WS7 verschillende interactie effecten

*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: Mon, 22 Nov 2010 22:30:04 +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/22/t1290465009md9labtbg5em06u.htm/, Retrieved Mon, 22 Nov 2010 23:30:09 +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/22/t1290465009md9labtbg5em06u.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 41 25 0 15 0 9 0 3 0 0 38 25 0 15 0 9 0 4 0 0 37 19 0 14 0 9 0 4 0 1 36 18 18 10 10 14 14 2 2 1 42 18 18 10 10 8 8 4 4 0 44 23 0 9 0 14 0 4 0 0 40 23 0 18 0 15 0 3 0 0 43 25 0 14 0 9 0 4 0 0 40 23 0 11 0 11 0 4 0 0 45 24 0 11 0 14 0 4 0 1 47 32 32 9 9 14 14 4 4 0 45 30 0 17 0 6 0 5 0 0 45 32 0 21 0 10 0 4 0 1 40 24 24 16 16 9 9 4 4 0 49 17 0 14 0 14 0 4 0 1 48 30 30 24 24 8 8 5 5 0 44 25 0 7 0 11 0 4 0 1 29 25 25 9 9 10 10 4 4 0 42 26 0 18 0 16 0 4 0 0 45 23 0 11 0 11 0 5 0 1 32 25 25 13 13 11 11 5 5 1 32 25 25 13 13 11 11 5 5 0 41 35 0 18 0 7 0 4 0 1 29 19 19 14 14 13 13 2 2 0 38 20 0 12 0 10 0 4 0 0 41 21 0 12 0 9 0 4 0 1 38 21 21 9 9 9 9 4 4 1 24 23 23 11 11 15 15 3 3 1 34 24 24 8 8 13 13 2 2 0 38 23 0 5 0 16 0 2 0 1 37 19 19 10 10 12 12 3 3 0 46 17 0 11 0 6 0 5 0 1 48 27 27 15 15 4 4 5 5 0 42 27 0 16 0 12 0 4 0 0 46 25 0 12 0 10 0 4 0 1 43 18 18 14 14 14 14 5 5 0 38 22 0 13 0 9 0 4 0 1 39 26 26 10 10 10 10 4 4 0 34 26 0 18 0 14 0 4 0 0 39 23 0 17 0 14 0 4 0 0 35 1 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 7 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Career[t] = + 32.4711075468022 -1.86339575161424G[t] + 0.191031539247891PersonalStandards[t] -0.0540144402593811PeG[t] -0.173665001512474ParentalExpectations[t] + 0.534242117931472PaG[t] -0.248653021388442Doubts[t] + 0.140966318180626DoG[t] + 2.38392162985542LeadershipPreference[t] -1.98395184485659LeaderG[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) 32.4711075468022 4.193313 7.7435 0 0 G -1.86339575161424 6.397871 -0.2913 0.771302 0.385651 PersonalStandards 0.191031539247891 0.144457 1.3224 0.188251 0.094125 PeG -0.0540144402593811 0.204208 -0.2645 0.79179 0.395895 ParentalExpectations -0.173665001512474 0.168811 -1.0288 0.305423 0.152711 PaG 0.534242117931472 0.249807 2.1386 0.034254 0.017127 Doubts -0.248653021388442 0.215297 -1.1549 0.250145 0.125072 DoG 0.140966318180626 0.300314 0.4694 0.639538 0.319769 LeadershipPreference 2.38392162985542 0.718795 3.3166 0.001169 0.000584 LeaderG -1.98395184485659 0.949644 -2.0892 0.038557 0.019279 Multiple Linear Regression - Regression Statistics Multiple R 0.473114760725264 R-squared 0.223837576816124 Adjusted R-squared 0.172473887046603 F-TEST (value) 4.35789519445598 F-TEST (DF numerator) 9 F-TEST (DF denominator) 136 p-value 5.2233134229529e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.80633728614537 Sum Squared Residuals 3141.71942271400 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 41 39.5558087023827 1.44419129761735 2 38 41.9397303322381 -3.93973033223809 3 37 40.9672060982632 -3.96720609826321 4 36 35.9721164662594 0.0278835337406244 5 42 37.4181762555039 4.58182374449607 6 44 41.3563921558749 2.64360784412506 7 40 37.1608324910188 2.83916750898119 8 43 42.1133953337506 0.88660466624944 9 40 41.7550212170153 -1.75502121701532 10 45 41.2000936920979 3.79990630790212 11 47 38.3297183056772 8.67028169432282 12 45 45.6774387194733 -0.677438719473339 13 45 41.98630807651 3.01369192348996 14 40 40.2960548447412 -0.296054844741168 15 49 39.3418779128252 9.65812208717478 16 48 44.5104308582309 3.48956914176914 17 44 42.831744301561 1.16825569843900 18 29 37.8013454255889 -8.80134542558888 19 42 39.8691957172295 2.13080428277054 20 45 44.1389428468707 0.861057153129264 21 32 39.5359369730559 -7.53593697305588 22 32 39.5359369730559 -7.53593697305588 23 41 43.8263567629565 -2.82635676295646 24 29 37.6591287341317 -8.6591287341317 25 38 41.2569146191476 -3.25691461914761 26 41 41.6965991797839 -0.696599179783944 27 38 37.3609637328426 0.639036267157348 28 24 37.3100621594119 -13.3100621594119 29 34 36.1807515305603 -2.18075153056026 30 38 36.7859028594371 1.21409714056288 31 37 36.7244767566623 0.275523243337651 32 46 44.2360187183256 1.76398128167440 33 48 41.2849323263256 6.71506767367439 34 42 41.4021693450561 0.59783065494393 35 46 42.2120723153871 3.78792768461293 36 43 38.6143342869319 4.38566571306814 37 38 41.7139657175194 -3.71396571751936 38 39 38.2989396409964 0.701060359003616 39 34 40.3665017600063 -6.36650176000635 40 39 39.9670721437751 -0.967072143775147 41 35 35.7249452024452 -0.724945202445207 42 41 39.2254050074509 1.77459499254913 43 40 38.4129372934348 1.58706270656523 44 43 37.2306849759689 5.76931502403111 45 37 36.3730278400649 0.626972159935151 46 41 41.7542206619245 -0.754220661924494 47 46 42.4031038546350 3.59689614536504 48 26 37.3576398021583 -11.3576398021583 49 41 37.6316740001353 3.36832599986472 50 37 40.0247040937138 -3.02470409371384 51 39 40.7936490601487 -1.79364906014868 52 44 44.0694770717708 -0.0694770717707643 53 39 36.3051627993048 2.69483720069517 54 36 40.490284466391 -4.49028446639096 55 38 36.8711344379326 1.12886556206745 56 38 38.4576907341857 -0.457690734185748 57 38 41.3563921558749 -3.35639215587494 58 32 38.5157613163121 -6.51576131631212 59 33 37.8845644133465 -4.88456441334646 60 46 37.6725149376152 8.32748506238478 61 42 42.2696937975276 -0.269693797527617 62 42 35.8994107590485 6.1005892409515 63 43 41.5821567705937 1.41784322940633 64 41 36.3980927163545 4.60190728364549 65 49 42.7962106709993 6.20378932900066 66 45 36.59223022041 8.40776977959003 67 39 41.8473757746267 -2.8473757746267 68 45 38.9224694434257 6.0775305565743 69 31 37.2760754552999 -6.27607545529991 70 30 37.2760754552999 -7.27607545529991 71 45 43.0575089162797 1.94249108372028 72 48 45.4232634533089 2.5767365466911 73 28 39.3071448373544 -11.3071448373544 74 35 37.1024209215856 -2.10242092158558 75 38 41.3090142323539 -3.30901423235386 76 39 38.2777958562305 0.722204143769454 77 40 39.0561626117298 0.943837388270175 78 38 38.4100423924829 -0.410042392482929 79 42 40.3657008290738 1.63429917092617 80 36 34.4240586133624 1.57594138663755 81 49 47.0493921938377 1.95060780616228 82 41 42.1370842955111 -1.1370842955111 83 18 35.5749691626933 -17.5749691626933 84 36 38.5493519480278 -2.54935194802778 85 42 39.8552457591108 2.14475424088924 86 41 40.8330417287285 0.166958271271491 87 43 41.0256281354946 1.97437186450541 88 46 39.4223986452753 6.57760135472467 89 37 38.326821767877 -1.32682176787697 90 38 37.9840645055394 0.0159354944605742 91 43 40.2678247783698 2.73217522163016 92 41 44.3986399819695 -3.39863998196947 93 35 34.741859113163 0.258140886837005 94 39 42.831744301561 -3.831744301561 95 42 39.8344626417586 2.16553735824137 96 36 41.1819265992716 -5.18192659927164 97 35 38.880181873894 -3.88018187389403 98 33 34.821887119412 -1.821887119412 99 36 37.7394489776968 -1.73944897769678 100 48 40.6080314264372 7.39196857356285 101 41 41.2569146191476 -0.256914619147610 102 47 41.8312171780897 5.16878282191029 103 41 38.7340610632506 2.26593893674945 104 31 37.5570690162767 -6.55706901627673 105 36 41.2422173639098 -5.24221736390976 106 46 40.7936490601487 5.20635093985132 107 39 37.1408197636909 1.85918023630911 108 44 41.424257196635 2.57574280336504 109 43 37.3504740671593 5.64952593284066 110 32 42.4788924296017 -10.4788924296017 111 40 38.1378838562902 1.86211614370981 112 40 39.0229690290441 0.977030970955874 113 46 37.4789512875072 8.52104871249278 114 45 39.561330571317 5.43866942868303 115 39 42.4836137434452 -3.48361374344522 116 44 43.3227279203128 0.677272079687243 117 35 41.2395480814122 -6.23954808141219 118 38 38.5722403546975 -0.572240354697498 119 38 35.6673237203047 2.33267627969534 120 36 35.7879491174087 0.212050882591279 121 42 38.2998983715485 3.70010162845148 122 39 42.3383590175368 -3.33835901753677 123 41 40.7464988102865 0.253501189713468 124 41 40.3949120356104 0.605087964389639 125 47 37.9470464995893 9.05295350041073 126 39 38.4862085969529 0.513791403047067 127 40 39.2879092401752 0.712090759824831 