*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: Wed, 24 Nov 2010 15:09:48 +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/24/t1290611289guc3peb0batgqy2.htm/, Retrieved Wed, 24 Nov 2010 16:08:09 +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/24/t1290611289guc3peb0batgqy2.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 0 11 0 7 0 8 0 25 23 0 0 17 0 6 0 17 0 8 0 30 25 0 0 18 0 8 0 12 0 9 0 22 19 0 0 16 0 10 0 12 0 7 0 22 29 0 0 20 0 10 0 11 0 4 0 25 25 0 0 16 0 11 0 11 0 11 0 23 21 0 0 18 0 16 0 12 0 7 0 17 22 0 0 17 0 11 0 13 0 7 0 21 25 0 0 30 0 12 0 16 0 10 0 19 18 0 0 23 0 8 0 11 0 10 0 15 22 0 0 18 0 12 0 10 0 8 0 16 15 0 0 21 0 9 0 9 0 9 0 22 20 0 0 31 0 14 0 17 0 11 0 23 20 0 0 27 0 15 0 11 0 9 0 23 21 0 0 21 0 9 0 14 0 13 0 19 21 0 0 16 0 8 0 15 0 9 0 23 24 0 0 20 0 9 0 15 0 6 0 25 24 0 0 17 0 9 0 13 0 6 0 22 23 0 0 25 0 16 0 18 0 16 0 26 24 0 0 26 0 11 0 18 0 5 0 29 18 0 0 25 0 8 0 12 0 7 0 32 25 0 0 17 0 9 0 17 0 9 0 25 21 0 0 32 0 12 0 18 0 12 0 28 22 0 0 22 0 9 0 14 0 9 0 25 23 0 0 17 0 9 0 16 0 5 0 25 23 0 0 20 0 14 0 14 0 10 0 18 24 0 0 29 0 10 0 12 0 8 0 25 23 0 0 23 0 14 0 17 0 7 0 25 21 0 0 20 0 10 0 12 0 8 0 20 28 0 0 11 0 6 0 6 0 4 0 15 16 0 0 26 0 13 0 12 0 8 0 24 29 0 0 22 0 10 0 12 0 8 0 26 27 0 0 14 0 15 0 13 0 8 0 14 16 0 0 19 0 12 0 14 0 7 0 24 28 0 0 20 0 11 0 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 PS[t] = + 7.64946770635602 -1.23162104238459Gender[t] + 0.297238099093969CM[t] + 0.0447956145838136CM_G[t] -0.313989175464361D[t] -0.130645172512864D_G[t] + 0.288558688674496PE[t] -0.284652770114991PE_G[t] -0.0339643857512098PC[t] + 0.110306020586238PC_G[t] + 0.369893399977349O[t] + 0.161964789752108O_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) 7.64946770635602 2.774485 2.7571 0.006572 0.003286 Gender -1.23162104238459 4.877395 -0.2525 0.800995 0.400497 CM 0.297238099093969 0.076686 3.8761 0.00016 8e-05 CM_G 0.0447956145838136 0.113897 0.3933 0.694667 0.347334 D -0.313989175464361 0.151023 -2.0791 0.039347 0.019674 D_G -0.130645172512864 0.226144 -0.5777 0.564345 0.282172 PE 0.288558688674496 0.132921 2.1709 0.031543 0.015772 PE_G -0.284652770114991 0.21516 -1.323 0.187896 0.093948 PC -0.0339643857512098 0.160025 -0.2122 0.83221 0.416105 PC_G 0.110306020586238 0.277517 0.3975 0.691594 0.345797 O 0.369893399977349 0.091109 4.0599 8e-05 4e-05 O_G 0.161964789752108 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 3.11771681221192 2 30 24.6996979590578 5.30030204094217 3 22 20.6728394782353 1.32716052176471 4 22 23.2172477003945 -1.21724770039454 5 25 22.7399609654402 2.26003903455985 6 23 19.5196950934320 3.48030490656795 7 17 19.3385350459549 -2.33853504595486 8 21 22.0094817127892 -1.00948171278925 9 19 23.7341169344749 -4.7341169344749 10 15 22.9461870992115 -7.94618709921147 11 16 17.3941561848707 -1.39415618487066 12 22 20.7547819340067 1.24521806599331 13 23 24.3977577855181 -1.39775778551813 14 23 21.6012862531107 1.39871374688932 15 19 22.4316112343517 -3.43161123435168 16 23 22.7935063459576 0.206493654042417 17 25 23.7703627241227 1.22963727587727 18 22 21.9316376495145 0.0683623504855215 19 26 23.5846611998534 2.41533880014656 20 29 23.6060930196684 5.39390698033157 21 32 25.0407953432596 6.95920465674041 22 25 22.2441924470041 2.75580755299586 23 28 26.3173553384188 1.68264466158119 24 25 23.6044936764052 1.39550632359481 25 25 22.8312781012892 2.16872189871082 26 18 21.7760006151216 -3.77600061512159 27 25 24.8280182030008 0.171981796999167 28 25 22.5256039357486 2.47439606425143 29 20 24.0023423110419 -4.00234231104186 30 15 16.5489407322832 -1.54894073228325 31 24 25.2136967791899 -1.21369677918994 32 26 24.2269251092524 1.77307489074756 33 14 16.4988057281025 -2.49880572810254 34 24 23.6882076241194 0.311792375880633 35 25 22.2901142469710 2.70988575302905 36 20 24.8228294406094 -4.82282944060945 37 21 21.3379147072532 -0.337914707253222 38 27 27.3806373577635 -0.380637357763492 39 23 24.8875313004724 -1.88753130047238 40 25 25.9236847186639 -0.923684718663894 41 20 22.3048028084823 -2.30480280848232 42 22 22.7351814508955 -0.735181450895489 43 25 24.2033849886421 0.79661501135793 44 25 23.4888364236610 1.51116357633895 45 17 23.6354773260822 -6.63547732608217 46 25 24.2370530452567 0.762946954743253 47 26 22.6333412788056 3.36665872119444 48 27 24.4833567931915 2.51664320680848 49 19 20.034027614319 -1.03402761431901 50 22 22.5712761469369 -0.571276146936908 51 32 28.9933452561865 3.00665474381355 52 21 24.7417584656952 -3.74175846569519 53 18 21.5563365981081 -3.55633659810815 54 23 23.1025501030034 -0.102550103003389 55 20 21.8295927359971 -1.82959273599708 56 21 22.6142313584396 -1.61423135843961 57 17 19.2372963735278 -2.23729637352785 58 18 20.3155783697159 -2.31557836971592 59 19 20.9172996003486 -1.91729960034863 60 22 22.4071052135827 -0.407105213582673 61 14 19.2545777542679 -5.25457775426786 62 18 26.5470290665061 -8.54702906650608 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 3.16280027280852 68 17 17.5001681270367 -0.500168127036698 69 25 20.4870454276716 4.51295457232843 70 20 20.3710266060241 -0.371026606024125 71 22 21.2232967322472 0.776703267752819 72 24 22.6600076469483 1.3399923530517 73 21 23.211232304497 -2.211232304497 74 26 25.2122420087995 0.787757991200532 75 24 20.7705608654128 3.22943913458719 76 16 20.5381252902889 -4.5381252902889 77 18 20.8620827426893 -2.86208274268930 78 19 19.5746801581445 -0.57468015814446 79 21 17.4860810651882 3.51391893481176 80 22 18.6764148543751 3.32358514562488 81 23 19.6406185486665 3.35938145133348 82 29 24.9251318457098 4.0748681542902 83 21 20.3323884410474 0.667611558952558 84 23 22.4791527699975 0.520847230002501 85 27 23.0933466330199 3.90665336698006 86 25 25.665739034365 -0.665739034365009 87 21 21.1025899837641 -0.102589983764136 88 10 17.5450137047189 -7.5450137047189 89 20 22.7748382661863 -2.77483826618630 90 26 22.4024263631451 3.59757363685486 91 24 24.1709732057960 -0.170973205796031 92 29 32.039524903068 -3.03952490306797 93 19 19.637559433908 -0.63755943390798 94 24 22.8678535163986 1.13214648360138 95 19 21.1918508495598 -2.19185084955978 96 22 22.2332378774937 -0.233237877493685 97 17 24.1770748831824 -7.17707488318235 98 24 23.1891525756979 0.810847424302147 99 19 20.1213719624976 -1.12137196249761 100 19 22.2397545862169 -3.23975458621690 101 23 19.0119528982715 3.98804710172846 102 27 23.9996984663534 3.00030153364658 103 14 15.0272740449086 -1.02727404490865 104 22 23.0668347665730 -1.06683476657305 105 21 22.480071673226 -1.480071673226 106 18 23.6785415443543 -5.67854154435434 107 20 23.5712368913594 -3.57123689135941 108 19 23.1012937277056 -4.10129372770564 109 24 22.9231870073325 1.07681299266752 110 25 26.0974028906659 -1.09740289066594 111 29 24.5702919430365 4.42970805696352 112 28 25.0080646376826 2.99193536231744 113 17 15.8795947450843 1.12040525491573 114 29 22.4470781605261 6.55292183947392 115 26 26.9014120632511 -0.901412063251076 116 14 17.8390211128729 -3.83902111287285 117 26 21.6947080858076 4.30529191419237 118 20 20.1628931194827 -0.162893119482684 119 32 24.7378779732098 7.2621220267902 120 23 21.2860663376028 1.71393366239721 121 21 21.9949450332778 -0.994945033277758 