Multiple Linear Regression - Celebrity 2

*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 15:31:03 +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/t1292340549ba7r64phytrtzzk.htm/, Retrieved Tue, 14 Dec 2010 16:29:20 +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/t1292340549ba7r64phytrtzzk.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 «

6 2 1 73 62 66 4 1 1 58 54 54 5 1 1 68 41 82 4 1 1 62 49 61 4 1 1 65 49 65 6 1 1 81 72 77 6 1 1 73 78 66 4 2 1 64 58 66 4 1 1 68 58 66 6 1 1 51 23 48 4 1 1 68 39 57 6 1 1 61 63 80 5 1 1 69 46 60 4 1 1 73 58 70 6 2 1 61 39 85 3 2 1 62 44 59 5 1 1 63 49 72 6 1 1 69 57 70 4 2 1 47 76 74 6 2 1 66 63 70 2 1 1 58 18 51 7 2 1 63 40 70 5 1 1 69 59 71 2 2 1 59 62 72 4 1 1 59 70 50 4 2 1 63 65 69 6 2 1 65 56 73 6 1 1 65 45 66 5 2 1 71 57 73 6 1 1 60 50 58 6 2 1 81 40 78 4 1 1 67 58 83 6 2 1 66 49 76 6 1 1 62 49 77 6 1 1 63 27 79 2 2 1 73 51 71 4 2 1 55 75 79 5 1 1 59 65 60 3 1 1 64 47 73 7 2 1 63 49 70 5 1 1 64 65 42 3 1 1 73 61 74 8 1 1 54 46 68 8 1 1 76 69 83 5 2 1 74 55 62 6 2 1 63 78 79 3 2 1 73 58 61 5 2 1 67 34 86 4 2 1 68 67 64 5 1 1 66 45 75 5 2 1 62 68 59 6 2 1 71 49 82 5 1 1 63 19 61 6 1 1 75 72 69 6 1 1 77 59 60 4 2 1 62 46 59 8 1 1 74 56 81 6 2 1 67 45 65 4 2 1 56 53 60 6 2 1 60 67 60 5 2 1 58 73 45 5 1 1 65 46 75 6 2 1 49 70 84 6 1 1 61 38 77 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 9 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Celebrity[t] = + 1.64525178041472 -0.44459465358534Gender[t] + 1.67653317331531Age[t] -0.000771955540575894NV[t] + 0.00519221184566125ANX[t] + 0.032351106163234GR[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) 1.64525178041472 1.60223 1.0269 0.306261 0.153131 Gender -0.44459465358534 0.235481 -1.888 0.061091 0.030546 Age 1.67653317331531 1.002441 1.6725 0.096669 0.048334 NV -0.000771955540575894 0.01603 -0.0482 0.96166 0.48083 ANX 0.00519221184566125 0.009182 0.5655 0.572648 0.286324 GR 0.032351106163234 0.012284 2.6335 0.0094 0.0047 Multiple Linear Regression - Regression Statistics Multiple R 0.287648778695546 R-squared 0.082741819885039 Adjusted R-squared 0.0499825991666475 F-TEST (value) 2.52575665936359 F-TEST (DF numerator) 5 F-TEST (DF denominator) 140 p-value 0.0319025204519411 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.35922235913879 Sum Squared Residuals 258.647959021595 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 6 4.83333303330175 1.16666696669825 2 4 4.85975605127163 -0.859756051271635 3 5 5.69036871444282 -0.690368714442823 4 4 5.05716491302367 -1.05716491302367 5 4 5.18425347105487 -1.18425347105487 6 6 5.67953632881468 0.320463671185325 7 6 5.36100307641768 0.638996923582325 8 4 4.81951178578429 -0.819511785784295 9 4 5.26101861720733 -1.26101861720733 10 6 4.51009453586077 1.48990546413923 11 4 4.87120663667066 -0.87120663667066 12 6 5.74529885150494 0.254701148495056 13 5 5.00383348253941 -0.00383348253941515 14 4 5.38656326415739 -1.38656326415739 15 6 5.33784664443990 0.662153355560095 16 3 4.52190698788355 -1.52190698788355 17 5 5.41225512527866 -0.412255125278662 18 6 5.38445887447403 0.615541125525971 19 4 5.18490369250186 -1.18490369250186 20 6 4.97333335858439 1.02666664141561 21 2 4.57578310633813 -2.57578310633813 22 7 4.85622835275590 2.14377164724410 23 5 5.42719440432859 -0.427194404328585 24 2 5.03824704784922 -3.03824704784922 25 4 4.8126550606087 -0.812655060608705 26 4 4.9536825427342 -0.95368254273420 27 6 5.03481314969503 0.965186850304966 28 6 5.19583572983546 0.804164270164538 29 5 5.03537362829724 -0.0353736282972399 30 6 4.96684771746078 1.03315228253922 31 6 5.10114200233141 0.89885799766859 32 4 5.81175937752288 -1.81175937752288 33 6 5.09474902972453 0.905250970275469 34 6 5.57478261163541 0.425217388364591 