Goldfeld-Quandt

R Software Module: rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Sun, 20 Jan 2008 06:56:54 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Jan/20/t1200837348nrvfakk5mq2iczb.htm/, Retrieved Sun, 20 Jan 2008 14:56:00 +0100

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

106 87 1 65.3 170 2.2 70 1 65.73 165 62.3 75 1 69.44 168 14.7 79 1 73.74 170 5 64.5 1 74.31 157 74.4 75 0 70.53 146 66.1 70 0 69.42 149 22 67 1 69.77 159 3.4 52 0 65.47 151 0.3 67.2 1 66.2 174 53.2 47 0 70.46 156 0 46.4 0 74.44 151.5 57.2 76 0 69.28 146 9.2 71.6 1 67.67 157 15.9 63.8 1 67.22 171.5 17.6 48.2 1 64.85 150 21 64.5 1 71.35 170 7.6 75.9 1 72.28 164.5 71.6 80 1 71.87 163 12.9 56 1 67.34 162.5 10.5 75.5 0 73.5 161 25.7 77 1 64.91 166.5 26.8 88 0 68.13 160 7.3 48 0 72.5 147 17.1 73 1 72.36 162.5 27.3 72 1 70.59 161 16.5 64 1 74.76 163.5 5.4 76 0 65.63 161 5.6 67.4 1 67.04 172.5 36.5 73.7 1 66.72 169.5 1.1 59.2 0 65.8 158 3.9 53 0 72.44 153.5 34.2 41.9 1 71.83 165.5 40.3 65.5 1 72.67 153.5 15.6 63 1 69.56 157.5 15.5 54 0 67 145.5 52.9 77.7 0 68.86 156 1.6 47.6 0 71.25 163 14.2 53.1 1 69.88 159 7.5 55.5 1 67.18 167 2 64 1 67.47 157.5 71.4 75.6 1 73.2 156 3.2 57 0 69.6 156.5 20 63 0 71.24 148.5 2.8 59.5 1 73.83 162.5 15.3 84.5 1 66.07 164 8 59.9 0 70.68 152 36.6 60 1 74.01 157.5 3.8 64 0 68.53 148 25.5 54 0 66.72 145.5 3.2 53.8 0 72.69 154.5 33.1 84 1 67.46 166.5 42 63.2 0 73.81 157 16.2 54.3 1 72.96 150 0 60 0 71.65 152 22.7 68 1 72.79 171 36.4 74 1 73.83 165.5 69 74 1 66.74 165 11.2 68.5 1 65.62 168.5 12.5 76 0 66.18 154 51.7 83 0 67.78 156.5 3.6 62.5 0 68.84 152 22.2 57 1 65.27 164.5 39.2 85 1 72.84 161 27.9 50 1 75.36 162 58.8 53 1 76.88 169 1 57 0 76.51 150 4.7 46 1 80.63 146 25.6 65.4 1 75.27 165 5.3 71.4 1 81.19 165.5 38.7 41 1 81.3 164 31.6 66 1 77.77 163 19.3 69.5 1 75.51 167.5 26.5 59 1 78.64 166 12.8 80 1 80.68 167.5 18.3 72 1 77.4 162 13.2 73 0 80.71 165 36 66.4 0 83.16 145 34.1 37 0 87.99 139 71.5 70 1 72.21 164 43.3 75 1 70.24 167 47.7 54 1 66.06 163 74.9 76.2 1 68.67 162.5 0.9 74.9 1 68.77 159.5 35.9 98 1 68.07 169 45.8 86.5 0 67.33 152.5 54.2 72.8 1 69.47 165 34 65 1 70.81 166 7.9 50 1 73.17 163 54.5 81 1 71.28 167.5 8.2 52 0 69.47 157.5 49.3 68 1 65.31 160 46.9 58.5 1 70.23 162 16.8 65.5 1 73.23 164.5 2.8 62.5 0 68.67 150 60.9 64 1 72.66 167 5.6 55.7 0 74.79 155 6.6 84 1 73.04 173.5 22.9 63.7 1 69.95 173 51.1 65 0 67.51 156 23.3 87.5 0 67.5 149.5 11.5 79 1 71.32 167 79.1 58.5 0 71.23 146 53.6 75 1 67.49 166 1.5 52.5 0 68.62 151.5 40.4 57.5 1 72.53 164 25.4 70 1 66.67 160 6.7 72 1 66.19 152.5 76 88 1 78.4 160 0.6 58 1 75.67 163 43.4 73 1 76.07 168 13 56 1 82.88 165.5 27.8 49 0 77.14 147 6.5 54.7 0 77.31 158 7.1 67 1 76.58 168 6 47 0 82.86 154.5 6.5 47 0 76.64 147

