*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: Fri, 24 Dec 2010 15:56:42 +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/24/t12932060758qoxu9owcm6lem7.htm/, Retrieved Fri, 24 Dec 2010 16:54:35 +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/24/t12932060758qoxu9owcm6lem7.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 «

15 10 12 16 6 2 0 0 9 12 9 7 12 6 1 1 2 9 9 12 11 11 4 1 2 1 9 10 12 11 12 6 0 0 0 9 13 9 14 14 6 0 0 0 9 16 11 16 16 7 1 0 0 9 14 12 13 13 6 0 0 0 9 16 11 13 14 7 1 1 0 9 10 12 5 13 6 0 0 0 9 8 12 8 13 4 2 0 1 10 12 11 14 13 5 1 0 0 10 15 11 15 15 8 0 0 0 10 14 12 8 14 4 0 1 0 10 14 6 13 12 6 1 1 2 10 12 13 12 12 6 1 2 1 10 12 11 11 12 5 0 0 0 10 10 12 8 11 4 0 0 0 10 4 10 4 10 2 0 0 0 10 14 11 15 15 8 0 1 0 10 15 12 12 16 7 0 0 0 10 16 12 14 14 6 0 0 0 10 12 12 9 13 4 0 1 0 10 12 11 16 13 4 0 0 0 10 12 12 10 13 4 0 0 1 10 12 12 8 13 5 1 0 1 9 12 12 14 14 4 0 0 0 9 11 6 6 9 4 3 2 1 9 11 5 16 14 6 1 0 0 9 11 12 11 12 6 1 1 0 9 11 14 7 13 6 1 1 0 9 11 12 13 11 4 3 1 1 9 11 9 7 13 2 0 0 0 9 15 11 14 15 7 0 0 0 9 15 11 17 16 6 0 0 0 9 9 11 15 15 7 0 0 0 9 16 12 8 14 4 0 0 0 9 13 10 8 8 4 0 2 1 9 9 12 11 11 4 1 0 0 9 16 11 16 15 6 0 0 0 9 12 12 10 15 6 0 0 0 9 15 9 5 11 3 0 0 2 9 5 15 8 12 3 0 0 0 9 11 11 8 12 6 2 2 0 9 17 11 15 14 5 2 2 0 9 9 15 6 8 4 0 1 1 9 13 12 16 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 8 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Popularity[t] = + 1.04499634277405 + 0.119499232591395FindingFriends[t] + 0.241564101757528KnowingPeople[t] + 0.378018708974303Liked[t] + 0.607491789609728Celebrity[t] -0.0489828130821502B[t] + 0.174147430517712`2B`[t] + 0.508543630098061`3B`[t] -0.136999283939151Month[t] -0.00188085357862906t + 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) 1.04499634277405 3.928053 0.266 0.790588 0.395294 FindingFriends 0.119499232591395 0.096875 1.2335 0.219357 0.109678 KnowingPeople 0.241564101757528 0.061926 3.9008 0.000146 7.3e-05 Liked 0.378018708974303 0.098076 3.8543 0.000173 8.7e-05 Celebrity 0.607491789609728 0.157196 3.8645 0.000167 8.3e-05 B -0.0489828130821502 0.224347 -0.2183 0.827473 0.413736 `2B` 0.174147430517712 0.270802 0.6431 0.521181 0.26059 `3B` 0.508543630098061 0.318646 1.596 0.112661 0.056331 Month -0.136999283939151 0.40264 -0.3403 0.734156 0.367078 t -0.00188085357862906 0.00416 -0.4522 0.651825 0.325912 Multiple Linear Regression - Regression Statistics Multiple R 0.717470503794907 R-squared 0.514763923815717 Adjusted R-squared 0.484852110900247 F-TEST (value) 17.2093856454116 F-TEST (DF numerator) 9 F-TEST (DF denominator) 146 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.10772912266704 Sum Squared Residuals 648.608219962662 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 15 13.4991679358302 1.50083206416975 2 12 11.8981100087714 0.101889991228633 3 9 11.2935847722229 -2.29358477222292 4 10 12.0768505287867 -2.07685052878672 5 13 13.1972017006551 -0.197201700655095 6 16 15.2319939102505 0.768006089749507 7 14 12.9323548805402 1.06764511945981 8 16 13.9216499103898 2.07835008961024 9 10 10.9960803593227 -0.99608035932271 10 8 10.7774869517918 -2.77748695179182 11 12 12.2534224487608 -0.253422448760777 12 15 15.1206012967996 -0.120601296799615 13 14 10.9134325266142 3.08656747338582 14 14 12.8294273946596 1.17057260534035 15 12 13.0880808678829 -1.08808086788291 16 12 11.1902899797029 0.809710020297104 17 10 9.59770555485905 0.402294445140952 18 4 6.79756754087376 -2.79756754087376 19 14 15.2815827522669 -1.28158275226692 20 15 14.2708883148540 0.72911168514603 21 16 13.3886064572321 2.61139354276794 22 12 10.7600502371897 1.23994976281025 23 12 12.1554714328047 -0.155471432804706 24 12 11.3322488313704 0.667751168629633 25 12 11.5427480347434 0.457251965256588 26 12 12.3012178940586 -0.301217894058612 27 11 8.46962533788692 