*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, 21 Dec 2010 18:27:37 +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/21/t1292956062rcjbfn2q6afp8r3.htm/, Retrieved Tue, 21 Dec 2010 19:27:53 +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/21/t1292956062rcjbfn2q6afp8r3.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 «

1 15 10 77 5 4 15 11 12 13 6 0 12 20 63 6 4 9 12 7 11 4 0 15 16 73 4 10 12 12 13 14 6 0 12 10 76 6 6 15 11 11 12 5 0 14 8 90 3 5 17 11 16 12 5 0 8 14 67 10 8 14 10 10 6 4 1 11 19 69 8 9 9 11 15 10 5 1 15 15 70 3 6 12 9 5 11 3 0 4 23 54 4 8 11 10 4 10 2 0 13 9 54 3 11 13 12 7 12 5 1 19 12 76 5 6 16 12 15 15 6 1 10 14 75 5 8 16 12 5 13 6 1 15 13 76 6 11 15 13 16 18 8 0 6 11 80 5 5 10 9 15 11 6 1 7 11 89 3 10 16 12 13 12 3 0 14 10 73 4 7 12 12 13 13 6 0 16 12 74 8 7 15 12 15 14 6 1 16 18 78 8 13 13 12 15 16 7 1 14 12 76 8 10 18 13 10 16 8 0 15 10 69 5 8 13 11 17 16 6 1 14 15 74 8 6 17 12 14 15 7 1 12 15 82 2 8 14 12 9 13 4 0 9 12 77 0 7 13 15 6 8 4 1 12 9 84 5 5 13 11 11 14 2 1 14 11 75 2 9 15 12 13 15 6 1 12 15 54 7 9 13 10 12 13 6 1 14 16 79 5 11 15 11 10 16 6 1 10 17 79 2 11 13 13 4 13 6 1 14 12 69 12 11 14 6 13 12 6 1 16 11 88 7 9 13 12 15 15 7 1 10 13 57 0 7 16 12 8 11 4 1 8 9 69 2 6 14 10 10 14 3 1 12 11 86 3 6 18 12 8 13 5 1 11 9 65 0 6 15 12 7 13 6 0 8 20 66 9 5 9 11 9 1 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 14 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation WeightedPopularity[t] = + 0.972956430682553 -0.720578550873085Gender[t] + 0.193977224888875Popularity[t] + 0.129569064646986Depression[t] + 0.0385516722914935Belonging[t] -0.0995732349156117ParentalCriticism[t] -0.181108909165785Happiness[t] -0.0293697936894506FindingFriends[t] + 0.223465400630920KnowingPeople[t] -0.124692442058178Liked[t] + 0.0928162102631865Celebrity[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) 0.972956430682553 3.627235 0.2682 0.788899 0.394449 Gender -0.720578550873085 0.511864 -1.4078 0.161344 0.080672 Popularity 0.193977224888875 0.118307 1.6396 0.103255 0.051628 Depression 0.129569064646986 0.092145 1.4061 0.161823 0.080912 Belonging 0.0385516722914935 0.024429 1.5781 0.116724 0.058362 ParentalCriticism -0.0995732349156117 0.092511 -1.0763 0.283561 0.14178 Happiness -0.181108909165785 0.123565 -1.4657 0.144896 0.072448 FindingFriends -0.0293697936894506 0.134956 -0.2176 0.828028 0.414014 KnowingPeople 0.223465400630920 0.09323 2.3969 0.017807 0.008903 Liked -0.124692442058178 0.138526 -0.9001 0.36954 0.18477 Celebrity 0.0928162102631865 0.232295 0.3996 0.690067 0.345034 Multiple Linear Regression - Regression Statistics Multiple R 0.446942917764097 R-squared 0.199757971739485 Adjusted R-squared 0.144568866342208 F-TEST (value) 3.61951820565913 F-TEST (DF numerator) 10 F-TEST (DF denominator) 145 p-value 0.000253968822219841 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.87910786879275 Sum Squared Residuals 1201.94300742093 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 5 5.60569168071815 -0.60569168071815 2 6 6.50401491305417 -0.504014913054166 3 4 6.96476841319411 -2.96476841319411 4 6 5.31505124596593 0.684948754034067 5 3 6.83827339826926 -3.83827339826926 6 10 5.1536588288539 4.8463411711461 7 8 7.22793558287429 0.772064417125709 8 3 4.79327359186798 -1.79327359186798 9 4 3.56083190628461 0.439168093715387 10 3 3.47244286333534 -0.472442863335341 11 5 6.61357318236603 -1.61357318236603 12 5 2.90394902334457 2.09605097665543 13 6 5.65612678333855 0.343873216661451 14 5 6.61019127341412 -1.61019127341412 15 3 3.90785411330265 -0.907854113302652 16 4 6.41678894722834 -2.41678894722834 17 8 6.88134483029786 1.11865516970214 18 8 6.70059709145739 1.29940290854261 19 8 3.79741948155179 4.20258051844821 20 5 6.72503140890843 -1.72503140890843 21 8 5.64353280774611 2.35646719225389 22 2 4.76178124413864 -2.76178124413864 