35 6 5.52448420781675 0.475515792183247 36 2 4.93797423381565 -2.93797423381565 37 4 5.33529136714776 -1.33529136714776 38 5 5.11020506301274 -0.110205063012738 39 3 5.43344985221 -2.43344985221000 40 7 4.90295825936686 2.09704174063314 41 5 4.52402537437165 0.475974625628353 42 3 5.5315443243473 -2.53154432434731 43 8 5.27422166495393 2.72577833504607 44 8 5.86192610795997 2.13807389204003 45 5 4.66681117018862 0.333188829811384 46 6 5.34469235836014 0.655307641639862 47 3 4.65080865510294 -1.65080865510294 48 5 5.33960495813138 -0.339604958131377 49 4 4.79845165790647 -0.798451657906474 50 5 5.48622372976399 -0.486223729763992 51 5 4.64652007217942 0.353479927820579 52 6 5.28499588900106 0.715004110998944 53 5 4.90062660211325 0.0993733978867491 54 6 5.42535921275226 0.574640787247742 55 6 5.0651565922084 0.934843407791596 56 4 4.53229141157487 -0.532291411574873 57 8 5.73126905272106 2.26873094727894 58 6 4.71734605900574 1.28265394099426 59 4 4.60561973390119 -0.605619733901192 60 6 4.67522287757815 1.32477712242185 61 5 4.22265346728476 0.777346532715245 62 5 5.49218789715023 -0.492187897150229 63 6 5.47571757197908 0.524282428020919 64 6 5.51844023687371 0.48155976312629 65 6 4.73249681499403 1.26750318500597 66 6 4.36899196967755 1.63100803032245 67 6 4.95053992507519 1.04946007492481 68 6 4.88699182347407 1.11300817652594 69 7 4.79397660610468 2.20602339389532 70 4 5.12031214313619 -1.12031214313619 71 4 5.51034959741597 -1.51034959741597 72 3 4.78878439425902 -1.78878439425902 73 6 5.06301319766021 0.936986802339787 74 5 5.2871363578773 -0.287136357877297 75 5 5.37527228587197 -0.375272285871973 76 3 5.08135366175576 -2.08135366175576 77 5 5.19799491501553 -0.197994915015529 78 4 4.23719480051803 -0.237194800518033 79 3 5.03369511816257 -2.03369511816257 80 7 5.50299820039024 1.49700179960976 81 4 4.72120583670862 -0.721205836708616 82 4 5.18635786073823 -1.18635786073823 83 5 4.90176042427759 0.0982395757224112 84 6 5.32356164435373 0.67643835564627 85 2 5.23766470509593 -3.23766470509593 86 2 4.23242554254911 -2.23242554254911 87 6 5.86439865602336 0.135601343976637 88 4 4.62998208655164 -0.629982086551636 89 5 5.17635738605881 -0.176357386058815 90 6 4.67816031005506 1.32183968994494 91 7 5.44649914418688 1.55350085581312 92 8 5.11127122472044 2.88872877527956 93 6 4.86705265280066 1.13294734719934 94 6 4.9751854726374 1.0248145273626 95 3 4.94858413125008 -1.94858413125008 96 7 4.95198824196766 2.04801175803234 97 3 5.36776128173263 -2.36776128173263 98 6 4.86134076104476 1.13865923895524 99 4 4.78708087606426 -0.787080876064256 100 4 5.60740291590567 -1.60740291590567 101 6 5.30425690449543 0.695743095504568 102 6 5.3683986382197 0.631601361780305 103 6 6.11985382027483 -0.119853820274826 104 4 4.86284387343394 -0.86284387343394 105 7 6.8801461797252 0.119853820274797 106 5 5.35351708033843 -0.353517080338432 107 7 5.62606737360496 1.37393262639504 108 4 5.41702145757563 -1.41702145757563 109 6 5.02597263708486 0.974027362915136 110 6 5.81202857562991 0.187971424370086 111 6 4.62727934524605 1.37272065475395 112 5 4.39443155516849 0.605568444831515 113 5 5.30796000075665 -0.307960000756652 114 6 5.45823808440913 0.541761915590867 115 7 5.16143502948558 1.83856497051442 116 4 4.85431335762277 -0.854313357622772 117 4 5.38403299492534 -1.38403299492534 118 8 5.40530752541348 2.59469247458652 119 6 5.21029433383998 0.789705666160016 120 3 5.00516299101025 -2.00516299101025 121 4 5.22874438892895 -1.22874438892895 122 5 5.36150583385122 -0.361505833851221 123 5 5.0593648968957 -0.0593648968957037 124 6 5.08441940152992 0.915580598470081 125 8 5.36403610308327 2.63596389691673 126 2 4.84566853131909 -2.84566853131909 127 4 4.48055868766849 -0.480558687668486 128 7 5.4293756718968 