month - assignment (jonas poels)

*Unverified author*

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

Title produced by software: Multiple Regression

Date of computation: Thu, 02 Dec 2010 18:43:17 +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/02/t12913153002pgdwc7e3efvq6y.htm/, Retrieved Thu, 02 Dec 2010 19:41:40 +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/02/t12913153002pgdwc7e3efvq6y.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 «

9 24 14 11 12 24 26 9 25 11 7 8 25 23 9 17 6 17 8 30 25 9 18 12 10 8 19 23 9 18 8 12 9 22 19 9 16 10 12 7 22 29 10 20 10 11 4 25 25 10 16 11 11 11 23 21 10 18 16 12 7 17 22 10 17 11 13 7 21 25 10 23 13 14 12 19 24 10 30 12 16 10 19 18 10 23 8 11 10 15 22 10 18 12 10 8 16 15 10 15 11 11 8 23 22 10 12 4 15 4 27 28 10 21 9 9 9 22 20 10 15 8 11 8 14 12 10 20 8 17 7 22 24 10 31 14 17 11 23 20 10 27 15 11 9 23 21 10 34 16 18 11 21 20 10 21 9 14 13 19 21 10 31 14 10 8 18 23 10 19 11 11 8 20 28 10 16 8 15 9 23 24 10 20 9 15 6 25 24 10 21 9 13 9 19 24 10 22 9 16 9 24 23 10 17 9 13 6 22 23 10 24 10 9 6 25 29 10 25 16 18 16 26 24 10 26 11 18 5 29 18 10 25 8 12 7 32 25 10 17 9 17 9 25 21 10 32 16 9 6 29 26 10 33 11 9 6 28 22 10 13 16 12 5 17 22 10 32 12 18 12 28 22 10 25 12 12 7 29 23 10 29 14 18 10 26 30 10 22 9 14 9 25 23 10 18 10 15 8 14 17 10 17 9 16 5 25 23 10 20 10 10 8 26 23 10 15 12 11 8 20 25 10 20 14 14 10 18 24 10 33 14 9 6 32 24 10 29 10 12 8 25 23 10 23 14 17 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 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation Doubtsaboutactions[t] = + 3.49500726801616 + 0.396068656024888month[t] + 0.247296922995200ConcernoverMistakes[t] -0.109229617447763ParentalExpectations[t] + 0.151907036430486ParentalCriticism[t] -0.190172028652719PersonalStandards[t] + 0.111057364353945`Organization `[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) 3.49500726801616 10.737381 0.3255 0.745251 0.372626 month 0.396068656024888 1.056626 0.3748 0.7083 0.35415 ConcernoverMistakes 0.247296922995200 0.040366 6.1263 0 0 ParentalExpectations -0.109229617447763 0.074666 -1.4629 0.145559 0.072779 ParentalCriticism 0.151907036430486 0.093867 1.6183 0.107666 0.053833 PersonalStandards -0.190172028652719 0.057182 -3.3257 0.001106 0.000553 `Organization ` 0.111057364353945 0.056914 1.9513 0.052859 0.02643 Multiple Linear Regression - Regression Statistics Multiple R 0.49015254029223 R-squared 0.240249512754926 Adjusted R-squared 0.210259361942621 F-TEST (value) 8.01094713589593 F-TEST (DF numerator) 6 F-TEST (DF denominator) 152 p-value 1.60506545610062e-07 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.48875024721804 Sum Squared Residuals 941.469424540232 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 14 11.9394727549027 2.06052724509729 2 11 11.4927158802524 -0.492715880252443 3 6 7.69329890725751 -1.69329890725751 4 12 10.5749807388591 1.42501926114093 5 8 9.4936829970201 -1.49368299702010 6 10 9.80584872170817 0.194151278291827 7 10 9.82986803449623 0.170131965503771 8 11 9.8401441974185 1.15985580258151 9 16 10.8699698165094 5.13003018349055 10 11 10.0859272545174 0.91407274548256 11 13 12.4893010501448 0.510698949855199 12 12 13.0317620172310 -1.03176201723103 13 8 13.0517492155301 -5.0517492155301 14 12 10.6531065660106 1.34689343398944 15 11 9.24818352948578 1.75181647051422 16 4 7.36740221649998 -3.36740221649998 17 9 11.0703886387278 -2.07038863872782 18 8 9.8491581438208 -1.84915814382081 19 8 10.0896701607053 -2.08967016070532 20 14 12.7831629733060 1.21683702669403 21 15 12.2565962775047 2.74340372249528 22 16 13.7961681821492 2.20383181785076 23 9 11.8134421475231 -2.81344214752305 24 14 14.3760814224743 -0.376081422474277 25 11 11.4742314935484 -0.474231493548405 26 8 9.4325837478283 -1.43258374782830 27 9 9.5857062732122 -0.585706273212204 28 9 11.6482157123107 -2.64821571231071 29 9 10.5059062753451 -1.50590627534507 30 9 9.52173346072635 -0.521733460726344 31 10 11.7855584916493 -1.78555849164930 32 16 11.8234003714971 4.17659962850294 33 11 9.16285962167508 1.83714037832492 34 8 10.0816399407469 -2.08163994074689 35 9 8.7479052855607 0.252094714439295 36 16 12.6700736679382 3.32992633206181 37 11 12.6633131621703 -1.66331316217033 38 16 9.32967112867248 6.67032887132752 39 12 12.3443919007282 -0.344391900728182 40 12 10.4300412979972 1.56995870200285 41 14 12.5674900310186 1.43250996898139 42 9 10.5341934815879 -1.53419348158788 43 10 10.7094172647851 -0.709417264785079 44 9 8.47162148599441 0.528378514005588 45 10 10.1344390403053 -0.134439040305326 46 12 10.1518717085058 1.84812829149423 47 14 11.6337682369509 2.36623176304905 48 14 12.1247397762673 1.87526022373266 49 10 12.3318241410193 -2.33182414101932 50 14 9.92787275067093 4.07212724932907 51 16 12.2383139701055 3.76168602989450 52 9 10.4833034670369 -1.48330346703694 53 10 11.6122987990958 -1.61229879909584 54 6 9.05254782211994 -3.05254782211994 55 8 11.3126240094914 -3.31262400949137 56 13 12.4464495868101 0.553550413189894 57 10 10.8548031088160 -0.85480310881598 58 8 8.9211024216192 -0.921102421619198 59 7 9.19209929934567 -2.19209929934567 60 15 9.82763144334586 5.17236855665414 61 9 9.92845485886802 -0.928454858868017 62 10 10.2416344597556 -0.241634459755649 63 12 10.2339474901638 1.76605250983625 64 13 10.5016128873003 2.49838711269968 65 10 8.42772328418314 1.57227671581685 66 11 11.8401612626324 -0.840161262632442 67 8 13.6172210547696 -5.61722105476958 68 9 9.14716650760623 -0.147166507606229 69 13 8.66367774377408 4.33632225622592 70 11 10.4374961802182 0.562503819781828 71 8 12.7724229605033 -4.77242296050328 72 9 10.795324841591 -1.795324841591 73 9 12.3561672838581 -3.35616728385811 74 15 12.5125116029296 2.48748839707036 75 9 11.1805451755373 -2.18054517553735 76 10 11.5767880176038 -1.57678801760383 77 14 8.94483252610616 5.05516747389384 78 12 10.9684700089048 1.03152999109522 79 12 11.1240023893920 0.875997610608046 80 11 11.5604261633614 -0.560426163361437 81 14 11.4868565158771 2.51314348412289 82 6 11.5753359278035 -5.57533592780347 83 12 11.2741939471723 0.72580605282767 84 8 10.0841048014864 -2.08410480148639 85 14 12.4145015928798 1.58549840712019 86 11 10.8265407584537 0.173459241546275 87 10 9.92784260091525 0.0721573990847512 88 14 10.2440443148589 3.75595568514108 89 12 12.0676495451566 -0.067649545156601 90 10 11.0062093751507 -1.00620937515070 91 14 13.0039077307139 0.996092269286107 92 5 9.01837653731086 -4.01837653731087 93 11 10.5103081143553 0.489691885644693 94 10 10.2065867477450 -0.206586747744983 95 9 11.4768161734027 -2.47681617340271 96 10 11.4529474681946 -1.45294746819459 97 16 13.7426686318634 2.25733136813656 98 13 12.8226479679577 0.177352032042325 99 9 10.8160373464980 -1.81603734649798 100 10 11.3784908057584 -1.37849080575841 101 10 10.9823971521634 -0.982397152163354 102 7 9.48477058943398 -2.48477058943398 103 9 9.81881717757514 -0.818817177575138 104 8 10.2373139588467 -2.23731395884674 105 14 12.8782906227441 1.12170937725588 106 14 11.6290622715850 2.37093772841498 107 8 11.0848609699681 -3.08486096996813 108 9 11.5424972385413 -2.54249723854129 109 14 11.8190700632370 2.18092993676304 110 14 10.7400525500353 3.25994744996475 111 8 9.87082919442977 -1.87082919442977 112 8 13.697498458878 -5.69749845887799 113 8 10.9938586542834 -2.99385865428335 114 7 8.57491538099254 -1.57491538099254 115 6 7.51783714558696 -1.51783714558696 116 8 9.42375889430732 -1.42375889430732 117 6 8.313190544643 -2.31319054464301 118 11 9.89476653040706 1.10523346959294 119 14 11.8484280834378 2.15157191656218 120 11 11.1069328584248 -0.106932858424812 121 11 11.9691146895295 -0.96911468952952 122 11 9.21443793840498 1.78556206159502 123 14 