23 0 4.46460133635785 -4.46460133635785 24 5 4.70727454711907 0.292725452880929 25 2 4.91102472414029 -2.91102472414029 26 7 4.67863830402502 2.32136169597498 27 5 4.74821141644848 0.251788583551520 28 2 3.43863473488165 -1.43863473488165 29 12 5.34154228268411 6.65845771731589 30 7 6.70211574356405 0.29788425643595 31 0 2.91417175784906 -2.91417175784906 32 2 2.97112902243149 -0.971129022431488 33 3 3.74177331751702 -0.74177331751702 34 0 2.89175038234243 -2.89175038234243 35 9 6.09579594835064 2.90420405164936 36 2 4.9923582272319 -2.99235822723190 37 3 2.80544932861253 0.194550671387470 38 1 3.76047815793811 -2.76047815793811 39 10 7.8225489424439 2.17745105755609 40 1 5.65426925740579 -4.65426925740579 41 4 4.7123042702586 -0.712304270258599 42 6 5.72087318170463 0.279126818295373 43 6 3.6044950285277 2.3955049714723 44 4 5.72735431099095 -1.72735431099095 45 4 4.95981358235764 -0.959813582357637 46 7 7.20276610299967 -0.202766102999666 47 7 5.68747740502759 1.31252259497241 48 7 6.35430491466374 0.645695085336258 49 0 5.18346853403717 -5.18346853403717 50 3 3.72826519530633 -0.728265195306329 51 8 4.84146877976996 3.15853122023004 52 8 6.32681761792297 1.67318238207703 53 10 4.73620633066368 5.26379366933632 54 11 6.50564090640692 4.49435909359308 55 6 5.14403211535227 0.855967884647727 56 2 4.59824736528953 -2.59824736528953 57 6 4.18344905448488 1.81655094551512 58 1 2.50884492740707 -1.50884492740707 59 5 4.48863355182368 0.511366448176319 60 4 5.23293283176608 -1.23293283176608 61 6 6.63841519108404 -0.638415191084038 62 6 8.09990505255761 -2.09990505255761 63 4 4.07585357749919 -0.0758535774991856 64 1 5.76391419661453 -4.76391419661453 65 6 2.87100781663647 3.12899218336353 66 7 6.83625227390868 0.163747726091319 67 7 7.35602215914786 -0.356022159147858 68 2 2.43169035853933 -0.431690358539328 69 7 7.25178570672601 -0.251785706726013 70 8 4.87592003840371 3.12407996159629 71 5 4.17160162607918 0.828398373920822 72 4 5.45789159535606 -1.45789159535606 73 2 6.5542380518797 -4.5542380518797 74 0 5.05339966072483 -5.05339966072483 75 7 4.72083467775146 2.27916532224854 76 0 6.5561028322966 -6.5561028322966 77 5 4.59682002292374 0.403179977076260 78 3 3.44709242924457 -0.447092429244573 79 3 2.33239643974695 0.667603560253050 80 3 2.50789492298660 0.492105077013402 81 3 6.71118567073936 -3.71118567073936 82 7 6.41135874017491 0.588641259825093 83 6 4.18396301920306 1.81603698079694 84 3 3.58905842484495 -0.589058424844949 85 0 4.00396157842198 -4.00396157842198 86 2 2.35380198307419 -0.35380198307419 87 0 5.15401141369296 -5.15401141369296 88 9 5.62789309989082 3.37210690010918 89 10 6.71350589599128 3.28649410400872 90 3 5.63975618049751 -2.63975618049751 91 7 4.9967444572467 2.00325554275330 92 3 4.08972412695808 -1.08972412695808 93 6 4.70598691957232 1.29401308042768 94 5 3.41125675803949 1.58874324196051 95 0 5.81838696908335 -5.81838696908335 96 0 3.20547359304074 -3.20547359304074 97 4 4.53242427847292 -0.532424278472920 98 0 3.78588577684123 -3.78588577684123 99 0 6.18935552005173 -6.18935552005173 100 7 6.67250050556968 0.32749949443032 101 3 4.10953106859248 -1.10953106859248 102 9 5.67352373810319 3.32647626189681 103 4 5.11404696764155 -1.11404696764155 104 4 5.80599429078765 -1.80599429078765 105 15 7.13138774127753 7.86861225872247 106 7 4.7238209330338 2.27617906696620 107 8 3.29236281271384 4.70763718728616 108 2 3.99260333741476 -1.99260333741476 109 8 4.92312495386332 3.07687504613668 110 7 5.89451514205713 1.10548485794287 111 3 6.42868385522656 -3.42868385522656 112 3 3.21194678083465 -0.211946780834653 113 6 6.10001901285009 -0.100019012850093 114 8 6.38028654845744 1.61971345154255 115 5 4.87018948618228 0.129810513817721 116 6 5.47847510107236 0.521524898927636 117 10 6.44030944324412 3.55969055675588 118 0 5.18691357904874 -5.18691357904874 119 5 