122 30 24.6954543998494 5.3045456001506 123 24 20.8889578555672 3.11104214443276 124 22 22.6345480739457 -0.634548073945696 125 24 22.2611111441645 1.73888885583554 126 24 22.4276344752233 1.57236552477667 127 24 20.2771256019659 3.72287439803414 128 19 18.0492519630842 0.95074803691578 129 31 27.8184536659721 3.18154633402794 130 22 26.8821117405067 -4.88211174050666 131 27 20.7453100963267 6.25468990367332 132 19 17.5094673012092 1.4905326987908 133 21 18.4446855636216 2.55531443637838 134 23 23.6545548145521 -0.654554814552138 135 19 20.6273326694800 -1.62733266948002 136 19 22.8328678522258 -3.83286785222581 137 20 22.0710595177597 -2.07105951775965 138 23 21.3932453453270 1.60675465467295 139 17 20.2204281949549 -3.22042819495492 140 17 22.5158548399329 -5.51585483993291 141 17 19.6490080600547 -2.64900806005468 142 21 24.0813306672545 -3.08133066725446 143 21 23.9449545369319 -2.94495453693186 144 18 20.6650625429321 -2.66506254293214 145 19 20.1098547493404 -1.10985474934043 146 20 24.2900101615384 -4.29001016153838 147 15 17.6814817408554 -2.68148174085542 148 24 21.5555549121971 2.44444508780293 149 20 17.9399506496497 2.06004935035033 150 22 22.2540686796497 -0.254068679649658 151 13 15.6183134640767 -2.61831346407671 152 19 17.8858937998625 1.11410620013753 153 21 21.2774151677469 -0.277415167746945 154 23 22.4836968759943 0.516303124005703 155 16 22.0445536366044 -6.04455363660443 156 26 22.0728915894783 3.92710841052169 157 21 22.1738873024122 -1.17388730241223 158 21 20.1046855891451 0.895314410854858 159 24 23.1882045116100 0.811795488390044 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 15 0.991089928435447 0.0178201431291054 0.00891007156455272 16 0.978993076153767 0.0420138476924658 0.0210069238462329 17 0.958232340501966 0.0835353189960684 0.0417676594980342 18 0.928479973760277 0.143040052479447 0.0715200262397233 19 0.921914894597964 0.156170210804072 0.0780851054020359 20 0.940872745606795 0.118254508786410 0.0591272543932048 21 0.967651616648574 0.0646967667028513 0.0323483833514256 22 0.954107122819172 0.0917857543616566 0.0458928771808283 23 0.931532985387778 0.136934029224444 0.0684670146122219 24 0.900828688693332 0.198342622613336 0.0991713113066681 25 0.866627613273007 0.266744773453986 0.133372386726993 26 0.864506687232454 0.270986625535092 0.135493312767546 27 0.821665547592746 0.356668904814507 0.178334452407254 28 0.781784167264594 0.436431665470812 0.218215832735406 29 0.806956764426896 0.386086471146209 0.193043235573104 30 0.764594033575445 0.470811932849111 0.235405966424555 31 0.715003696060086 0.569992607879828 0.284996303939914 32 0.665223359444431 0.669553281111138 0.334776640555569 33 0.614292727158253 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0.898285557316562 0.203428885366876 0.101714442683438 69 0.910006700788321 0.179986598423358 0.089993299211679 70 0.889099009056177 0.221801981887647 0.110900990943823 71 0.868656798231748 0.262686403536505 0.131343201768252 72 0.845843415731827 0.308313168536345 0.154156584268173 73 0.824269337496932 0.351461325006136 0.175730662503068 74 0.796757597733303 0.406484804533393 0.203242402266697 75 0.789833411238327 0.420333177523347 0.210166588761673 76 0.802410046824845 0.395179906350311 0.197589953175155 77 0.788384621167523 0.423230757664954 0.211615378832477 78 0.753383931789522 0.493232136420956 0.246616068210478 79 0.753901481978203 0.492197036043595 0.246098518021797 80 0.738061747195006 0.523876505609989 0.261938252804994 81 0.724422180204356 0.551155639591288 0.275577819795644 82 0.74486933914194 0.510261321716119 