Mini-Tutorial FMPS - Gender

*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, 23 Nov 2010 01:09:50 +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/Nov/23/t12904746075gq8bwgk9g7uqg4.htm/, Retrieved Tue, 23 Nov 2010 02:10:17 +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/Nov/23/t12904746075gq8bwgk9g7uqg4.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 «

0 25 25 11 11 7 7 8 8 25 23 23 0 17 17 6 6 17 17 8 8 30 25 25 0 18 18 8 8 12 12 9 9 22 19 19 0 16 16 10 10 12 12 7 7 22 29 29 0 20 20 10 10 11 11 4 4 25 25 25 0 16 16 11 11 11 11 11 11 23 21 21 0 18 18 16 16 12 12 7 7 17 22 22 0 17 17 11 11 13 13 7 7 21 25 25 0 30 30 12 12 16 16 10 10 19 18 18 0 23 23 8 8 11 11 10 10 15 22 22 0 18 18 12 12 10 10 8 8 16 15 15 0 21 21 9 9 9 9 9 9 22 20 20 0 31 31 14 14 17 17 11 11 23 20 20 0 27 27 15 15 11 11 9 9 23 21 21 0 21 21 9 9 14 14 13 13 19 21 21 0 16 16 8 8 15 15 9 9 23 24 24 0 20 20 9 9 15 15 6 6 25 24 24 0 17 17 9 9 13 13 6 6 22 23 23 0 25 25 16 16 18 18 16 16 26 24 24 0 26 26 11 11 18 18 5 5 29 18 18 0 25 25 8 8 12 12 7 7 32 25 25 0 17 17 9 9 17 17 9 9 25 21 21 0 32 32 12 12 18 18 12 12 28 22 22 0 22 22 9 9 14 14 9 9 25 23 23 0 17 17 9 9 16 16 5 5 25 23 23 0 20 20 14 14 14 14 10 10 18 24 24 0 29 29 10 10 12 12 8 8 25 23 23 0 23 23 14 14 17 17 7 7 25 21 21 0 20 20 10 10 12 12 8 8 20 28 28 0 11 11 6 6 6 6 4 4 15 16 16 0 26 26 13 13 12 12 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 PS[t] = + 7.649467706356 -1.23162104238454Gender[t] + 0.252442484510156CM[t] + 0.0447956145838135CM_G[t] -0.183344002951500D[t] -0.130645172512862D_G[t] + 0.573211458789485PE[t] -0.284652770114989PE_G[t] -0.144270406337441PC[t] + 0.110306020586233PC_G[t] + 0.207928610225245O[t] + 0.161964789752105O_G[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) 7.649467706356 2.774485 2.7571 0.006572 0.003286 Gender -1.23162104238454 4.877395 -0.2525 0.800995 0.400497 CM 0.252442484510156 0.17497 1.4428 0.151211 0.075605 CM_G 0.0447956145838135 0.113897 0.3933 0.694667 0.347334 D -0.183344002951500 0.345782 -0.5302 0.596752 0.298376 D_G -0.130645172512862 0.226144 -0.5777 0.564345 0.282172 PE 0.573211458789485 0.315116 1.8191 0.070939 0.035469 PE_G -0.284652770114989 0.21516 -1.323 0.187896 0.093948 PC -0.144270406337441 0.392223 -0.3678 0.713531 0.356766 PC_G 0.110306020586233 0.277517 0.3975 0.691594 0.345797 O 0.207928610225245 0.224399 0.9266 0.355652 0.177826 O_G 0.161964789752105 0.159538 1.0152 0.311673 0.155837 Multiple Linear Regression - Regression Statistics Multiple R 0.6216762962826 R-squared 0.386481417359652 Adjusted R-squared 0.340571863556632 F-TEST (value) 8.41832223022465 F-TEST (DF numerator) 11 F-TEST (DF denominator) 147 p-value 2.19315676730503e-11 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.42438888696967 Sum Squared Residuals 1723.78656963260 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 25 21.8822831877881 3.11771681221186 2 30 24.6996979590578 5.3003020409422 3 22 20.6728394782353 1.32716052176471 4 22 23.2172477003945 -1.21724770039454 5 25 22.7399609654401 2.26003903455985 6 23 19.5196950934320 3.48030490656795 7 17 19.3385350459549 -2.33853504595487 8 21 22.0094817127892 -1.00948171278924 9 19 23.7341169344749 -4.7341169344749 10 15 22.9461870992115 -7.94618709921148 11 16 17.3941561848706 -1.39415618487064 12 22 20.7547819340067 1.24521806599330 13 23 24.3977577855181 -1.39775778551813 14 23 21.6012862531107 1.39871374688932 15 19 22.4316112343517 -3.4316112343517 16 23 22.7935063459576 0.206493654042413 17 25 23.7703627241227 1.22963727587728 18 22 21.9316376495145 0.0683623504855256 19 26 23.5846611998534 2.41533880014655 20 29 23.6060930196684 5.39390698033159 21 32 25.0407953432596 6.95920465674041 22 25 22.2441924470041 2.75580755299586 23 28 26.3173553384188 1.68264466158119 24 25 23.6044936764052 1.39550632359480 25 25 