3.56480185298814 1.43519814701186 120 0 3.46814869255672 -3.46814869255672 121 0 1.58937724809078 -1.58937724809078 122 5 6.86703652146192 -1.86703652146192 123 10 7.18111477362052 2.81888522637948 124 0 3.38905146920374 -3.38905146920374 125 5 3.18186568562194 1.81813431437806 126 6 5.9204447379578 0.079555262042203 127 1 4.24289648778702 -3.24289648778702 128 5 4.97104008609245 0.0289599139075477 129 3 3.4180994435836 -0.418099443583603 130 3 5.06092007228957 -2.06092007228957 131 6 4.01750046253311 1.98249953746689 132 2 4.00933740177566 -2.00933740177566 133 5 5.50491603601132 -0.504916036011322 134 6 3.93486271146558 2.06513728853442 135 2 0.976338740808302 1.02366125919170 136 3 5.99919851835793 -2.99919851835793 137 7 6.13256497331756 0.86743502668244 138 6 5.67719586571114 0.322804134288856 139 3 1.68547072758373 1.31452927241627 140 6 6.11961119552671 -0.11961119552671 141 9 3.99931146454370 5.0006885354563 142 2 5.72722004402486 -3.72722004402486 143 5 5.29882986863366 -0.298829868633656 144 10 5.72619915670747 4.27380084329253 145 9 4.82856543924774 4.17143456075226 146 8 5.9747105625861 2.02528943741390 147 8 5.56919386447429 2.43080613552571 148 5 5.76005507058476 -0.76005507058476 149 9 6.80953058716144 2.19046941283856 150 9 5.39096655614733 3.60903344385267 151 14 6.4888250712299 7.5111749287701 152 5 5.47679018683418 -0.476790186834178 153 12 4.58147503775713 7.41852496224287 154 6 3.58283432887199 2.41716567112801 155 6 4.35731132749077 1.64268867250923 156 8 5.22008334582687 2.77991665417313 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 14 0.205203972148304 0.410407944296608 0.794796027851696 15 0.111160507070983 0.222321014141965 0.888839492929017 16 0.0496178398652025 0.099235679730405 0.950382160134797 17 0.0681290262286085 0.136258052457217 0.931870973771391 18 0.0474972549918605 0.094994509983721 0.95250274500814 19 0.0267760417852838 0.0535520835705675 0.973223958214716 20 0.0177724306268490 0.0355448612536979 0.98222756937315 21 0.0091215102960704 0.0182430205921408 0.99087848970393 22 0.00496591544000413 0.00993183088000826 0.995034084559996 23 0.00625023501476083 0.0125004700295217 0.993749764985239 24 0.06828283019677 0.13656566039354 0.93171716980323 25 0.0923720573824035 0.184744114764807 0.907627942617596 26 0.0695769284925674 0.139153856985135 0.930423071507433 27 0.0456037465603508 0.0912074931207017 0.95439625343965 28 0.0303592006987808 0.0607184013975616 0.96964079930122 29 0.0343130280020628 0.0686260560041256 0.965686971997937 30 0.0305894255297157 0.0611788510594314 0.969410574470284 31 0.0593468979216523 0.118693795843305 0.940653102078348 32 0.040885953919839 0.081771907839678 0.959114046080161 33 0.0278222557508719 0.0556445115017437 0.972177744249128 34 0.0264015810552280 0.0528031621104561 0.973598418944772 35 0.0408179676599031 0.0816359353198061 0.959182032340097 36 0.0572439647558879 0.114487929511776 0.942756035244112 37 0.0405847512125233 0.0811695024250466 0.959415248787477 38 0.031372868138605 0.06274573627721 0.968627131861395 39 0.0452633001386075 0.090526600277215 0.954736699861392 40 0.0834287210750448 0.166857442150090 0.916571278924955 41 0.0624363885805147 0.124872777161029 0.937563611419485 42 0.0486331417236651 0.0972662834473301 0.951366858276335 43 0.0733019584166359 0.146603916833272 0.926698041583364 44 0.0615464512580027 0.123092902516005 0.938453548741997 45 0.0555110002037823 0.111022000407565 0.944488999796218 46 0.0415856381254606 0.0831712762509213 0.95841436187454 47 0.0403024651652465 0.080604930330493 0.959697534834753 48 0.0317120279810846 0.0634240559621693 0.968287972018915 49 0.0684882027343239 0.136976405468648 0.931511797265676 50 0.0524055745105189 0.104811149021038 0.947594425489481 51 0.0662646779503417 0.132529355900683 0.933735322049658 52 0.0577677192570065 0.115535438514013 0.942232280742993 53 