0.255130660858060 83 0.772488421496387 0.455023157007226 0.227511578503613 84 0.738852375180549 0.522295249638902 0.261147624819451 85 0.747706606416284 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103 0.420904224779436 0.841808449558873 0.579095775220564 104 0.389479119291342 0.778958238582683 0.610520880708658 105 0.362525352015143 0.725050704030287 0.637474647984857 106 0.43332425014131 0.86664850028262 0.56667574985869 107 0.428895674135223 0.857791348270445 0.571104325864777 108 0.431411429331738 0.862822858663477 0.568588570668262 109 0.390370071107651 0.780740142215302 0.609629928892349 110 0.352740732054551 0.705481464109103 0.647259267945449 111 0.368190926655983 0.736381853311967 0.631809073344017 112 0.366324938492512 0.732649876985024 0.633675061507488 113 0.325703879125679 0.651407758251358 0.674296120874321 114 0.448544038377967 0.897088076755933 0.551455961622033 115 0.394796671119665 0.789593342239329 0.605203328880335 116 0.406195153091628 0.812390306183256 0.593804846908372 117 0.428563770795834 0.857127541591668 0.571436229204166 118 0.372116719784851 0.744233439569702 0.627883280215149 119 0.621211216646473 0.757577566707054 0.378788783353527 120 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0.76447107979931 0.617764460100345 138 0.326107184438964 0.652214368877927 0.673892815561036 139 0.272257765770568 0.544515531541137 0.727742234229432 140 0.30833798361651 0.61667596723302 0.69166201638349 141 0.225118962606645 0.450237925213291 0.774881037393355 142 0.158919981688500 0.317839963376999 0.8410800183115 143 0.116192285727414 0.232384571454828 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/24/t1290611289guc3peb0batgqy2/10kt0e1290611376.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290611289guc3peb0batgqy2/10kt0e1290611376.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290611289guc3peb0batgqy2/1vs3k1290611376.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290611289guc3peb0batgqy2/1vs3k1290611376.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290611289guc3peb0batgqy2/2o1251290611376.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290611289guc3peb0batgqy2/2o1251290611376.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290611289guc3peb0batgqy2/3o1251290611376.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290611289guc3peb0batgqy2/3o1251290611376.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290611289guc3peb0batgqy2/4o1251290611376.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290611289guc3peb0batgqy2/4o1251290611376.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290611289guc3peb0batgqy2/5ysjq1290611376.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290611289guc3peb0batgqy2/5ysjq1290611376.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290611289guc3peb0batgqy2/6ysjq1290611376.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290611289guc3peb0batgqy2/6ysjq1290611376.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290611289guc3peb0batgqy2/7r2jb1290611376.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290611289guc3peb0batgqy2/7r2jb1290611376.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290611289guc3peb0batgqy2/8r2jb1290611376.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290611289guc3peb0batgqy2/8r2jb1290611376.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290611289guc3peb0batgqy2/9kt0e1290611376.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/24/t1290611289guc3peb0batgqy2/9kt0e1290611376.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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