Text written by user:

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 compuational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation y[t] = + 61.5198389564683 + 0.716989169203628weight[t] + 10.0572486368982sex[t] + 0.119109062022990age[t] -0.608987531164764`height `[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) 61.5198389564683 66.972904 0.9186 0.36029 0.180145 weight 0.716989169203628 0.186978 3.8346 0.000208 0.000104 sex 10.0572486368982 5.882887 1.7096 0.090113 0.045057 age 0.119109062022990 0.445398 0.2674 0.789637 0.394818 `height ` -0.608987531164764 0.380946 -1.5986 0.112723 0.056362 Multiple Linear Regression - Regression Statistics Multiple R 0.378964915955848 R-squared 0.143614407525423 Adjusted R-squared 0.113029207794188 F-TEST (value) 4.69555238440237 F-TEST (DF numerator) 4 F-TEST (DF denominator) 112 p-value 0.00153379659481434 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 21.6102713221872 Sum Squared Residuals 52304.4285812773 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 106 38.2050867661735 67.7949132338265 2 2.2 29.1124254422055 -26.9124254422055 3 62.3 31.3123033148346 30.9876966851654 4 14.7 33.4744538960185 -18.7744538960185 5 5 31.0628410130609 -26.0628410130609 6 74.4 34.7826092411663 39.6173907588337 7 66.1 29.2384897428083 36.8615102571917 8 22 31.0965837321561 -9.09658373215608 9 3.4 14.6442288398227 -11.2442288398227 10 0.3 21.6799492471033 -21.3799492471033 11 53.2 8.60869955747544 44.5913004425246 12 0 11.3930040130462 -11.3930040130462 13 57.2 35.3507120828412 21.8492879171588 14 9.2 35.362579942574 -26.162579942574 15 15.9 20.8861461429863 -4.98614614298629 16 17.6 22.5120585464576 -4.91205854645763 17 21 22.7934402843309 -1.79344028433093 18 7.6 34.4273196623399 -26.8273196623399 19 71.6 38.2316218373925 33.3683781626075 20 12.9 20.7888114911236 -7.88881149112362 21 10.5 26.3600447725049 -15.8600447725049 22 25.7 33.1201988990249 -7.4201988990249 23 26.8 35.2917812556516 -8.49178125565158 24 7.3 15.0495589936888 -7.74955899368884 25 17.1 33.5755548589407 -16.4755548589407 26 27.3 33.5612239467035 -6.26122394670354 27 16.5 26.7995265537985 -10.2995265537985 28 5.4 25.7811510389858 -20.3811510389858 29 5.6 22.8368799897905 -17.2368799897905 30 36.5 29.1427594494202 7.35724055057975 31 1.1 15.5829441304030 -14.4829441304030 32 3.9 14.6689393434146 -10.7689393434146 33 34.2 9.3871013003414 24.8128986996586 34 40.3 33.7159476796235 6.58405232037649 35 15.6 29.1170954490639 -13.5170954490639 36 15.5 19.6098754645313 -4.10987546453131 37 52.9 30.4296925527900 22.4703074472100 38 1.6 4.87007649984242 -3.27007649984242 39 14.2 21.1435362770482 -6.94353627704817 40 7.5 17.6708155663567 -10.1708155663567 41 2 29.5851466786395 -27.5851466786395 42 71.4 39.4981972635404 31.9018027364596 43 3.2 15.3716636905896 -12.1716636905896 44 20 24.7408378168471 -4.74083781684715 45 2.8 24.0712913958655 -21.2712913958655 46 15.3 40.1582530079107 -24.8582530079107 47 8 20.3200139585064 -12.3200139585064 48 36.6 27.4961632674553 9.1038367325447 49 3.8 25.4395351935509 -21.6395351935509 50 25.5 19.5765249271649 5.92347507283513 51 3.2 14.6633204131185 -11.4633204131185 52 33.1 38.4428511916089 -5.34285119160892 53 42 20.0139519251865 21.9860480748135 54 16.2 27.8516669716062 -11.6516669716062 55 0 20.5072486655890 -20.5072486655890 56 22.7 24.8654318946920 -2.16543189469197 57 36.4 32.6406717558239 3.75932824417615 58 69 32.1006822716632 36.8993177283368 59 11.2 25.8923833325009 -14.6923833325009 60 12.5 30.1095737412518 -17.6095737412518 61 51.7 33.7966035970021 17.9033964029979 62 3.6 21.9650251243135 -18.3650251243135 63 22.2 20.0412698396101 2.15873016038987 64 39.2 43.1500785359024 -3.95007853590244 65 27.9 17.7466249189086 10.1533750810914 66 58.8 15.8157254826411 42.9842745173589 67 1 20.1531262617394 -19.1531262617394 68 4.7 25.2501734975915 -20.5501734975915 69 25.6 26.9505757155681 -1.35057571556814 70 5.3 31.6531426123836 -26.3531426123836 71 38.7 10.783255162163 27.916744837837 72 31.6 28.8965169344773 2.70348306552269 73 19.3 28.3963486562766 -9.09634865627662 74 26.5 22.1542550405176 4.34574495948237 75 12.8 36.5405287835736 -23.7405287835736 76 18.3 33.7633691279153 -15.4633691279153 77 13.2 22.9903980620225 -9.79039806202254 78 36 30.7298373705302 5.27016262946979 79 34.1 13.8795777525032 20.2204222474968 80 71.5 30.4932396952792 41.0067603047208 81 43.3 32.0165780956178 11.2834219043822 82 47.7 18.8978797877446 28.8021202122554 83 74.9 35.4304077615275 39.4695922384725 84 0.9 36.3371953412594 -35.4371953412594 85 35.9 47.0308872603818 -11.1308872603818 86 45.8 38.6884167359635 7.11158326403653 87 54.2 31.5654630079416 22.6345369920584 88 34 25.5235661000994 8.47643389990062 89 7.9 16.8767885419135 -8.97678854191351 90 54.5 36.1378927697611 18.3621072302389 91 8.2 11.1622461353437 -2.96224613534367 92 49.3 30.6733589535724 18.6266410464276 93 46.9 23.2300033689615 23.6699966310385 94 16.8 27.0837859115440 -10.2837859115440 95 2.8 23.1627516460991 -20.3627516460991 96 60.9 24.4179411644735 36.4820588355265 97 5.6 15.9712350992713 -10.3712350992713 98 6.6 34.8445670395439 -28.2445670395439 99 22.9 20.2261336686415 2.67386633135845 100 51.1 21.1631328701729 29.9368671298271 101 23.3 41.2526170392053 -17.9526170392053 102 11.5 35.0131725594171 -23.5131725594171 103 79.1 23.0356642927225 56.0643357072775 104 53.6 32.2980157062193 21.3019842937807 105 1.5 15.0734232042145 -13.5734232042145 106 40.4 21.5689899800812 18.8310100199188 107 25.4 32.2693256163309 -6.86932561633093 108 6.7 38.2135380887029 -31.5135380887029 109 76 46.5722799595259 29.4277200404741 110 0.6 22.9104745506 -22.3104745506 111 43.4 30.6680180576398 12.7319819423602 112 13 20.8128037214666 -7.8128037214666 113 27.8 16.3192142106791 11.4807857893209 114 6.5 13.7274381728713 -7.22743817287133 115 7.1 26.4268286640498 -19.3268286640498 116 6 10.9991332233077 -4.99913322330766 117 6.5 14.8256813412604 -8.32568134126039 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.862234991999203 0.275530016001594 0.137765008000797 9 0.787339011013664 0.425321977972673 0.212660988986336 10 0.681777542658884 0.636444914682232 0.318222457341116 11 0.97863231469106 0.0427353706178814 0.0213676853089407 12 0.964028032281573 0.0719439354368548 0.0359719677184274 13 0.961533375134236 0.0769332497315289 0.0384666248657645 14 0.944629330040398 0.110741339919205 0.0553706699596024 15 0.914636258294815 0.170727483410371 0.0853637417051854 16 0.954531358088372 0.090937283823257 0.0454686419116285 17 0.934332852917823 0.131334294164353 0.0656671470821766 18 0.934394965664284 0.131210068671433 0.0656050343357163 19 0.957852511871446 0.0842949762571083 0.0421474881285542 20 0.941440833627518 0.117118332744964 0.0585591663724820 21 0.970232327367022 0.0595353452659569 0.0297676726329784 22 0.964854705650323 0.0702905886993547 0.0351452943496773 23 0.97910690044765 0.0417861991046992 0.0208930995523496 24 0.96977057229773 0.060458855404539 0.0302294277022695 25 0.960227330352587 0.0795453392948269 0.0397726696474134 26 0.944119658450046 0.111760683099907 