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0.0225360527053503 0.0450721054107007 0.97746394729465 71 0.0175258408415884 0.0350516816831767 0.982474159158412 72 0.0156957565654994 0.0313915131309987 0.9843042434345 73 0.0369858439231928 0.0739716878463855 0.963014156076807 74 0.055383225847039 0.110766451694078 0.94461677415296 75 0.0507059224430959 0.101411844886192 0.949294077556904 76 0.155035800289562 0.310071600579125 0.844964199710438 77 0.128853786113168 0.257707572226337 0.871146213886832 78 0.106560699803479 0.213121399606959 0.89343930019652 79 0.0876871929429309 0.175374385885862 0.91231280705707 80 0.0733612527792818 0.146722505558564 0.926638747220718 81 0.0816748229581679 0.163349645916336 0.918325177041832 82 0.0661234930369951 0.132246986073990 0.933876506963005 83 0.0583663775720521 0.116732755144104 0.941633622427948 84 0.0458001657198821 0.0916003314397642 0.954199834280118 85 0.0587549483718055 0.117509896743611 0.941245051628194 86 0.0492502630668099 0.0985005261336197 0.95074973693319 87 0.0983777970154517 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0.764157266064019 105 0.495699748072097 0.991399496144194 0.504300251927903 106 0.473769127969048 0.947538255938096 0.526230872030952 107 0.566092739414362 0.867814521171277 0.433907260585638 108 0.551925129306775 0.89614974138645 0.448074870693225 109 0.540924039843099 0.918151920313801 0.459075960156900 110 0.507709290645521 0.984581418708958 0.492290709354479 111 0.555076457146806 0.889847085706387 0.444923542853194 112 0.498902757406635 0.99780551481327 0.501097242593365 113 0.441773668187862 0.883547336375725 0.558226331812138 114 0.393999550025688 0.787999100051376 0.606000449974312 115 0.34591823469377 0.69183646938754 0.65408176530623 116 0.295087117547408 0.590174235094817 0.704912882452592 117 0.273605139912032 0.547210279824063 0.726394860087968 118 0.44421928049599 0.88843856099198 0.55578071950401 119 0.388082350250029 0.776164700500057 0.611917649749971 120 0.605402591925288 0.789194816149424 0.394597408074712 121 0.620994269878995 0.758011460242009 0.379005730121005 122 0.566500659687523 0.866998680624954 0.433499340312477 123 0.556981069148364 0.886037861703272 0.443018930851636 124 0.611775237719386 0.776449524561229 0.388224762280614 125 0.564226289536975 0.87154742092605 0.435773710463025 126 0.494809012955687 0.989618025911374 0.505190987044313 127 0.471833084668395 0.94366616933679 0.528166915331605 128 0.403236501061141 0.806473002122283 0.596763498938859 129 0.417460531899471 0.834921063798941 0.58253946810053 130 0.481345222172774 0.962690444345548 0.518654777827226 131 0.409592167697223 0.819184335394445 0.590407832302777 132 0.424982536950546 0.849965073901092 0.575017463049454 133 0.364786626513801 0.729573253027601 0.635213373486199 134 0.293582441366393 0.587164882732787 0.706417558633607 135 0.227875956084402 0.455751912168803 0.772124043915599 136 0.181769175383961 0.363538350767922 0.818230824616039 137 0.200197806217822 0.400395612435643 0.799802193782178 138 0.147085402106348 0.294170804212697 0.852914597893652 139 0.151538338720174 0.303076677440347 0.848461661279826 140 0.0962810551724454 0.192562110344891 0.903718944827555 141 0.686935484875172 0.626129030249656 0.313064515124828 142 0.556698366069108 0.886603267861783 0.443301633930892 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.00775193798449612 OK 5% type I error level 9 0.0697674418604651 NOK 10% type I error level 36 0.279069767441860 NOK

Charts produced by software:

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Parameters (Session):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 5 ; 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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Software written by Ed van Stee & Patrick Wessa

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