10.3937780970331 3.60622190296693 124 8 10.3942057228836 -2.39420572288361 125 20 11.5070132450521 8.49298675494791 126 11 10.3206918614346 0.679308138565415 127 8 9.1620717585722 -1.16207175857221 128 11 10.7387255732248 0.261274426775163 129 10 10.6388659972798 -0.638865997279794 130 14 13.4621841505030 0.537815849496953 131 11 10.5821223787796 0.417877621220388 132 9 10.6063976712483 -1.60639767124831 133 9 9.7312857236005 -0.731285723600498 134 8 10.1682290492721 -2.1682290492721 135 10 12.0649970149322 -2.06499701493220 136 13 10.7186811123059 2.28131888769407 137 13 10.1156039940656 2.88439600593437 138 12 9.30434987812627 2.69565012187372 139 8 10.3879135804722 -2.38791358047223 140 13 11.0423380333320 1.95766196666805 141 14 12.4395447309082 1.56045526909177 142 12 11.6835008055674 0.316499194432622 143 14 10.9244109318456 3.0755890681544 144 15 11.3156212139699 3.6843787860301 145 13 10.5323084720619 2.46769152793814 146 16 11.8679529926546 4.13204700734539 147 9 11.9879474787856 -2.98794747878561 148 9 10.4909904366288 -1.49099043662884 149 9 11.0108779647636 -2.01087796476355 150 8 11.3037885682201 -3.30378856822013 151 7 10.0660590949341 -3.06605909493411 152 16 11.9488381412397 4.05116185876027 153 11 13.3281789260286 -2.32817892602855 154 9 9.99787353631934 -0.997873536319345 155 11 9.84885001222374 1.15114998777626 156 9 9.87013481797661 -0.870134817976613 157 14 12.4949862284785 1.50501377152146 158 13 11.0363296221748 1.96367037782518 159 16 14.4652710924792 1.53472890752081 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.180728421510582 0.361456843021163 0.819271578489418 11 0.361303593392599 0.722607186785199 0.6386964066074 12 0.244096494997623 0.488192989995246 0.755903505002377 13 0.838011222799046 0.323977554401908 0.161988777200954 14 0.767010364468424 0.465979271063153 0.232989635531576 15 0.68217476756583 0.635650464868341 0.317825232434170 16 0.74776268662691 0.50447462674618 0.25223731337309 17 0.733567282650309 0.532865434699382 0.266432717349691 18 0.707161640187011 0.585676719625978 0.292838359812989 19 0.64050011095733 0.71899977808534 0.35949988904267 20 0.631780683770262 0.736438632459477 0.368219316229738 21 0.626165583901606 0.747668832196788 0.373834416098394 22 0.596612165108433 0.806775669783135 0.403387834891567 23 0.606997565678549 0.786004868642902 0.393002434321451 24 0.560211350462842 0.879577299074316 0.439788649537158 25 0.489135780696371 0.978271561392742 0.510864219303629 26 0.425243913159762 0.850487826319524 0.574756086840238 27 0.358336692704933 0.716673385409866 0.641663307295067 28 0.350149245384501 0.700298490769001 0.6498507546155 29 0.300223900183415 0.600447800366829 0.699776099816585 30 0.245031010224482 0.490062020448965 0.754968989775518 31 0.225890988154945 0.451781976309891 0.774109011845055 32 0.295856875866834 0.591713751733669 0.704143124133166 33 0.265429308153696 0.530858616307392 0.734570691846304 34 0.267435255740389 0.534870511480777 0.732564744259611 35 0.21975969127747 0.43951938255494 0.78024030872253 36 0.239313733557395 0.478627467114789 0.760686266442605 37 0.236589311641621 0.473178623283242 0.763410688358379 38 0.634738208365953 0.730523583268094 0.365261791634047 39 0.582739911542692 0.834520176914616 0.417260088457308 40 0.547685987870755 0.90462802425849 0.452314012129245 41 0.507024075003963 0.985951849992075 0.492975924996037 42 0.472715913630671 0.945431827261342 0.527284086369329 43 0.423769184834661 0.847538369669323 0.576230815165339 44 0.373554647354932 0.747109294709863 0.626445352645068 45 0.323174198041425 0.646348396082851 0.676825801958575 46 0.303117080464295 0.606234160928591 0.696882919535705 47 0.29250746883378 0.58501493766756 0.70749253116622 48 0.268986110590189 0.537972221180377 0.731013889409811 49 0.270077543763935 0.540155087527871 0.729922456236065 50 0.334893311917815 