0.0558803415499537 27 0.926081843449017 0.147836313101966 0.0739181565509829 28 0.946234076168542 0.107531847662915 0.0537659238314577 29 0.933720483366719 0.132559033266562 0.0662795166332812 30 0.914959570968571 0.170080858062858 0.0850404290314289 31 0.902619072155824 0.194761855688351 0.0973809278441757 32 0.876933656231753 0.246132687536493 0.123066343768247 33 0.921028383965644 0.157943232068713 0.0789716160343564 34 0.898737472468665 0.202525055062671 0.101262527531335 35 0.879344197705143 0.241311604589714 0.120655802294857 36 0.84981824718461 0.300363505630778 0.150181752815389 37 0.841353099067347 0.317293801865307 0.158646900932653 38 0.804228218527901 0.391543562944198 0.195771781472099 39 0.76511024168763 0.469779516624741 0.234889758312371 40 0.727880904223398 0.544238191553204 0.272119095776602 41 0.75216737000199 0.495665259996021 0.247832629998011 42 0.797361067749587 0.405277864500825 0.202638932250412 43 0.769783661145604 0.460432677708792 0.230216338854396 44 0.730168244327586 0.539663511344829 0.269831755672414 45 0.721970889695019 0.556058220609962 0.278029110304981 46 0.74315558430675 0.5136888313865 0.25684441569325 47 0.714028463788551 0.571943072422897 0.285971536211448 48 0.677003604021066 0.645992791957868 0.322996395978934 49 0.683158648615371 0.633682702769257 0.316841351384629 50 0.635606991236587 0.728786017526826 0.364393008763413 51 0.598857896909814 0.802284206180372 0.401142103090186 52 0.548434208550299 0.903131582899402 0.451565791449701 53 0.54592787148397 0.90814425703206 0.45407212851603 54 0.509526690288265 0.98094661942347 0.490473309711735 55 0.509073569088814 0.981852861822373 0.490926430911187 56 0.456711601614961 0.913423203229922 0.543288398385039 57 0.404202099656503 0.808404199313006 0.595797900343497 58 0.502937305942402 0.994125388115197 0.497062694057598 59 0.481901412620135 0.96380282524027 0.518098587379865 60 0.472231722970652 0.944463445941304 0.527768277029348 61 0.452583869361459 0.905167738722918 0.547416130638541 62 0.444116729110972 0.888233458221944 0.555883270889028 63 0.405834374564316 0.811668749128633 0.594165625435684 64 0.357242073963087 0.714484147926175 0.642757926036913 65 0.321401323050207 0.642802646100414 0.678598676949793 66 0.453236537425105 0.90647307485021 0.546763462574895 67 0.449530987205868 0.899061974411736 0.550469012794132 68 0.467123377957582 0.934246755915164 0.532876622042418 69 0.412960555473328 0.825921110946655 0.587039444526672 70 0.43381285819488 0.86762571638976 0.56618714180512 71 0.455053113949796 0.910106227899593 0.544946886050204 72 0.399722231497842 0.799444462995684 0.600277768502158 73 0.355810317998603 0.711620635997206 0.644189682001397 74 0.305173462751410 0.610346925502819 0.69482653724859 75 0.305876052906213 0.611752105812427 0.694123947093787 76 0.285531300461259 0.571062600922518 0.714468699538741 77 0.242551221208485 0.485102442416971 0.757448778791515 78 0.20177207137445 0.4035441427489 0.79822792862555 79 0.189530574513200 0.379061149026399 0.8104694254868 80 0.284334629885041 0.568669259770082 0.715665370114959 81 0.245080222036636 0.490160444073272 0.754919777963364 82 0.254017596188038 0.508035192376077 0.745982403811962 83 0.355386551817094 0.710773103634189 0.644613448182906 84 0.461144891297971 0.922289782595942 0.538855108702029 85 0.416167415194646 0.832334830389292 0.583832584805354 86 0.359470686284452 0.718941372568904 0.640529313715548 87 0.350826060202194 0.701652120404388 0.649173939797806 88 0.298444014324857 0.596888028649714 0.701555985675143 