*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: Thu, 02 Dec 2010 15:07:12 +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/02/t129130235366hc4rpoc9o0nfj.htm/, Retrieved Thu, 02 Dec 2010 16:05:53 +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/02/t129130235366hc4rpoc9o0nfj.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 «

3 3 4 4 4 4 3 4 3 4 4 4 3 4 3 3 3 4 3 2 2 2 3 3 3 2 3 4 4 4 5 4 4 4 5 3 2 4 3 4 2 3 4 4 4 2 4 2 3 2 4 3 2 4 2 3 3 4 3 4 3 4 4 4 4 4 2 4 3 5 4 2 4 3 5 2 3 3 4 4 3 2 4 3 3 4 4 4 4 4 2 2 3 3 4 2 1 2 3 2 3 3 2 4 4 4 4 4 4 4 2 2 3 3 4 2 3 4 3 4 3 3 4 4 4 4 4 3 4 4 4 3 3 4 4 3 3 2 4 3 3 4 3 4 3 4 4 4 4 4 2 4 3 2 3 3 3 3 4 4 4 4 4 4 4 2 2 4 3 4 4 4 3 4 4 4 3 4 4 4 2 2 2 3 3 3 4 3 4 4 4 4 4 4 4 4 4 4 3 4 3 4 3 4 3 4 2 5 3 5 3 2 3 3 4 3 3 3 3 4 3 4 4 3 4 3 5 4 4 4 2 2 5 2 5 4 3 3 3 4 4 3 4 4 4 4 2 4 3 4 2 2 2 3 3 3 3 4 4 4 3 2 4 3 4 3 4 4 4 5 3 3 3 4 4 2 3 3 4 3 4 4 3 5 3 4 1 2 4 4 4 4 4 4 4 3 2 4 3 4 4 4 4 3 4 3 4 3 3 3 4 4 4 4 3 3 2 3 3 3 3 4 4 4 4 3 2 4 3 4 3 4 4 3 4 4 4 4 3 4 1 1 4 1 5 4 4 4 4 3 4 4 4 4 4 3 3 4 4 3 5 3 2 4 2 3 3 3 4 4 3 3 4 4 4 3 3 4 3 5 4 3 3 3 2 4 4 4 3 4 3 1 4 3 4 3 3 4 4 4 4 3 3 4 4 2 3 3 4 3 4 4 3 2 4 3 3 4 3 5 2 2 4 3 2 4 3 2 4 2 4 4 4 4 4 3 3 3 4 4 4 4 4 4 3 4 3 3 4 4 4 4 4 4 4 3 4 3 4 4 3 3 3 3 4 4 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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation SocialVisible[t] = + 1.12351677669589 + 0.205006149466121ManyFriends[t] + 0.0858332162777073MakeNewFriends[t] + 0.258524308214946QuiteAccepted[t] + 0.107736898186249IntendMakeNewFriends[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) 1.12351677669589 0.460376 2.4404 0.015867 0.007934 ManyFriends 0.205006149466121 0.074354 2.7572 0.006575 0.003288 MakeNewFriends 0.0858332162777073 0.09535 0.9002 0.369502 0.184751 QuiteAccepted 0.258524308214946 0.095395 2.71 0.007533 0.003767 IntendMakeNewFriends 0.107736898186249 0.093528 1.1519 0.251238 0.125619 Multiple Linear Regression - Regression Statistics Multiple R 0.438634013380361 R-squared 0.192399797694162 Adjusted R-squared 0.170273764754276 F-TEST (value) 8.69563008501758 F-TEST (DF numerator) 4 F-TEST (DF denominator) 146 p-value 2.52776170817093e-06 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.713165967765175 Sum Squared Residuals 74.256431846452 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3 3.54691291580986 -0.546912915809856 2 4 3.28838860759491 0.711611392405088 3 4 3.55834895081202 0.441651049187978 4 3 3.07291481122241 -0.0729148112224138 5 2 2.88981234366483 -0.889812343664835 6 2 3.54691291580986 -1.54691291580986 7 5 3.85965596346223 1.14034403653777 8 3 3.08338245812879 -0.0833824581287911 9 2 3.54691291580986 -1.54691291580986 10 2 3.10625452813312 -1.10625452813312 11 4 3.15977268688194 0.840227313118055 12 3 3.28838860759491 -0.288388607594912 13 3 3.75191906527598 -0.751919065275978 14 4 3.19111935631504 0.80888064368496 15 4 3.19111935631504 0.80888064368496 16 2 3.46107969953215 -1.46107969953215 17 3 2.97564555994254 0.0243544400574579 18 4 3.75191906527598 0.248080934724022 19 2 2.99754924185108 -0.997549241851084 20 2 2.49123607973476 -0.491236079734758 21 3 3.37524648325444 -0.375246483254443 22 4 3.75191906527598 0.248080934724022 23 2 2.99754924185108 -0.997549241851084 24 2 3.28838860759491 -1.28838860759491 25 3 3.54691291580986 -0.546912915809858 