Paper interactiemodellen

*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, 01 Dec 2010 14:29:23 +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/01/t1291213841gv43h2tf61myt86.htm/, Retrieved Wed, 01 Dec 2010 15:30:51 +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/01/t1291213841gv43h2tf61myt86.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 «

66 4964 4818 4488 5 73 68 54 3132 3132 2916 12 58 54 82 2788 5576 3362 11 68 41 61 3038 3782 2989 6 62 49 65 3185 4225 3185 12 65 49 77 5832 6237 5544 11 81 72 66 5694 4818 5148 12 73 78 66 3712 4224 3828 7 64 58 66 3944 4488 3828 8 68 58 48 1173 2448 1104 13 51 23 57 2652 3876 2223 12 68 39 80 3843 4880 5040 13 61 63 60 3174 4140 2760 12 69 46 70 4234 5110 4060 12 73 58 85 2379 5185 3315 11 61 39 59 2728 3658 2596 12 62 44 72 3087 4536 3528 12 63 49 70 3933 4830 3990 12 69 57 74 3572 3478 5624 11 47 76 70 4158 4620 4410 13 66 63 51 1044 2958 918 9 58 18 70 2520 4410 2800 11 63 40 71 4071 4899 4189 11 69 59 72 3658 4248 4464 11 59 62 50 4130 2950 3500 9 59 70 69 4095 4347 4485 11 63 65 73 3640 4745 4088 12 65 56 66 2925 4290 2970 12 65 45 73 4047 5183 4161 10 71 57 58 3000 3480 2900 12 60 50 78 3240 6318 3120 12 81 40 83 3886 5561 4814 12 67 58 76 3234 5016 3724 9 66 49 77 3038 4774 3773 9 62 49 79 1701 4977 2133 12 63 27 71 3723 5183 3621 14 73 51 79 4125 4345 5925 12 55 7 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 10 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Vrienden_vinden[t] = + 14.8343830437332 -0.189388395423180Groepsgevoel[t] -0.00274311701082415`InteractieNV-U`[t] + 0.00134424111559316InteractieGR_NV[t] + 0.00258194445835426interacteiGR_U[t] + 0.0610534658445095NV[t] -0.0134045678273368Uitingsangst[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) 14.8343830437332 8.564361 1.7321 0.085473 0.042736 Groepsgevoel -0.189388395423180 0.135808 -1.3945 0.165381 0.08269 `InteractieNV-U` -0.00274311701082415 0.001543 -1.7777 0.077642 0.038821 InteractieGR_NV 0.00134424111559316 0.001959 0.6861 0.493823 0.246912 interacteiGR_U 0.00258194445835426 0.001078 2.3946 0.01797 0.008985 NV 0.0610534658445095 0.141942 0.4301 0.667766 0.333883 Uitingsangst -0.0134045678273368 0.099466 -0.1348 0.892992 0.446496 Multiple Linear Regression - Regression Statistics Multiple R 0.314037555070108 R-squared 0.0986195859944112 Adjusted R-squared 0.0597110789150334 F-TEST (value) 2.5346535602925 F-TEST (DF numerator) 6 F-TEST (DF denominator) 139 p-value 0.0232763320814189 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.74208597726733 Sum Squared Residuals 421.846033754616 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 5 10.3276289224843 -5.32762892248427 2 12 10.5723347838844 1.42766521611565 3 11 11.4347585188950 -0.434758518895009 4 6 10.8779443880497 -4.87794438804971 5 12 11.0018729313446 0.99812706865542 6 11 10.9321519539294 0.0678480460705732 7 12 9.89519116882307 2.10480883117693 8 7 10.8440133405327 -3.84401334053273 9 8 10.8067037119162 -2.80670371191616 10 13 11.4726544407602 1.52734555923979 11 12 11.3432968190274 0.656703180972614 12 13 11.5941830948745 1.40581690512550 13 12 11.0518298728136 0.94817012718638 14 12 10.9940426145441 1.00595738545589 15 11 10.9410133990561 0.058986600943941 16 12 10.9927202209215 1.00727977907852 17 12 11.1265386349191 0.87346136508095 18 12 11.0317879148006 0.968212085199402 19 11 12.0681197933857 -1.06811979338569 20 13 10.9531248211018 2.04687517889816 21 9 11.9580697486339 -2.95806974863385 22 11 11.1322739351021 -0.132273935102098 23 11 11.0435998204174 -0.0435998204174359 24 11 11.1713041483338 -0.171304148333796 25 9 9.70204165002264 -0.702041650022644 26 11 10.9320280648384 0.0679719351616262 27 12 11.1753167792451 0.82468322075487 28 12 11.1115708440124 0.888429155987582 29 10 11.1890439371691 -1.18904393716909 30 12 10.7790826475116 1.22091735248844 31 12 12.1321191843496 -0.132119184349553 32 12 11.6733162634741 0.32668373652586 33 9 11.3002040994909 -2.30020409949086 34 9 11.2054617032970 -2.20546170329699 35 12 10.8886783487329 1.11132165126713 36 14 11.2648749706642 2.73512502933579 