1.57062432810320 129 5 5.21114316726541 -0.211143167265415 130 6 5.38695013884124 0.613049861158757 131 6 5.47794077008408 0.522059229915923 132 4 5.44383135022937 -1.44383135022937 133 5 4.72857931612249 0.27142068387751 134 6 5.06446151455268 0.935538485447317 135 6 5.91456538646045 0.085434613539547 136 6 5.36785687592132 0.632143124078684 137 6 5.14893221930616 0.851067780693838 138 5 5.4984212626435 -0.498421262643496 139 5 4.46050992600166 0.539490073998343 140 6 5.10229611305675 0.897703886943246 141 4 5.41507667500589 -1.41507667500589 142 6 5.21612390217271 0.783876097827294 143 3 5.79386980103612 -2.79386980103612 144 6 5.30551246075336 0.694487539246641 145 8 5.38557398033454 2.61442601966546 146 4 5.26615603355628 -1.26615603355628 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 9 0.186501488894151 0.373002977788302 0.813498511105849 10 0.353060448573535 0.706120897147071 0.646939551426465 11 0.501615643978539 0.996768712042922 0.498384356021461 12 0.456141778986611 0.912283557973222 0.543858221013389 13 0.341848329031239 0.683696658062478 0.658151670968761 14 0.302375186454222 0.604750372908445 0.697624813545778 15 0.227141246776649 0.454282493553298 0.772858753223351 16 0.271842195390626 0.543684390781251 0.728157804609374 17 0.197408339456888 0.394816678913776 0.802591660543112 18 0.168520835465849 0.337041670931698 0.831479164534151 19 0.137718007201575 0.275436014403149 0.862281992798425 20 0.134637111860867 0.269274223721735 0.865362888139133 21 0.207284520928601 0.414569041857203 0.792715479071399 22 0.309124104105386 0.618248208210772 0.690875895894614 23 0.245163638456262 0.490327276912523 0.754836361543738 24 0.504608367211959 0.990783265576082 0.495391632788041 25 0.44135383098551 0.88270766197102 0.55864616901449 26 0.391963069553757 0.783926139107514 0.608036930446243 27 0.362786373024775 0.725572746049551 0.637213626975225 28 0.344286196209513 0.688572392419026 0.655713803790487 29 0.286490464382277 0.572980928764554 0.713509535617723 30 0.311009539342007 0.622019078684014 0.688990460657993 31 0.261223590547796 0.522447181095592 0.738776409452204 32 0.283594596873235 0.56718919374647 0.716405403126765 33 0.251761111191965 0.50352222238393 0.748238888808035 34 0.221662691403237 0.443325382806474 0.778337308596763 35 0.183855957382227 0.367711914764454 0.816144042617773 36 0.398713669108455 0.79742733821691 0.601286330891545 37 0.369876421347216 0.739752842694431 0.630123578652784 38 0.325239787791429 0.650479575582857 0.674760212208571 39 0.419873608351952 0.839747216703905 0.580126391648048 40 0.511221042799263 0.977557914401473 0.488778957200737 41 0.478325066757657 0.956650133515314 0.521674933242343 42 0.579403940074847 0.841192119850305 0.420596059925153 43 0.757904789794127 0.484190420411747 0.242095210205873 44 0.827330318675126 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62 0.397452164887521 0.794904329775042 0.602547835112479 63 0.359014969275343 0.718029938550686 0.640985030724657 64 0.320323599579638 0.640647199159276 0.679676400420362 65 0.312657609086325 0.62531521817265 0.687342390913675 66 0.329003767657747 0.658007535315495 0.670996232342253 67 0.30978999763666 0.61957999527332 0.69021000236334 68 0.299518675352939 0.599037350705879 0.70048132464706 69 0.368599399064976 0.737198798129951 0.631400600935024 70 0.355700090612370 0.711400181224739 0.64429990938763 71 0.363382563212912 0.726765126425824 0.636617436787088 72 0.392839437585047 0.785678875170093 0.607160562414953 73 0.368858414111889 0.737716828223778 0.631141585888111 74 0.325320319223108 0.650640638446216 0.674679680776892 75 0.285549974550675 0.571099949101349 0.714450025449325 76 0.335610843074452 0.671221686148904 0.664389156925548 77 0.292885948500654 0.585771897001308 