22.8312781012892 2.16872189871083 26 18 21.7760006151216 -3.77600061512159 27 25 24.8280182030008 0.171981796999165 28 25 22.5256039357486 2.47439606425144 29 20 24.0023423110419 -4.00234231104186 30 15 16.5489407322832 -1.54894073228324 31 24 25.2136967791899 -1.21369677918994 32 26 24.2269251092524 1.77307489074755 33 14 16.4988057281025 -2.49880572810253 34 24 23.6882076241194 0.311792375880633 35 25 22.2901142469710 2.70988575302905 36 20 24.8228294406094 -4.82282944060945 37 21 21.3379147072532 -0.337914707253223 38 27 27.3806373577635 -0.380637357763498 39 23 24.8875313004724 -1.88753130047238 40 25 25.9236847186639 -0.923684718663891 41 20 22.3048028084823 -2.30480280848232 42 22 22.7351814508955 -0.73518145089549 43 25 24.2033849886421 0.796615011357936 44 25 23.4888364236611 1.51116357633895 45 17 23.6354773260822 -6.63547732608218 46 25 24.2370530452568 0.762946954743247 47 26 22.6333412788056 3.36665872119444 48 27 24.4833567931915 2.51664320680847 49 19 20.034027614319 -1.03402761431901 50 22 22.5712761469369 -0.5712761469369 51 32 28.9933452561865 3.00665474381354 52 21 24.7417584656952 -3.74175846569519 53 18 21.5563365981081 -3.55633659810815 54 23 23.1025501030034 -0.102550103003391 55 20 21.8295927359971 -1.82959273599708 56 21 22.6142313584396 -1.61423135843962 57 17 19.2372963735278 -2.23729637352783 58 18 20.3155783697159 -2.31557836971592 59 19 20.9172996003486 -1.91729960034863 60 22 22.4071052135827 -0.407105213582674 61 14 19.2545777542679 -5.25457775426785 62 18 26.5470290665061 -8.5470290665061 63 35 23.9911319079473 11.0088680920527 64 29 18.8830850110273 10.1169149889727 65 21 22.3522465116134 -1.35224651161343 66 25 21.2253467574949 3.77465324250508 67 26 22.8371997271915 3.16280027280853 68 17 17.5001681270367 -0.500168127036687 69 25 20.4870454276716 4.51295457232843 70 20 20.3710266060241 -0.371026606024127 71 22 21.2232967322472 0.776703267752824 72 24 22.6600076469483 1.33999235305170 73 21 23.211232304497 -2.21123230449701 74 26 25.2122420087995 0.787757991200531 75 24 20.7705608654128 3.22943913458719 76 16 20.5381252902889 -4.53812529028889 77 18 20.8620827426893 -2.8620827426893 78 19 19.5746801581444 -0.574680158144449 79 21 17.4860810651882 3.51391893481177 80 22 18.6764148543751 3.32358514562488 81 23 19.6406185486665 3.35938145133349 82 29 24.9251318457098 4.0748681542902 83 21 20.3323884410474 0.667611558952551 84 23 22.4791527699975 0.520847230002499 85 27 23.0933466330199 3.90665336698007 86 25 25.665739034365 -0.665739034365007 87 21 21.1025899837641 -0.102589983764134 88 10 17.5450137047189 -7.54501370471889 89 20 22.7748382661863 -2.7748382661863 90 26 22.4024263631451 3.59757363685485 91 24 24.1709732057960 -0.170973205796027 92 29 32.039524903068 -3.03952490306797 93 19 19.6375594339080 -0.637559433907965 94 24 22.8678535163986 1.13214648360137 95 19 21.1918508495598 -2.19185084955978 96 22 22.2332378774937 -0.233237877493686 97 17 24.1770748831824 -7.17707488318236 98 24 23.1891525756978 0.810847424302161 99 19 20.1213719624976 -1.12137196249761 100 19 22.2397545862169 -3.23975458621689 101 23 19.0119528982715 3.98804710172846 102 27 23.9996984663534 3.00030153364658 103 14 15.0272740449087 -1.02727404490866 104 22 23.0668347665731 -1.06683476657306 105 21 22.480071673226 -1.48007167322601 106 18 23.6785415443543 -5.67854154435434 107 20 23.5712368913594 -3.57123689135940 108 19 23.1012937277056 -4.10129372770564 109 24 22.9231870073325 1.07681299266751 110 25 26.0974028906659 -1.09740289066593 111 29 24.5702919430365 4.42970805696352 112 28 25.0080646376826 2.99193536231743 113 17 15.8795947450843 1.12040525491572 114 29 22.4470781605261 6.55292183947392 115 26 26.9014120632511 -0.901412063251071 116 14 17.8390211128729 -3.83902111287286 117 26 21.6947080858076 4.30529191419237 118 20 20.1628931194827 -0.162893119482682 119 32 