26 4 3.66608584899827 0.333914151001729 27 4 3.46107969953215 0.53892030046785 28 3 3.26750958506819 -0.267509585068194 29 3 3.55834895081202 -0.558348950812022 30 4 3.75191906527598 0.248080934724022 31 2 3.04130033438213 -1.04130033438213 32 3 3.46107969953215 -0.46107969953215 33 4 3.75191906527598 0.248080934724022 34 2 3.08338245812879 -1.08338245812879 35 4 3.66608584899827 0.333914151001729 36 4 3.54691291580986 0.453087084190142 37 2 2.80397912738713 -0.803979127387127 38 3 3.66608584899827 -0.666085848998271 39 4 3.75191906527598 0.248080934724022 40 4 3.49339475706103 0.506605242938968 41 3 3.55834895081202 -0.558348950812022 42 4 3.27695257259275 0.723047427407252 43 3 2.99754924185108 0.00245075814891616 44 3 3.20255539131720 -0.202555391317204 45 3 3.49339475706103 -0.493394757061032 46 3 3.9569252147421 -0.956925214742099 47 2 3.0184282643778 -1.01842826437780 48 4 3.20255539131720 0.797444608682795 49 4 3.54691291580986 0.453087084190142 50 4 3.08338245812879 0.916617541871209 51 2 2.80397912738713 -0.803979127387127 52 3 3.54691291580986 -0.546912915809858 53 3 3.08338245812879 -0.0833824581287911 54 3 3.85965596346223 -0.859655963462227 55 3 3.46107969953215 -0.46107969953215 56 2 3.3533428013459 -1.3533428013459 57 4 3.81687325902697 0.183126740973032 58 4 2.9652341843222 1.03476581567780 59 4 3.75191906527598 0.248080934724022 60 3 3.08338245812879 -0.0833824581287911 61 4 3.49339475706103 0.506605242938968 62 3 3.29982464259708 -0.299824642597076 63 4 3.64418216708973 0.355817832910271 64 3 2.88981234366483 0.110187656335165 65 3 3.75191906527598 -0.751919065275978 66 3 3.08338245812879 -0.0833824581287911 67 3 3.49339475706103 -0.493394757061032 68 4 3.49339475706103 0.506605242938968 69 1 2.46906459041903 -1.46906459041903 70 4 3.64418216708973 0.355817832910271 71 4 3.75191906527598 0.248080934724022 72 3 3.43917601762361 -0.439176017623608 73 5 3.15977268688194 1.84022731311806 74 3 3.46107969953215 -0.46107969953215 75 3 3.54691291580986 -0.546912915809858 76 3 3.39612550578116 -0.396125505781161 77 4 2.98708159494471 1.01291840505529 78 4 3.49339475706103 0.506605242938968 79 3 2.87837630866267 0.121623691337329 80 3 3.54691291580986 -0.546912915809858 81 4 3.46107969953215 0.53892030046785 82 2 3.3533428013459 -1.3533428013459 83 4 3.14903723256838 0.85096276743162 84 3 3.39612550578116 -0.396125505781161 85 2 2.86790866175629 -0.867908661756293 86 4 3.15977268688194 0.840227313118055 87 4 3.75191906527598 0.248080934724022 88 3 3.46107969953215 -0.46107969953215 89 4 3.64418216708973 0.355817832910271 90 4 3.46107969953215 0.53892030046785 91 4 3.75191906527598 0.248080934724022 92 3 3.66608584899827 -0.666085848998271 93 3 3.20255539131720 -0.202555391317204 94 4 3.08338245812879 0.916617541871209 95 5 2.31854498779752 2.68145501220248 96 3 2.82485814991385 0.175141850086155 97 4 3.17024033378832 0.829759666211678 98 4 3.28838860759491 0.711611392405088 99 4 3.75191906527598 0.248080934724022 100 4 3.75191906527598 0.248080934724022 101 5 3.91317412221105 1.08682587778895 102 4 3.28838860759491 0.711611392405088 103 3 2.27549447595507 0.724505524044928 104 4 3.54691291580986 0.453087084190142 105 4 3.09481849313096 0.905181506869045 106 4 3.75191906527598 0.248080934724022 107 4 3.25607355006603 0.74392644993397 108 4 3.46107969953215 0.53892030046785 109 3 3.26750958506819 -0.267509585068194 110 4 3.28838860759491 0.711611392405088 111 4 3.75191906527598 0.248080934724022 112 4 3.75191906527598 0.248080934724022 113 4 3.34363200641416 0.65636799358584 114 4 3.49339475706103 0.506605242938968 115 4 3.34190676634374 0.658093233656263 116 4 3.54691291580986 0.453087084190142 117 3 3.40756154078333 -0.407561540783325 118 4 3.28838860759491 0.711611392405088 119 3 3.49339475706103 -0.493394757061032 120 3 3.25607355006603 -0.256073550066030 121 4 3.75191906527598 0.248080934724022 122 4 3.49339475706103 0.506605242938968 123 4 3.28838860759491 0.711611392405088 124 4 3.75191906527598 0.248080934724022 125 3 3.54691291580986 -0.546912915809858 126 3 3.3533428013459 -0.353342801345901 127 1 1.95228378139632 -0.952283781396325 128 4 3.75191906527598 0.248080934724022 129 3 3.75191906527598 -0.751919065275978 130 4 3.34190676634374 0.658093233656263 131 4 3.54691291580986 0.453087084190142 132 3 3.75191906527598 -0.751919065275978 133 4 3.54691291580986 0.453087084190142 134 4 3.75191906527598 0.248080934724022 135 2 3.34190676634374 -1.34190676634374 136 4 3.9569252147421 0.0430747852579012 137 3 3.3533428013459 -0.353342801345901 138 3 3.66608584899827 -0.666085848998271 139 4 3.54691291580986 0.453087084190142 140 4 3.75191906527598 0.248080934724022 141 3 3.54691291580986 -0.546912915809858 142 3 3.54691291580986 -0.546912915809858 143 3 3.34190676634374 -0.341906766343737 144 4 3.75191906527598 0.248080934724022 145 4 3.75191906527598 0.248080934724022 146 3 3.54691291580986 -0.546912915809858 147 4 4.01044337349092 -0.0104433734909239 148 3 2.97564555994254 0.0243544400574579 149 4 3.64418216708973 0.355817832910271 150 4 3.49339475706103 0.506605242938968 151 4 3.18065170940866 0.819348290591337 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.348049599806604 0.696099199613208 0.651950400193396 9 0.306033364114778 0.612066728229555 0.693966635885222 10 0.832174546466564 0.335650907066873 0.167825453533437 11 0.989961074650637 0.0200778506987261 0.0100389253493630 12 0.981336493405565 0.0373270131888702 0.0186635065944351 13 0.976560566355956 0.0468788672880872 0.0234394336440436 14 0.974436410912984 0.0511271781740309 0.0255635890870155 15 0.965972465703523 0.0680550685929531 0.0340275342964766 16 0.985016794063572 0.0299664118728564 0.0149832059364282 17 0.977736033474614 0.0445279330507722 0.0222639665253861 18 0.97028028043963 0.0594394391207411 0.0297197195603705 19 0.974510265626771 0.0509794687464579 0.0254897343732290 20 0.965080919960152 0.0698381600796966 0.0349190800398483 21 0.949884541113095 0.100230917773810 0.0501154588869049 22 0.934355404081713 0.131289191836574 0.065644595918287 23 0.938700071638551 0.122599856722897 0.0612999283614487 24 0.965900924935426 0.0681981501291475 0.0340990750645737 25 0.954486086740536 0.0910278265189273 0.0455139132594637 26 0.94407110662251 0.111857786754979 0.0559288933774895 27 0.944398473164635 0.111203053670731 0.0556015268353654 28 0.926347957877192 0.147304084245615 0.0736520421228077 29 0.910258367143783 0.179483265712435 0.0897416328562174 30 0.888585203194873 0.222829593610253 0.111414796805127 31 0.898998570972616 0.202002858054767 0.101001429027384 32 0.877270461797157 0.245459076405686 0.122729538202843 33 0.851085289610113 0.297829420779774 0.148914710389887 34 0.863418760614653 0.273162478770695 0.136581239385347 35 