37 12 12.0486887330513 -0.0486887330513444 38 11 10.5102800946623 0.489719905337683 39 9 11.1740872641105 -2.17408726411047 40 11 11.0829104882820 -0.0829104882819551 41 7 9.16685706622441 -2.16685706622441 42 15 11.1602538938449 3.83974610615507 43 11 10.8347221778060 0.165277822194036 44 12 11.7116577131004 0.288342286899648 45 12 10.6803303767613 1.31966962323866 46 9 11.79406465647 -2.79406465646999 47 12 10.4675967531471 1.53240324685289 48 11 11.2281102964106 -0.228110296410598 49 11 10.3909294408144 0.609070559185607 50 8 11.2775751274862 -3.27757512748618 51 7 10.2452858339887 -3.24528583398868 52 12 11.6396273984466 0.360372601553353 53 8 11.7482536549045 -3.74825365490449 54 10 10.3511807981519 -0.351180798151857 55 12 10.2698234429971 1.73017655700291 56 15 10.9304340220104 4.06956597798959 57 12 11.6628281057235 0.337171894276533 58 12 11.1473738120381 0.852626187961942 59 12 10.7652935486195 1.23470645138047 60 12 10.4275355797878 1.57246442021221 61 8 9.25027225083847 -1.25027225083847 62 10 11.2410825043049 -1.24108250430493 63 14 12.2848964064311 1.71510359356892 64 10 10.9764892092198 -0.976489209219842 65 12 10.8440133134716 1.15598668652836 66 14 10.8817322350482 3.11826776495179 67 6 11.1908123377809 -5.19081233778092 68 11 10.7248997130671 0.275100286932905 69 10 11.0883118320864 -1.08831183208639 70 14 12.5973310928326 1.40266890716737 71 12 11.0573070519540 0.94269294804597 72 13 11.1070287269076 1.89297127309244 73 11 10.7743211413879 0.225678858612129 74 11 10.9660887608403 0.033911239159701 75 12 11.2372156707279 0.762784329272054 76 13 10.8218288220564 2.17817117794361 77 12 10.2927813603116 1.70721863968845 78 8 9.98697403412809 -1.98697403412809 79 12 11.2918670212511 0.708132978748919 80 11 11.2877616814385 -0.287761681438509 81 10 11.0224294476832 -1.02242944768317 82 12 11.0230852988741 0.976914701125868 83 11 11.0126957836665 -0.0126957836665200 84 12 11.0844001355874 0.915599864412637 85 12 11.0496464261716 0.950353573828377 86 10 10.4794482524057 -0.479448252405747 87 12 11.4741811296339 0.525818870366149 88 12 10.8944827124484 1.10551728755163 89 11 11.2789027368865 -0.278902736886497 90 10 10.9475864914106 -0.947586491410563 91 12 11.3649998171774 0.635000182822633 92 11 11.0381966513290 -0.0381966513289806 93 12 10.4262858103979 1.57371418960209 94 12 9.62262107055148 2.37737892944852 95 10 9.59371678505544 0.406283214944556 96 11 11.0922833214020 -0.092283321401954 97 10 10.9914064497695 -0.991406449769524 98 11 10.9754574958867 0.0245425041133088 99 11 10.528039833022 0.471960166977996 100 12 11.2674854492816 0.732514550718434 101 11 10.9195604086767 0.0804395913233085 102 11 10.7196842586905 0.28031574130952 103 7 8.78169720837158 -1.78169720837158 104 12 10.6302781138762 1.36972188612377 105 8 10.5637655017874 -2.56376550178743 106 10 11.0594311674627 -1.05943116746267 107 12 11.2947615363055 0.705238463694473 108 11 11.0815243483923 -0.081524348392288 109 13 11.1596577379906 1.84034226200939 110 9 11.4640022119820 -2.46400221198205 111 11 11.2934875112473 -0.293487511247310 112 13 11.1867289822827 1.81327101771726 113 8 10.7893845921053 -2.78938459210532 114 12 11.1396475031692 0.860352496830779 115 11 10.5424325384555 0.457567461544546 116 11 10.657064832192 0.342935167808007 117 12 10.8531102642966 1.14688973570342 118 13 11.3373734686506 1.66262653134945 119 11 11.2896164245962 -0.28961642459616 120 10 10.7733243523953 -0.773324352395322 121 10 10.8915188872227 -0.891518887222733 122 10 10.9026492235152 -0.902649223515228 123 12 10.8622026281523 1.13779737184766 124 12 11.0774765322399 0.92252346776008 125 13 10.9482774928625 2.05172250713749 126 11 11.0141201408704 -0.0141201408704338 127 11 9.9703982977929 1.02960170220710 128 12 11.0951823361871 0.904817663812928 129 9 10.7254460200678 -1.72544602006781 130 11 11.7573290299829 -0.757329029982945 131 12 10.9344622961164 1.06553770388357 132 12 10.9509041365785 1.04909586342148 133 13 10.7686539031229 2.23134609687711 134 6 10.8385987923467 -4.83859879234671 135 11 11.9467365957407 -0.946736595740722 136 10 11.8041130887521 -1.80411308875214 137 12 11.0044154267555 0.995584573244477 138 11 11.0858670054927 -0.0858670054927345 139 12 12.1157454454488 -0.115745445448829 140 12 11.1216876562962 0.8783123437038 141 7 11.1351676694415 -4.13516766944154 142 12 11.1240060047649 0.87599399523512 143 12 12.1991888487773 -0.199188848777291 144 9 10.9928847308025 -1.99288473080253 145 12 11.2235291837483 0.776470816251698 146 12 11.2659416290712 0.734058370928845 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.998642400081436 0.00271519983712866 0.00135759991856433 11 0.9993622467455 0.00127550650900134 0.00063775325450067 12 0.999794277589984 0.000411444820032319 0.000205722410016159 13 0.99972511439493 0.00054977121013852 0.00027488560506926 14 0.999541648507345 0.000916702985309755 0.000458351492654877 15 0.999209898188707 0.00158020362258661 0.000790101811293307 16 0.998763986568516 0.00247202686296822 0.00123601343148411 17 0.997934661619076 0.00413067676184727 0.00206533838092363 18 0.996887721625454 0.00622455674909203 0.00311227837454601 19 0.994980451761196 0.0100390964776088 0.00501954823880441 20 0.99580847146764 0.00838305706472045 0.00419152853236023 21 0.998167918459836 0.00366416308032707 0.00183208154016354 22 0.99682572543108 0.00634854913783929 0.00317427456891964 23 0.994695418462819 0.0106091630743624 0.00530458153718121 24 0.991445619146953 0.0171087617060950 0.00855438085304751 25 0.98709211537239 0.0258157692552208 0.0129078846276104 26 0.980581596642826 0.0388368067143481 0.0194184033571741 27 0.973450575071226 0.0530988498575486 0.0265494249287743 28 0.966262021547937 0.0674759569041267 0.0337379784520634 29 0.958517314173874 0.0829653716522526 0.0414826858261263 30 0.952063063483437 0.0958738730331259 0.0479369365165629 31 0.934987466906821 0.130025066186358 0.0650125330931791 32 0.913361718043918 0.173276563912164 0.0866382819560818 33 0.923776191973336 0.152447616053327 0.0762238080266636 34 0.928273502697567 0.143452994604866 0.0717264973024329 35 0.915795476670436 0.168409046659128 0.084204523329564 36 0.942759622411898 0.114480755176205 0.0572403775881024 37 0.925284403894012 0.149431192211975 0.0747155961059877 38 0.905216578674534 0.189566842650932 0.0947834213254662 39 0.911065929193706 0.177868141612588 0.0889340708062938 40 0.886506227633937 0.226987544732126 0.113493772366063 41 0.883689890902806 0.232620218194388 0.116310109097194 42 0.947597353551237 0.104805292897525 0.0524026464487625 43 0.93224867095486 0.135502658090279 0.0677513290451395 44 0.916666010391017 0.166667979217966 0.083333989608983 45 0.911604964962083 0.176790070075833 0.0883950350379165 46 0.936958399469398 0.126083201061204 0.0630416005306021 47 0.933977941116989 0.132044117766022 0.066022058883011 48 0.916242050806744 0.167515898386511 0.0837579491932557 49 0.897656602655674 0.204686794688652 0.102343397344326 50 0.939499389338795 0.121001221322411 0.0605006106612053 51 0.964330018046664 0.0713399639066712 0.0356699819533356 52 0.953393556561234 0.0932128868775311 0.0466064434387656 53 0.980707881366066 0.0385842372678678 0.0192921186339339 54 0.974309679770434 0.0513806404591322 0.0256903202295661 55 0.974580648139608 0.0508387037207833 0.0254193518603916 56 0.994015555128892 0.0119688897422157 0.00598444487110784 57 0.991620194966353 0.0167596100672931 0.00837980503364655 58 0.98917677764107 0.0216464447178621 0.0108232223589311 59 0.987556249570296 0.0248875008594085 0.0124437504297042 60 0.986638613246337 0.0267227735073262 0.0133613867536631 61 0.984739417361985 0.0305211652760296 0.0152605826380148 62 0.981938744444572 0.0361225111108552 0.0180612555554276 63 0.981252307798583 0.0374953844028346 0.0187476922014173 64 0.976688604962619 0.0466227900747627 0.0233113950373813 65 0.972434951259476 0.0551300974810475 0.0275650487405237 66 0.9853016121427 0.0293967757146013 0.0146983878573007 67 0.999215153368168 0.00156969326366318 0.000784846631831591 68 0.998860781069468 0.00227843786106383 0.00113921893053192 69 0.9985572256697 0.00288554866059927 0.00144277433029963 70 0.998135352028135 0.00372929594372984 0.00186464797186492 71 0.997564194066466 