24.7378779732098 7.2621220267902 120 23 21.2860663376028 1.71393366239721 121 21 21.9949450332778 -0.994945033277756 122 30 24.6954543998494 5.30454560015062 123 24 20.8889578555672 3.11104214443275 124 22 22.6345480739457 -0.634548073945691 125 24 22.2611111441645 1.73888885583555 126 24 22.4276344752233 1.57236552477667 127 24 20.2771256019659 3.72287439803415 128 19 18.0492519630842 0.950748036915772 129 31 27.8184536659721 3.18154633402795 130 22 26.8821117405066 -4.88211174050665 131 27 20.7453100963267 6.25468990367332 132 19 17.5094673012092 1.49053269879080 133 21 18.4446855636216 2.55531443637837 134 23 23.6545548145521 -0.654554814552129 135 19 20.6273326694800 -1.62733266948003 136 19 22.8328678522258 -3.83286785222581 137 20 22.0710595177597 -2.07105951775966 138 23 21.3932453453270 1.60675465467296 139 17 20.2204281949549 -3.22042819495492 140 17 22.5158548399329 -5.51585483993291 141 17 19.6490080600547 -2.64900806005469 142 21 24.0813306672545 -3.08133066725445 143 21 23.9449545369319 -2.94495453693186 144 18 20.6650625429321 -2.66506254293215 145 19 20.1098547493404 -1.10985474934044 146 20 24.2900101615384 -4.29001016153837 147 15 17.6814817408554 -2.68148174085543 148 24 21.5555549121971 2.44444508780294 149 20 17.9399506496497 2.06004935035033 150 22 22.2540686796497 -0.25406867964966 151 13 15.6183134640767 -2.61831346407673 152 19 17.8858937998625 1.11410620013753 153 21 21.2774151677470 -0.277415167746951 154 23 22.4836968759943 0.516303124005700 155 16 22.0445536366044 -6.04455363660442 156 26 22.0728915894783 3.92710841052168 157 21 22.1738873024122 -1.17388730241222 158 21 20.1046855891451 0.895314410854857 159 24 23.1882045116100 0.811795488390046 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 15 0.991089928435447 0.0178201431291054 0.00891007156455268 16 0.978993076153767 0.0420138476924658 0.0210069238462329 17 0.958232340501966 0.0835353189960685 0.0417676594980342 18 0.928479973760277 0.143040052479446 0.071520026239723 19 0.921914894597965 0.156170210804070 0.0780851054020352 20 0.940872745606796 0.118254508786409 0.0591272543932044 21 0.967651616648574 0.0646967667028513 0.0323483833514256 22 0.954107122819171 0.0917857543616574 0.0458928771808287 23 0.931532985387778 0.136934029224445 0.0684670146122223 24 0.900828688693332 0.198342622613336 0.099171311306668 25 0.866627613273007 0.266744773453985 0.133372386726993 26 0.864506687232454 0.270986625535092 0.135493312767546 27 0.821665547592747 0.356668904814507 0.178334452407253 28 0.781784167264593 0.436431665470814 0.218215832735407 29 0.806956764426897 0.386086471146207 0.193043235573103 30 0.764594033575445 0.47081193284911 0.235405966424555 31 0.715003696060088 0.569992607879825 0.284996303939912 32 0.66522335944443 0.669553281111139 0.334776640555569 33 0.614292727158252 0.771414545683496 0.385707272841748 34 0.550609387042139 0.898781225915722 0.449390612957861 35 0.528316968945499 0.943366062109002 0.471683031054501 36 0.625447078009828 0.749105843980343 0.374552921990172 37 0.564390643747608 0.871218712504785 0.435609356252392 38 0.508839557003748 0.982320885992504 0.491160442996252 39 0.46694829410928 0.93389658821856 0.53305170589072 40 0.427423880464196 0.854847760928392 0.572576119535804 41 0.390875235140014 0.78175047028003 0.609124764859986 42 0.336958003070702 0.673916006141403 0.663041996929298 43 0.286507786180003 0.573015572360007 0.713492213819997 44 0.251951714383036 0.503903428766073 0.748048285616964 45 0.390474184075861 0.780948368151723 0.609525815924139 46 0.338603067586609 0.677206135173218 0.661396932413391 47 0.330401806044187 0.660803612088374 0.669598193955813 48 0.320280738578883 0.640561477157767 0.679719261421117 49 0.274291624070354 0.548583248140708 0.725708375929646 50 0.236929977813501 0.473859955627001 0.763070022186499 51 0.217828677216026 0.435657354432051 