0.83946971530985 0.321060569380299 0.160530284690150 36 0.82403887858614 0.351922242827721 0.175961121413860 37 0.81137529237271 0.377249415254581 0.188624707627290 38 0.8024940913965 0.395011817207001 0.197505908603500 39 0.767660707660962 0.464678584678075 0.232339292339038 40 0.756668991803284 0.486662016393431 0.243331008196716 41 0.733503331232836 0.532993337534328 0.266496668767164 42 0.744294214120265 0.51141157175947 0.255705785879735 43 0.709105588267532 0.581788823464937 0.290894411732468 44 0.666078045632144 0.667843908735713 0.333921954367856 45 0.636598035680723 0.726803928638554 0.363401964319277 46 0.678344303831735 0.64331139233653 0.321655696168265 47 0.702872722330415 0.59425455533917 0.297127277669585 48 0.742884724801514 0.514230550396972 0.257115275198486 49 0.722572209363079 0.554855581273842 0.277427790636921 50 0.771580016411437 0.456839967177126 0.228419983588563 51 0.776977840133983 0.446044319732034 0.223022159866017 52 0.759472110283629 0.481055779432742 0.240527889716371 53 0.720071861010776 0.559856277978448 0.279928138989224 54 0.743610304010747 0.512779391978505 0.256389695989253 55 0.719943544097387 0.560112911805225 0.280056455902613 56 0.813539598152449 0.372920803695103 0.186460401847551 57 0.787156952157202 0.425686095685596 0.212843047842798 58 0.821241271350417 0.357517457299165 0.178758728649583 59 0.794918977971189 0.410162044057622 0.205081022028811 60 0.759464953456233 0.481070093087535 0.240535046543767 61 0.756219053339707 0.487561893320586 0.243780946660293 62 0.734886519362695 0.53022696127461 0.265113480637305 63 0.713458541877737 0.573082916244526 0.286541458122263 64 0.683548134065066 0.632903731869868 0.316451865934934 65 0.688749389250827 0.622501221498345 0.311250610749173 66 0.645817987282068 0.708364025435864 0.354182012717932 67 0.621317330578991 0.757365338842018 0.378682669421009 68 0.610856954013604 0.778286091972793 0.389143045986396 69 0.748409930498557 0.503180139002886 0.251590069501443 70 0.722354044181411 0.555291911637178 0.277645955818589 71 0.6863186915199 0.627362616960201 0.313681308480100 72 0.661345913496027 0.677308173007946 0.338654086503973 73 0.876730884512819 0.246538230974362 0.123269115487181 74 0.865903607354542 0.268192785290916 0.134096392645458 75 0.858812906906985 0.282374186186029 0.141187093093015 76 0.850491094031523 0.299017811936953 0.149508905968477 77 0.886145885933951 0.227708228132097 0.113854114066049 78 0.876624699000377 0.246750601999246 0.123375300999623 79 0.855135382171036 0.289729235657928 0.144864617828964 80 0.849420686777657 0.301158626444686 0.150579313222343 81 0.835819686434466 0.328360627131069 0.164180313565534 82 0.909451173904185 0.181097652191630 0.0905488260958152 83 0.917046271594391 0.165907456811217 0.0829537284056086 84 0.915914181525409 0.168171636949182 0.0840858184745912 85 0.931070368916603 0.137859262166793 0.0689296310833967 86 0.943307976099897 0.113384047800205 0.0566920239001026 87 0.929807281008718 0.140385437982565 0.0701927189912824 88 0.924383522582784 0.151232954834432 0.0756164774172162 89 0.91383098148379 0.172338037032421 0.0861690185162105 90 0.903205302915832 0.193589394168337 0.0967946970841683 91 0.882909793064006 0.234180413871989 0.117090206935994 92 0.884343906558136 0.231312186883728 0.115656093441864 93 0.872536999469305 0.254926001061391 0.127463000530695 94 0.876839389052196 