0.707114051499346 78 0.253654221909635 0.507308443819270 0.746345778090365 79 0.300051285685775 0.600102571371551 0.699948714314225 80 0.306299958797464 0.612599917594927 0.693700041202536 81 0.275704063127611 0.551408126255222 0.724295936872389 82 0.265048689731732 0.530097379463463 0.734951310268268 83 0.226278955541811 0.452557911083622 0.773721044458189 84 0.198677368438761 0.397354736877522 0.801322631561239 85 0.385558463206689 0.771116926413377 0.614441536793311 86 0.47467029804007 0.94934059608014 0.52532970195993 87 0.425748614010425 0.851497228020851 0.574251385989575 88 0.394254568562871 0.788509137125742 0.605745431437129 89 0.348410904908243 0.696821809816486 0.651589095091757 90 0.340351566405115 0.68070313281023 0.659648433594885 91 0.347653874970436 0.695307749940873 0.652346125029564 92 0.504765241349624 0.990469517300752 0.495234758650376 93 0.48779467705981 0.97558935411962 0.51220532294019 94 0.471641989562357 0.943283979124713 0.528358010437643 95 0.512096919974785 0.97580616005043 0.487903080025215 96 0.572226925128758 0.855546149742484 0.427773074871242 97 0.673595527645624 0.652808944708751 0.326404472354376 98 0.648611232442692 0.702777535114615 0.351388767557308 99 0.630102716388975 0.73979456722205 0.369897283611025 100 0.653567463275449 0.692865073449103 0.346432536724551 101 0.614230698242151 0.771538603515698 0.385769301757849 102 0.571329242256851 0.857341515486297 0.428670757743149 103 0.518673947526787 0.962652104946426 0.481326052473213 104 0.493979991547611 0.987959983095222 0.506020008452389 105 0.438725221399722 0.877450442799445 0.561274778600278 106 0.388667636440774 0.777335272881548 0.611332363559226 107 0.378281936257457 0.756563872514914 0.621718063742543 108 0.387416497050782 0.774832994101564 0.612583502949218 109 0.350061944568123 0.700123889136245 0.649938055431877 110 0.298279955598729 0.596559911197457 0.701720044401271 111 0.296176174802093 0.592352349604185 0.703823825197907 112 0.260418098541572 0.520836197083144 0.739581901458428 113 0.221386219446010 0.442772438892019 0.77861378055399 114 0.194998623889316 0.389997247778632 0.805001376110684 115 0.210201108468087 0.420402216936174 0.789798891531913 116 0.199554655492034 0.399109310984069 0.800445344507966 117 0.211676859241958 0.423353718483916 0.788323140758042 118 0.35655018382916 0.71310036765832 0.64344981617084 119 0.339354363338415 0.678708726676829 0.660645636661585 120 0.418488695994714 0.836977391989428 0.581511304005286 121 0.424074309816765 0.84814861963353 0.575925690183235 122 0.394312639291561 0.788625278583122 0.605687360708439 123 0.355850641779446 0.711701283558891 0.644149358220554 124 0.294513457842883 0.589026915685765 0.705486542157117 125 0.415290524180661 0.830581048361322 0.584709475819339 126 0.699235678261984 0.601528643476033 0.300764321738016 127 0.837331612905111 0.325336774189778 0.162668387094889 128 0.849360144305117 0.301279711389766 0.150639855694883 129 0.786739224244982 0.426521551510037 0.213260775755018 130 0.720999900558748 0.558000198882503 0.279000099441252 131 0.646669851796246 0.706660296407507 0.353330148203754 132 0.859442334686893 0.281115330626214 0.140557665313107 133 0.882987933853674 0.234024132292651 0.117012066146326 134 0.809226884881061 0.381546230237878 0.190773115118939 135 0.88647441730387 0.227051165392260 0.113525582696130 136 0.850055779190663 0.299888441618673 0.149944220809337 137 0.751303639178937 0.497392721642125 0.248696360821063 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 0 0 OK 10% type I error level 0 0 OK

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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Software written by Ed van Stee & Patrick Wessa

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