0.782171322783974 52 0.228580048111317 0.457160096222634 0.771419951888683 53 0.225761286385816 0.451522572771632 0.774238713614184 54 0.187330445476787 0.374660890953574 0.812669554523213 55 0.161890690296247 0.323781380592495 0.838109309703753 56 0.135818449185777 0.271636898371555 0.864181550814223 57 0.115689563144120 0.231379126288241 0.88431043685588 58 0.0995442952146906 0.199088590429381 0.90045570478531 59 0.0845276793080294 0.169055358616059 0.91547232069197 60 0.0661396861046581 0.132279372209316 0.933860313895342 61 0.084477350442251 0.168954700884502 0.915522649557749 62 0.253780187053354 0.507560374106707 0.746219812946646 63 0.702129226138421 0.595741547723158 0.297870773861579 64 0.923934497026453 0.152131005947094 0.076065502973547 65 0.908202957896551 0.183594084206898 0.091797042103449 66 0.919212439900795 0.161575120198411 0.0807875600992054 67 0.918423374626801 0.163153250746398 0.081576625373199 68 0.898285557316561 0.203428885366878 0.101714442683439 69 0.91000670078832 0.179986598423359 0.0899932992116797 70 0.889099009056177 0.221801981887647 0.110900990943823 71 0.868656798231746 0.262686403536507 0.131343201768254 72 0.845843415731828 0.308313168536345 0.154156584268172 73 0.82426933749693 0.351461325006139 0.175730662503070 74 0.796757597733303 0.406484804533393 0.203242402266697 75 0.789833411238326 0.420333177523347 0.210166588761674 76 0.802410046824846 0.395179906350308 0.197589953175154 77 0.788384621167523 0.423230757664954 0.211615378832477 78 0.753383931789521 0.493232136420958 0.246616068210479 79 0.753901481978202 0.492197036043595 0.246098518021798 80 0.738061747195004 0.523876505609992 0.261938252804996 81 0.724422180204356 0.551155639591287 0.275577819795644 82 0.74486933914194 0.51026132171612 0.25513066085806 83 0.772488421496388 0.455023157007225 0.227511578503612 84 0.73885237518055 0.522295249638899 0.261147624819450 85 0.747706606416285 0.50458678716743 0.252293393583715 86 0.723671336633689 0.552657326732622 0.276328663366311 87 0.681729909974356 0.636540180051287 0.318270090025644 88 0.78495549835949 0.430089003281021 0.215044501640510 89 0.769433838985687 0.461132322028627 0.230566161014313 90 0.799046546049217 0.401906907901567 0.200953453950783 91 0.777233585788498 0.445532828423003 0.222766414211502 92 0.75319643582929 0.493607128341421 0.246803564170711 93 0.71232585691901 0.57534828616198 0.28767414308099 94 0.668776669325121 0.662446661349758 0.331223330674879 95 0.62779186022122 0.74441627955756 0.37220813977878 96 0.579436122289843 0.841127755420313 0.420563877710157 97 0.610803191721143 0.778393616557714 0.389196808278857 98 0.56305988252169 0.873880234956621 0.436940117478311 99 0.515419726282715 0.96916054743457 0.484580273717285 100 0.493308076048114 0.986616152096228 0.506691923951886 101 0.483156204953527 0.966312409907054 0.516843795046473 102 0.467953348302517 0.935906696605033 0.532046651697484 103 0.420904224779434 0.841808449558868 0.579095775220566 104 0.389479119291344 0.778958238582687 0.610520880708656 105 0.362525352015146 0.725050704030293 0.637474647984854 106 0.43332425014131 0.86664850028262 0.56667574985869 107 0.42889567413522 0.85779134827044 0.57110432586478 108 0.431411429331737 0.862822858663475 0.568588570668263 109 0.390370071107653 0.780740142215306 0.609629928892347 110 0.352740732054549 0.705481464109097 0.647259267945451 111 0.368190926655984 0.736381853311967 0.631809073344016 112 0.366324938492514 0.732649876985028 0.633675061507486 113 0.325703879125682 0.651407758251364 0.674296120874318 114 0.448544038377966 0.897088076755932 0.551455961622034 115 0.394796671119665 0.78959334223933 0.605203328880335 116 0.406195153091627 0.812390306183254 0.593804846908373 117 0.428563770795836 0.857127541591672 0.571436229204164 118 0.372116719784850 0.744233439569699 0.62788328021515 119 