0.246321221895609 0.123160610947804 95 0.998917276190154 0.00216544761969174 0.00108272380984587 96 0.998546846125694 0.00290630774861171 0.00145315387430586 97 0.998783654470279 0.00243269105944228 0.00121634552972114 98 0.998529625833307 0.00294074833338622 0.00147037416669311 99 0.997825654279675 0.00434869144064969 0.00217434572032485 100 0.996819773445662 0.00636045310867662 0.00318022655433831 101 0.997850712282475 0.00429857543504926 0.00214928771752463 102 0.997420791858313 0.00515841628337454 0.00257920814168727 103 0.996529128992685 0.00694174201463092 0.00347087100731546 104 0.99547701110574 0.00904597778852042 0.00452298889426021 105 0.997421474426686 0.00515705114662889 0.00257852557331445 106 0.996183395316945 0.00763320936610923 0.00381660468305462 107 0.996932083429304 0.00613583314139258 0.00306791657069629 108 0.9968197942786 0.0063604114428019 0.00318020572140095 109 0.99565281900981 0.0086943619803793 0.00434718099018965 110 0.994791459603975 0.0104170807920491 0.00520854039602457 111 0.992358857321678 0.0152822853566442 0.00764114267832211 112 0.988955091469245 0.0220898170615098 0.0110449085307549 113 0.993498477161285 0.0130030456774307 0.00650152283871537 114 0.991058617954443 0.0178827640911142 0.00894138204555712 115 0.991957829013445 0.0160843419731109 0.00804217098655546 116 0.990234037074525 0.0195319258509501 0.00976596292547505 117 0.985771758756084 0.028456482487831 0.0142282412439155 118 0.985636495237715 0.0287270095245700 0.0143635047622850 119 0.987684259515362 0.0246314809692761 0.0123157404846381 120 0.985819458054457 0.0283610838910865 0.0141805419455432 121 0.978871633907843 0.0422567321843138 0.0211283660921569 122 0.97044679889146 0.0591064022170794 0.0295532011085397 123 0.973675385994068 0.0526492280118639 0.0263246140059319 124 0.96192554829123 0.0761489034175383 0.0380744517087691 125 0.954322902756643 0.0913541944867141 0.0456770972433571 126 0.938740166410123 0.122519667179754 0.0612598335898768 127 0.953965628500888 0.092068742998225 0.0460343714991125 128 0.93507532081189 0.129849358376221 0.0649246791881103 129 0.949296704337172 0.101406591325656 0.0507032956628281 130 0.979176293089565 0.0416474138208703 0.0208237069104352 131 0.983082511513474 0.0338349769730514 0.0169174884865257 132 0.99237958850167 0.0152408229966612 0.00762041149833062 133 0.995663714049367 0.00867257190126495 0.00433628595063248 134 0.991648263879035 0.0167034722419298 0.00835173612096492 135 0.997208446546167 0.00558310690766585 0.00279155345383293 136 0.99631693862529 0.00736612274942095 0.00368306137471047 137 0.992568494104168 0.0148630117916640 0.00743150589583201 138 0.983299506032979 0.0334009879340419 0.0167004939670209 139 0.993634822847365 0.0127303543052692 0.0063651771526346 140 0.98361830263482 0.0327633947303598 0.0163816973651799 141 0.97108826476277 0.0578234704744602 0.0289117352372301 142 0.956707594685902 0.0865848106281963 0.0432924053140982 143 0.916759477556936 0.166481044886129 0.0832405224430644 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 18 0.132352941176471 NOK 5% type I error level 43 0.316176470588235 NOK 10% type I error level 57 0.419117647058824 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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Software written by Ed van Stee & Patrick Wessa

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