0.00487161186706854 0.00243580593353427 72 0.99775648439941 0.00448703120118138 0.00224351560059069 73 0.996760458134634 0.00647908373073247 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0.425799001972729 108 0.516355150963017 0.967289698073966 0.483644849036983 109 0.525502729167294 0.948994541665412 0.474497270832706 110 0.551763963901348 0.896472072197305 0.448236036098652 111 0.499522618325279 0.999045236650557 0.500477381674721 112 0.497415146948931 0.994830293897863 0.502584853051069 113 0.598947352713265 0.80210529457347 0.401052647286735 114 0.55784017854979 0.884319642900421 0.442159821450210 115 0.495065454628753 0.990130909257505 0.504934545371247 116 0.431036183578976 0.862072367157952 0.568963816421024 117 0.388285584287296 0.776571168574592 0.611714415712704 118 0.399862842135859 0.799725684271718 0.600137157864141 119 0.336692385390331 0.673384770780663 0.663307614609669 120 0.288786174814657 0.577572349629315 0.711213825185343 121 0.250817096054721 0.501634192109442 0.749182903945279 122 0.220790091236585 0.44158018247317 0.779209908763415 123 0.190110254080898 0.380220508161797 0.809889745919102 124 0.156800917488985 0.313601834977969 0.843199082511015 125 0.176875878987430 0.353751757974859 0.82312412101257 126 0.132802661120135 0.265605322240271 0.867197338879865 127 0.156362690598845 0.312725381197690 0.843637309401155 128 0.13096995985428 0.26193991970856 0.86903004014572 129 0.149657640087204 0.299315280174408 0.850342359912796 130 0.103859746850116 0.207719493700231 0.896140253149884 131 0.0785147451936414 0.157029490387283 0.921485254806359 132 0.0520610688339489 0.104122137667898 0.94793893116605 133 0.0429236272401314 0.0858472544802628 0.957076372759869 134 0.250754926528566 0.501509853057131 0.749245073471434 135 0.157215613633371 0.314431227266743 0.842784386366629 136 0.58948885653729 0.82102228692542 0.41051114346271 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 23 0.181102362204724 NOK 5% type I error level 46 0.362204724409449 NOK 10% type I error level 59 0.464566929133858 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213841gv43h2tf61myt86/10dibe1291213752.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213841gv43h2tf61myt86/10dibe1291213752.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213841gv43h2tf61myt86/1zqvn1291213752.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213841gv43h2tf61myt86/1zqvn1291213752.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213841gv43h2tf61myt86/2zqvn1291213752.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213841gv43h2tf61myt86/2zqvn1291213752.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213841gv43h2tf61myt86/3zqvn1291213752.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213841gv43h2tf61myt86/3zqvn1291213752.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213841gv43h2tf61myt86/4szv81291213752.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213841gv43h2tf61myt86/4szv81291213752.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213841gv43h2tf61myt86/5szv81291213752.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213841gv43h2tf61myt86/5szv81291213752.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213841gv43h2tf61myt86/6szv81291213752.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213841gv43h2tf61myt86/6szv81291213752.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213841gv43h2tf61myt86/73rut1291213752.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213841gv43h2tf61myt86/73rut1291213752.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213841gv43h2tf61myt86/8dibe1291213752.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213841gv43h2tf61myt86/8dibe1291213752.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213841gv43h2tf61myt86/9dibe1291213752.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291213841gv43h2tf61myt86/9dibe1291213752.ps (open in new window)

Parameters (Session):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 5 ; 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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