2.53037466211308 28 11 13.1100905284140 -2.11009052841395 29 11 12.1549938067566 -1.15499380675655 30 11 11.8038737203049 -0.803873720304907 31 11 11.4519360188544 -0.451936018854354 32 11 8.6474840743162 2.3525159256838 33 15 14.3690467642203 0.6309532357797 34 15 14.8623851352788 0.137614864721171 35 9 14.6068491588206 -5.60684915882057 36 16 10.8330247477272 5.16697525227284 37 13 9.1808716662534 3.81912833374659 38 9 10.3709164058374 -1.37091640583742 39 16 14.2333980566539 1.76660194334615 40 12 12.9016318251215 -0.901631825121454 41 15 9.01596982045073 5.98403017954927 42 5 9.81670811647123 -4.81670811647123 43 11 11.4096349362273 -0.409634936227332 44 17 13.2472484232903 3.75275157670973 45 9 9.10604536654858 -0.106045366548579 46 13 14.7177500231691 -1.71775002316915 47 16 14.2203721878772 1.77962781212282 48 16 13.8209516141241 2.17904838587587 49 14 14.3483495841271 -0.34834958412712 50 16 13.5435983667526 2.45640163324742 51 11 12.8177075336821 -1.81770753368211 52 11 11.8786166688209 -0.878616668820855 53 11 13.7454798740905 -2.74547987409047 54 12 12.2263929114652 -0.226392911465156 55 12 13.8219110758755 -1.82191107587552 56 12 12.0787466070131 -0.0787466070131064 57 14 13.7158698120011 0.284130187998901 58 10 10.8938292706448 -0.89382927064478 59 9 9.36948662402635 -0.369486624026354 60 12 12.2721352488986 -0.272135248898609 61 10 10.0150315755398 -0.0150315755398049 62 14 12.9817732388354 1.01822676116463 63 8 9.91845471742309 -1.91845471742309 64 16 14.4858215406104 1.51417845938958 65 14 15.7326316930428 -1.73263169304282 66 14 10.7303196129192 3.26968038708076 67 12 11.2259567009063 0.774043299093689 68 14 13.3732883951205 0.626711604879521 69 7 11.1119568444562 -4.11195684445617 70 19 13.9160274426111 5.08397255738893 71 15 12.8356176170298 2.16438238297024 72 8 11.2358065245671 -3.23580652456709 73 10 14.2688825796494 -4.26888257964936 74 13 12.9157668620937 0.0842331379063024 75 13 11.3430448027132 1.65695519728685 76 10 10.4753560056011 -0.475356005601079 77 12 9.1508164416875 2.8491835583125 78 15 17.4307739990282 -2.43077399902825 79 7 11.1523826850282 -4.15238268502825 80 14 14.2927376671468 -0.292737667146837 81 10 8.49722945746841 1.50277054253159 82 6 9.778959586541 -3.778959586541 83 11 11.3135254367911 -0.313525436791105 84 12 9.36712354727252 2.63287645272748 85 14 14.2858637651183 -0.285863765118301 86 12 13.3938248740130 -1.39382487401296 87 14 14.3869799035657 -0.386979903565727 88 11 10.1295339571697 0.870466042830313 89 10 9.31280240685697 0.687197593143033 90 13 13.4068525327782 -0.406852532778172 91 8 10.3371693538776 -2.33716935387761 92 9 11.8039272023981 -2.80392720239807 93 6 12.0021952766312 -6.0021952766312 94 12 13.1287885999813 -1.12878859998133 95 14 12.1418479246633 1.85815207533665 96 11 10.4348955403776 0.565104459622418 97 8 10.5675183131860 -2.56751831318605 98 7 9.21588333435467 -2.21588333435467 99 9 10.4441986498397 -1.44419864983974 100 14 12.0891262285322 1.91087377146778 101 13 10.1836602510807 2.81633974891929 102 15 12.6166745066313 2.38332549336872 103 5 5.22559837218436 -0.225598372184355 104 15 12.1546848703017 2.84531512969831 105 13 12.1776875868034 0.822312413196625 106 12 11.5236623723415 0.476337627658545 107 6 7.68133413696577 -1.68133413696577 108 7 9.56300248642058 -2.56300248642058 109 13 8.47050163971717 4.52949836028283 110 16 14.819878161772 1.18012183822800 111 10 13.2362850097808 -3.23628500978083 112 16 15.0641061926502 0.935893807349793 113 15 13.0816789573562 1.91832104264385 114 8 8.30768738998187 -0.307687389981865 115 11 12.5043362098399 -1.50433620983993 116 13 13.1382002746666 -0.138200274666613 117 16 15.1277839808410 0.872216019158957 118 11 8.59259420130102 2.40740579869898 119 14 14.3285466292558 -0.32854662925576 120 9 10.1143989090423 -1.11439890904234 121 8 10.1727908583271 -2.17279085832707 122 8 11.0159195208167 -3.01591952081667 123 11 11.7691753077400 -0.769175307740025 124 12 13.1879187906606 -1.18791879066064 125 11 10.9280990114490 0.0719009885509566 126 14 14.5188061294066 -0.518806129406621 127 11 12.5720705896542 -1.57207058965423 128 14 12.1874548876841 1.81254511231592 129 13 14.6398066696386 -1.63980666963858 130 12 10.6435888134915 1.35641118650845 131 4 5.77603215019458 -1.77603215019458 132 15 12.7839109993750 2.21608900062504 133 10 11.2696200846096 -1.26962008460959 134 13 13.7567513171914 -0.756751317191428 135 15 14.0748769251425 0.925123074857494 136 12 13.1336737189485 -1.13367371894854 137 13 13.1704274421111 -0.170427442111091 138 8 7.72415846530568 0.275841534694315 139 10 10.3945396148313 -0.394539614831348 140 15 13.5284138522989 1.47158614770114 141 16 14.2727781124753 1.72722188752472 142 16 14.7661164835338 1.23388351646619 143 14 12.8150343609158 1.18496563908422 144 14 12.9154330640543 1.08456693594569 145 12 10.5149301647128 1.48506983528721 146 15 13.1038147889474 1.89618521105256 147 13 12.8368377178223 0.163162282177732 148 16 13.0302388201548 2.96976117984522 149 14 13.3894213009251 0.61057869907493 150 8 9.84763560704182 -1.84763560704182 151 16 13.5598070242855 2.44019297571448 152 16 15.7469438831333 0.25305611686666 153 12 13.0038791776363 -1.00387917763625 154 11 12.1176937259202 -1.11769372592018 155 16 15.7413013223975 0.258698677602547 156 9 9.83635048557004 -0.836350485570044 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 13 0.565235017893787 0.869529964212426 0.434764982106213 14 0.395844308804126 0.791688617608253 0.604155691195874 15 0.269509968134723 0.539019936269446 0.730490031865277 16 0.186636967441124 0.373273934882248 0.813363032558876 17 0.120239139388344 0.240478278776689 0.879760860611656 18 0.217017024159084 0.434034048318167 0.782982975840916 19 0.364243292701171 0.728486585402341 0.63575670729883 20 0.275005018713094 0.550010037426187 0.724994981286906 21 0.283088104208521 0.566176208417041 0.71691189579148 22 0.210887010571300 0.421774021142601 0.7891129894287 23 0.176694478721540 0.353388957443081 0.82330552127846 24 0.125654982914781 0.251309965829561 0.87434501708522 25 0.0866417828838409 0.173283565767682 0.913358217116159 26 0.0765812365536045 0.153162473107209 0.923418763446396 27 0.0671015388700469 0.134203077740094 0.932898461129953 28 0.172415686860242 0.344831373720483 0.827584313139758 29 0.144493922184317 0.288987844368633 0.855506077815683 30 0.1148369847796 0.2296739695592 0.8851630152204 31 0.084780953573215 0.16956190714643 0.915219046426785 32 0.0728711260172474 0.145742252034495 0.927128873982753 33 0.0518076382115338 0.103615276423068 0.948192361788466 34 0.0379218229756794 0.0758436459513588 0.96207817702432 35 0.225157413559947 0.450314827119894 0.774842586440053 36 0.446130916308308 0.892261832616617 0.553869083691692 37 0.577861687757512 0.844276624484977 0.422138312242488 38 0.524759880017355 0.95048023996529 0.475240119982645 39 0.495880210901419 0.991760421802838 0.504119789098581 40 0.467811263222468 0.935622526444936 0.532188736777532 41 0.699023071202614 0.601953857594771 0.300976928797386 42 0.85881548724376 0.28236902551248 0.14118451275624 43 0.827155014403286 0.345689971193428 0.172844985596714 44 0.863579993966095 0.272840012067809 0.136420006033905 45 0.837186335844402 0.325627328311195 0.162813664155598 46 0.836228403939024 0.327543192121953 0.163771596060976 47 0.814300414805883 0.371399170388233 0.185699585194117 48 0.824409901755058 0.351180196489883 0.175590098244942 49 0.789352148479073 0.421295703041854 0.210647851520927 50 0.795803066118476 0.408393867763049 0.204196933881524 51 0.772248233313521 0.455503533372958 0.227751766686479 52 0.759289056428259 0.481421887143482 0.240710943571741 53 0.865944463348877 0.268111073302246 