0.621211216646473 0.757577566707055 0.378788783353527 120 0.640558863033935 0.718882273932129 0.359441136966065 121 0.587104062916222 0.825791874167557 0.412895937083778 122 0.625105106436965 0.74978978712607 0.374894893563035 123 0.617444449578850 0.765111100842299 0.382555550421150 124 0.555486926514508 0.889026146970984 0.444513073485492 125 0.50427776886023 0.99144446227954 0.49572223113977 126 0.470549917653021 0.941099835306043 0.529450082346979 127 0.480032512679873 0.960065025359746 0.519967487320127 128 0.412664922414595 0.82532984482919 0.587335077585405 129 0.63514123534114 0.72971752931772 0.36485876465886 130 0.594301002917109 0.811397994165782 0.405698997082891 131 0.799401464823038 0.401197070353924 0.200598535176962 132 0.738203627454924 0.523592745090153 0.261796372545076 133 0.688652100677118 0.622695798645765 0.311347899322882 134 0.61932478158588 0.76135043682824 0.38067521841412 135 0.542998598279951 0.914002803440098 0.457001401720049 136 0.468036793584562 0.936073587169124 0.531963206415438 137 0.382235539899655 0.76447107979931 0.617764460100345 138 0.326107184438964 0.652214368877927 0.673892815561036 139 0.272257765770569 0.544515531541139 0.72774223422943 140 0.308337983616509 0.616675967233019 0.69166201638349 141 0.225118962606646 0.450237925213291 0.774881037393354 142 0.158919981688500 0.317839963377000 0.8410800183115 143 0.116192285727414 0.232384571454829 0.883807714272586 144 0.0679527430220946 0.135905486044189 0.932047256977905 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 2 0.0153846153846154 OK 10% type I error level 5 0.0384615384615385 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/23/t12904746075gq8bwgk9g7uqg4/10a0z41290474575.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12904746075gq8bwgk9g7uqg4/10a0z41290474575.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12904746075gq8bwgk9g7uqg4/1wq1v1290474575.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12904746075gq8bwgk9g7uqg4/1wq1v1290474575.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12904746075gq8bwgk9g7uqg4/2wq1v1290474575.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12904746075gq8bwgk9g7uqg4/2wq1v1290474575.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12904746075gq8bwgk9g7uqg4/3wq1v1290474575.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12904746075gq8bwgk9g7uqg4/3wq1v1290474575.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12904746075gq8bwgk9g7uqg4/47zjg1290474575.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12904746075gq8bwgk9g7uqg4/47zjg1290474575.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12904746075gq8bwgk9g7uqg4/57zjg1290474575.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12904746075gq8bwgk9g7uqg4/57zjg1290474575.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12904746075gq8bwgk9g7uqg4/67zjg1290474575.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12904746075gq8bwgk9g7uqg4/67zjg1290474575.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12904746075gq8bwgk9g7uqg4/7i90j1290474575.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12904746075gq8bwgk9g7uqg4/7i90j1290474575.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12904746075gq8bwgk9g7uqg4/8i90j1290474575.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12904746075gq8bwgk9g7uqg4/8i90j1290474575.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12904746075gq8bwgk9g7uqg4/9a0z41290474575.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12904746075gq8bwgk9g7uqg4/9a0z41290474575.ps (open in new window)

Parameters (Session):

par1 = 10 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 10 ; 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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This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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