128 44 40.6774975773788 3.32250242262119 129 42 40.9727279671975 1.02727203280249 130 35 39.1326432573727 -4.13264325737266 131 46 44.3418190549197 1.65818094508025 132 43 37.0066975656542 5.99330243434577 133 40 37.703293602296 2.29670639770398 134 44 41.2750817119738 2.72491828802615 135 37 38.9989500890685 -1.99895008906854 136 46 38.3561521636577 7.64384783634233 137 44 44.1389428468707 -0.138942846870735 138 35 38.7569494699203 -3.75694946992027 139 39 38.1778667343050 0.822133265694958 140 40 38.5090970036226 1.49090299637735 141 42 39.8552457591108 2.14475424088924 142 37 37.6821481806818 -0.682148180681815 143 29 39.1230100143061 -10.1230100143061 144 33 38.1110193317288 -5.1110193317288 145 35 37.0575991390849 -2.05759913908494 146 42 36.1355674232284 5.86443257677163 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 13 0.138597162388021 0.277194324776042 0.861402837611979 14 0.0545768614944916 0.109153722988983 0.945423138505508 15 0.324342990571369 0.648685981142738 0.675657009428631 16 0.211616583197529 0.423233166395059 0.78838341680247 17 0.144403910141993 0.288807820283986 0.855596089858007 18 0.161867375537669 0.323734751075338 0.838132624462331 19 0.163608217717723 0.327216435435445 0.836391782282277 20 0.106716746252957 0.213433492505915 0.893283253747043 21 0.659110689022468 0.681778621955065 0.340889310977532 22 0.610288165462973 0.779423669074054 0.389711834537027 23 0.533922055284988 0.932155889430024 0.466077944715012 24 0.674333633421152 0.651332733157695 0.325666366578848 25 0.632306221623772 0.735387556752456 0.367693778376228 26 0.557921294231796 0.884157411536407 0.442078705768204 27 0.489056177505178 0.978112355010355 0.510943822494822 28 0.615198504918122 0.769602990163756 0.384801495081878 29 0.605628801303089 0.788742397393821 0.394371198696911 30 0.541230380386176 0.917539239227647 0.458769619613824 31 0.547626438042494 0.904747123915011 0.452373561957506 32 0.510599074217097 0.978801851565806 0.489400925782903 33 0.486541887991732 0.973083775983463 0.513458112008268 34 0.424177502965508 0.848355005931017 0.575822497034492 35 0.402957739834935 0.80591547966987 0.597042260165065 36 0.662822332064316 0.674355335871368 0.337177667935684 37 0.636379758288642 0.727240483422717 0.363620241711358 38 0.578692658908737 0.842614682182526 0.421307341091263 39 0.679413824380631 0.641172351238739 0.320586175619369 40 0.635931371075282 0.728137257849435 0.364068628924718 41 0.579749656907653 0.840500686184694 0.420250343092347 42 0.530034443583107 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0.477969904108995 0.95593980821799 0.522030095891005 61 0.427099774761699 0.854199549523397 0.572900225238301 62 0.456654515994874 0.913309031989748 0.543345484005126 63 0.415505287372566 0.831010574745131 0.584494712627434 64 0.464913438486898 0.929826876973796 0.535086561513102 65 0.504710587070479 0.990578825859042 0.495289412929521 66 0.571660532547544 0.856678934904912 0.428339467452456 67 0.540508357478957 0.918983285042086 0.459491642521043 68 0.571160191993693 0.857679616012614 0.428839808006307 69 0.615644048383365 0.76871190323327 0.384355951616635 70 0.688649828905563 0.622700342188873 0.311350171094436 71 0.662432870110273 0.675134259779453 0.337567129889727 72 0.634162311299532 0.731675377400936 0.365837688700468 73 0.817695888436467 0.364608223127065 0.182304111563533 74 0.786072862367028 0.427854275265944 0.213927137632972 75 0.776374595726135 0.447250808547729 0.223625404273865 76 0.738309603185842 0.523380793628315 0.261690396814158 77 0.698359163044077 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0.19626477971064 95 0.766973996193153 0.466052007613695 0.233026003806847 96 0.773998476971019 0.452003046057963 0.226001523028981 97 0.752393430274116 0.495213139451768 0.247606569725884 98 0.793166688951802 0.413666622096396 0.206833311048198 99 0.794705380048825 0.410589239902351 0.205294619951175 100 0.818512477761955 0.362975044476089 0.181487522238045 101 0.779097979897869 0.441804040204263 