89 0.258074661053381 0.516149322106761 0.74192533894662 90 0.243409043976259 0.486818087952518 0.756590956023741 91 0.195269919165092 0.390539838330184 0.804730080834908 92 0.171207419584105 0.342414839168210 0.828792580415895 93 0.169035975656048 0.338071951312096 0.830964024343952 94 0.134304417155074 0.268608834310149 0.865695582844926 95 0.130779032790659 0.261558065581318 0.86922096720934 96 0.214013704996288 0.428027409992576 0.785986295003712 97 0.177907273699678 0.355814547399357 0.822092726300322 98 0.191822197137384 0.383644394274768 0.808177802862616 99 0.144354729940613 0.288709459881227 0.855645270059387 100 0.168767896145201 0.337535792290402 0.831232103854799 101 0.230620692440367 0.461241384880734 0.769379307559633 102 0.282832905937496 0.565665811874992 0.717167094062504 103 0.701922077263387 0.596155845473225 0.298077922736613 104 0.711472647015294 0.577054705969413 0.288527352984706 105 0.608675273842969 0.782649452314062 0.391324726157031 106 0.89416138982004 0.211677220359919 0.105838610179960 107 0.902416226476463 0.195167547047074 0.0975837735235371 108 0.897695251594504 0.204609496810991 0.102304748405496 109 0.924899717875219 0.150200564249562 0.0751002821247811 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 0.0196078431372549 OK 10% type I error level 10 0.0980392156862745 OK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/20/t1200837348nrvfakk5mq2iczb/10blg31200837407.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/20/t1200837348nrvfakk5mq2iczb/10blg31200837407.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/20/t1200837348nrvfakk5mq2iczb/1r58v1200837407.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/20/t1200837348nrvfakk5mq2iczb/1r58v1200837407.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/20/t1200837348nrvfakk5mq2iczb/2xchl1200837407.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/20/t1200837348nrvfakk5mq2iczb/2xchl1200837407.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/20/t1200837348nrvfakk5mq2iczb/3vc5l1200837407.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/20/t1200837348nrvfakk5mq2iczb/3vc5l1200837407.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/20/t1200837348nrvfakk5mq2iczb/484pt1200837407.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/20/t1200837348nrvfakk5mq2iczb/484pt1200837407.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/20/t1200837348nrvfakk5mq2iczb/5wldi1200837407.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/20/t1200837348nrvfakk5mq2iczb/5wldi1200837407.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/20/t1200837348nrvfakk5mq2iczb/6f8hu1200837407.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/20/t1200837348nrvfakk5mq2iczb/6f8hu1200837407.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/20/t1200837348nrvfakk5mq2iczb/7wm4d1200837407.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/20/t1200837348nrvfakk5mq2iczb/7wm4d1200837407.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/20/t1200837348nrvfakk5mq2iczb/8nq3e1200837407.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/20/t1200837348nrvfakk5mq2iczb/8nq3e1200837407.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/20/t1200837348nrvfakk5mq2iczb/9eh0a1200837407.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/20/t1200837348nrvfakk5mq2iczb/9eh0a1200837407.ps (open in new window)

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') }

Copyright

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

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by