.0443937425530143 0.0221968712765072 47 0.968812757442212 0.0623744851155753 0.0311872425577877 48 0.958881216625886 0.0822375667482278 0.0411187833741139 49 0.958022442654251 0.0839551146914977 0.0419775573457488 50 0.959866585810892 0.0802668283782162 0.0401334141891081 51 0.94560146162808 0.108797076743839 0.0543985383719195 52 0.939702031480633 0.120595937038734 0.060297968519367 53 0.94952691902854 0.100946161942921 0.0504730809714605 54 0.933867762244741 0.132264475510517 0.0661322377552587 55 0.922593083613156 0.154813832773689 0.0774069163868445 56 0.921048441019065 0.157903117961869 0.0789515589809345 57 0.94251100437934 0.114977991241318 0.0574889956206589 58 0.96578871039541 0.0684225792091804 0.0342112896045902 59 0.982302530400547 0.0353949391989061 0.017697469599453 60 0.976668419260734 0.0466631614785329 0.0233315807392664 61 0.969194629386445 0.0616107412271102 0.0308053706135551 62 0.974807155293025 0.0503856894139504 0.0251928447069752 63 0.968499352803823 0.0630012943923542 0.0315006471961771 64 0.970657081161648 0.0586858376767051 0.0293429188383525 65 0.95970373121606 0.0805925375678825 0.0402962687839413 66 0.946006332267104 0.107987335465791 0.0539936677328956 67 0.974245723119482 0.0515085537610367 0.0257542768805184 68 0.969043372009827 0.0619132559803458 0.0309566279901729 69 0.967386159560418 0.0652276808791633 0.0326138404395817 70 0.954878367114564 0.0902432657708728 0.0451216328854364 71 0.943737768000955 0.11252446399809 0.0562622319990448 72 0.991079437855965 0.0178411242880696 0.00892056214403479 73 0.987735012752799 0.0245299744944026 0.0122649872472013 74 0.981862568492406 0.0362748630151886 0.0181374315075943 75 0.975338052676835 0.0493238946463303 0.0246619473231652 76 0.965951839112072 0.0680963217758564 0.0340481608879282 77 0.963137970599201 0.0737240588015969 0.0368620294007985 78 0.959673071516648 0.080653856966704 0.040326928483352 79 0.943520218866027 0.112959562267946 0.0564797811339731 80 0.93220389709133 0.135592205817338 0.0677961029086692 81 0.915827842088573 0.168344315822853 0.0841721579114266 82 0.91759215468635 0.164815690627301 0.0824078453136506 83 0.88945875561902 0.22108248876196 0.11054124438098 84 0.855742490728831 0.288515018542337 0.144257509271169 85 0.840308374949021 0.319383250101958 0.159691625050979 86 0.937231039507212 0.125537920985577 0.0627689604927883 87 0.91304279936502 0.173914401269961 0.0869572006349803 88 0.943405026258143 0.113189947483714 0.0565949737418568 89 0.919921141328626 0.160157717342748 0.0800788586713739 90 0.891637630813683 0.216724738372633 0.108362369186317 91 0.888882785870538 0.222234428258923 0.111117214129462 92 0.8456790404511 0.3086419190978 0.1543209595489 93 0.795626428445281 0.408747143109438 0.204373571554719 94 0.734938187644109 0.530123624711782 0.265061812355891 95 0.808919820929389 0.382160358141223 0.191080179070611 96 0.756391959323186 0.487216081353628 0.243608040676814 97 0.673170554835699 0.653658890328602 0.326829445164301 98 0.670564909814913 0.658870180370175 0.329435090185087 99 0.714818659080686 0.570362681838628 0.285181340919314 100 0.674145603434191 0.651708793131618 0.325854396565809 101 0.667417534956349 0.665164930087301 0.332582465043651 102 0.544142881444977 0.911714237110046 0.455857118555023 103 0.433223801334356 0.866447602668712 0.566776198665644 104 0.28201743395629 0.56403486791258 0.71798256604371 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 8 0.0888888888888889 NOK 10% type I error level 26 0.288888888888889 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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

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

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