0.220902020102131 102 0.789197318745428 0.421605362509144 0.210802681254572 103 0.750319615578945 0.49936076884211 0.249680384421055 104 0.786940202472244 0.426119595055511 0.213059797527756 105 0.779983417486347 0.440033165027306 0.220016582513653 106 0.823767514890454 0.352464970219092 0.176232485109546 107 0.783771049509942 0.432457900980115 0.216228950490058 108 0.740324374493805 0.51935125101239 0.259675625506195 109 0.731124732711183 0.537750534577635 0.268875267288817 110 0.865961177390249 0.268077645219503 0.134038822609751 111 0.83075926313239 0.338481473735220 0.169240736867610 112 0.79081141340138 0.41837717319724 0.20918858659862 113 0.858680760483472 0.282638479033056 0.141319239516528 114 0.854979967733835 0.290040064532329 0.145020032266165 115 0.822606409714813 0.354787180570375 0.177393590285187 116 0.782695383518474 0.434609232963052 0.217304616481526 117 0.824905428711078 0.350189142577844 0.175094571288922 118 0.772261105270916 0.455477789458169 0.227738894729084 119 0.713960234233319 0.572079531533362 0.286039765766681 120 0.674869939609651 0.650260120780698 0.325130060390349 121 0.62356944875981 0.75286110248038 0.37643055124019 122 0.579224635115138 0.841550729769724 0.420775364884862 123 0.558392323832602 0.883215352334796 0.441607676167398 124 0.569262057225896 0.861475885548207 0.430737942774104 125 0.547251846576069 0.905496306847863 0.452748153423931 126 0.46439739660428 0.92879479320856 0.53560260339572 127 0.422785364779619 0.845570729559238 0.577214635220381 128 0.335326298422462 0.670652596844923 0.664673701577538 129 0.244356538327140 0.488713076654281 0.75564346167286 130 0.170764273455846 0.341528546911692 0.829235726544154 131 0.106034373907508 0.212068747815016 0.893965626092492 132 0.0718381973307419 0.143676394661484 0.928161802669258 133 0.0352012167512303 0.0704024335024607 0.96479878324877 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 0 0 OK 10% type I error level 2 0.0165289256198347 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/22/t1290465009md9labtbg5em06u/10lx4b1290464993.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290465009md9labtbg5em06u/10lx4b1290464993.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290465009md9labtbg5em06u/1p6o21290464993.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290465009md9labtbg5em06u/1p6o21290464993.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290465009md9labtbg5em06u/2p6o21290464993.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290465009md9labtbg5em06u/2p6o21290464993.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290465009md9labtbg5em06u/3p6o21290464993.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290465009md9labtbg5em06u/3p6o21290464993.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290465009md9labtbg5em06u/40fnn1290464993.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290465009md9labtbg5em06u/40fnn1290464993.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290465009md9labtbg5em06u/50fnn1290464993.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290465009md9labtbg5em06u/50fnn1290464993.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290465009md9labtbg5em06u/60fnn1290464993.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290465009md9labtbg5em06u/60fnn1290464993.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290465009md9labtbg5em06u/7a6n81290464993.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290465009md9labtbg5em06u/7a6n81290464993.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290465009md9labtbg5em06u/8lx4b1290464993.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290465009md9labtbg5em06u/8lx4b1290464993.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290465009md9labtbg5em06u/9lx4b1290464993.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290465009md9labtbg5em06u/9lx4b1290464993.ps (open in new window)

Parameters (Session):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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