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0.89281824457968 0.214363510840642 0.107181755420321 72 0.907673365353169 0.184653269293662 0.092326634646831 73 0.94842616692006 0.103147666159878 0.0515738330799391 74 0.936156595506957 0.127686808986086 0.0638434044930429 75 0.929024153402011 0.141951693195977 0.0709758465979886 76 0.911499692139989 0.177000615720022 0.0885003078600112 77 0.932844757086761 0.134310485826477 0.0671552429132387 78 0.934912594926411 0.130174810147178 0.0650874050735888 79 0.97112368128819 0.0577526374236188 0.0288763187118094 80 0.96214294606661 0.075714107866781 0.0378570539333905 81 0.959431357363503 0.0811372852729943 0.0405686426364971 82 0.977480615906263 0.0450387681874732 0.0225193840937366 83 0.97121752101839 0.0575649579632204 0.0287824789816102 84 0.977142269322926 0.0457154613541484 0.0228577306770742 85 0.969794988119506 0.0604100237609889 0.0302050118804944 86 0.963184775566767 0.0736304488664664 0.0368152244332332 87 0.953074449887289 0.093851100225422 0.046925550112711 88 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0.00717483512826536 105 0.989949408274573 0.0201011834508546 0.0100505917254273 106 0.987266617746458 0.025466764507084 0.012733382253542 107 0.985281666146469 0.0294366677070626 0.0147183338535313 108 0.989717739714185 0.0205645205716297 0.0102822602858148 109 0.998983993828767 0.00203201234246677 0.00101600617123339 110 0.998671567811605 0.00265686437678938 0.00132843218839469 111 0.999577555351422 0.000844889297155985 0.000422444648577992 112 0.999304829128739 0.00139034174252294 0.000695170871261468 113 0.999293458297452 0.00141308340509546 0.000706541702547731 114 0.998843361918988 0.002313276162024 0.001156638081012 115 0.998266299464972 0.00346740107005555 0.00173370053502778 116 0.99713469342585 0.00573061314829885 0.00286530657414942 117 0.99557115566991 0.008857688660181 0.0044288443300905 118 0.99928575384014 0.00142849231972000 0.000714246159860001 119 0.99875689838233 0.00248620323533880 0.00124310161766940 120 0.997906634033036 0.00418673193392748 0.00209336596696374 121 0.99737515821669 0.00524968356661813 0.00262484178330906 122 0.996628323039009 0.0067433539219828 0.0033716769609914 123 0.994724900840254 0.0105501983194923 0.00527509915974613 124 0.994622704684837 0.0107545906303261 0.00537729531516303 125 0.991111858656729 0.0177762826865429 0.00888814134327143 126 0.98706737337536 0.0258652532492802 0.0129326266246401 127 0.983650942186366 0.0326981156272681 0.0163490578136341 128 0.97589199526384 0.0482160094723218 0.0241080047361609 129 0.98769067153392 0.0246186569321617 0.0123093284660809 130 0.989789219226607 0.0204215615467852 0.0102107807733926 131 0.984899765858125 0.0302004682837503 0.0151002341418751 132 0.98199610693167 0.0360077861366613 0.0180038930683307 133 0.97488405455083 0.0502318908983412 0.0251159454491706 134 0.96221397071084 0.0755720585783198 0.0377860292891599 135 0.937878628501461 0.124242742997077 0.0621213714985387 136 0.953839477303385 0.0923210453932305 0.0461605226966152 137 0.972144179918852 0.0557116401622961 0.0278558200811481 138 0.947584174198594 0.104831651602811 0.0524158258014057 139 0.90425799252785 0.191484014944301 0.0957420074721503 140 0.853858044619189 0.292283910761623 0.146141955380811 141 0.773120447739717 0.453759104520565 0.226879552260283 142 0.709951349663682 0.580097300672637 0.290048650336318 143 0.554429649931078 0.891140700137844 0.445570350068922 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 15 0.114503816793893 NOK 5% type I error level 42 0.320610687022901 NOK 10% type I error level 56 0.427480916030534 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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Software written by Ed van Stee & Patrick Wessa

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