0.669786623835629 0.665106688082185 51 0.382500126416518 0.765000252833035 0.617499873583482 52 0.353365247479741 0.706730494959482 0.646634752520259 53 0.32786334913723 0.65572669827446 0.67213665086277 54 0.345724873119674 0.691449746239348 0.654275126880326 55 0.367110855096901 0.734221710193801 0.6328891449031 56 0.322124492745865 0.64424898549173 0.677875507254135 57 0.283702808818113 0.567405617636226 0.716297191181887 58 0.247998053718992 0.495996107437984 0.752001946281008 59 0.235373189677573 0.470746379355145 0.764626810322427 60 0.371015787663607 0.742031575327213 0.628984212336393 61 0.329636676729931 0.659273353459863 0.670363323270069 62 0.287580264333987 0.575160528667974 0.712419735666013 63 0.266726397830043 0.533452795660085 0.733273602169957 64 0.276065034821851 0.552130069643702 0.723934965178149 65 0.251045975528134 0.502091951056268 0.748954024471866 66 0.218853646573136 0.437707293146272 0.781146353426864 67 0.396892099574743 0.793784199149486 0.603107900425257 68 0.351530452085616 0.703060904171231 0.648469547914384 69 0.438156942576485 0.87631388515297 0.561843057423515 70 0.393987372536352 0.787974745072704 0.606012627463648 71 0.519733325226462 0.960533349547076 0.480266674773538 72 0.49766551401752 0.99533102803504 0.50233448598248 73 0.532374445400242 0.935251109199516 0.467625554599758 74 0.532018575648426 0.935962848703148 0.467981424351574 75 0.519383628932437 0.961232742135127 0.480616371067563 76 0.49164426064901 0.98328852129802 0.50835573935099 77 0.641132141777 0.717735716446001 0.358867858223000 78 0.604402523698038 0.791194952603924 0.395597476301962 79 0.564048598796626 0.871902802406747 0.435951401203374 80 0.520782006246579 0.958435987506843 0.479217993753421 81 0.520260829464174 0.959478341071651 0.479739170535826 82 0.693013141877124 0.613973716245753 0.306986858122876 83 0.654104266077126 0.691791467845747 0.345895733922873 84 0.637181055605434 0.725637888789132 0.362818944394566 85 0.60801639565462 0.78396720869076 0.39198360434538 86 0.563072363893891 0.873855272212218 0.436927636106109 87 0.516415347191739 0.967169305616522 0.483584652808261 88 0.582864908247488 0.834270183505024 0.417135091752512 89 0.537543595331019 0.924912809337961 0.462456404668981 90 0.496606226060556 0.993212452121112 0.503393773939444 91 0.456487059415457 0.912974118830915 0.543512940584543 92 0.51628547550523 0.96742904898954 0.48371452449477 93 0.472097263672455 0.94419452734491 0.527902736327545 94 0.424827961744755 0.84965592348951 0.575172038255245 95 0.421798328639387 0.843596657278774 0.578201671360613 96 0.391831330924637 0.783662661849274 0.608168669075363 97 0.375675007377201 0.751350014754402 0.624324992622799 98 0.331272525159957 0.662545050319913 0.668727474840043 99 0.307833250676609 0.615666501353217 0.692166749323391 100 0.278213554704648 0.556427109409295 0.721786445295352 101 0.244678604677005 0.48935720935401 0.755321395322995 102 0.236706203416504 0.473412406833007 0.763293796583496 103 0.202710507155482 0.405421014310963 0.797289492844519 104 0.193016265016738 0.386032530033476 0.806983734983262 105 0.164659000143653 0.329318000287306 0.835340999856347 106 0.160001531629451 0.320003063258901 0.83999846837055 107 0.173003922948002 0.346007845896004 0.826996077051998 108 0.177026424580709 0.354052849161418 0.822973575419291 109 0.164494693307168 0.328989386614335 0.835505306692832 110 0.188039253933575 0.376078507867149 0.811960746066425 111 0.167904458684710 0.335808917369419 0.83209554131529 112 0.383554354666379 0.767108709332759 0.61644564533362 113 0.440996573081688 0.881993146163376 0.559003426918312 114 0.404991602863803 0.809983205727605 0.595008397136197 115 0.396873925734874 0.793747851469748 0.603126074265126 116 0.354703097268667 0.709406194537333 0.645296902731333 117 0.339974884443602 0.679949768887203 0.660025115556399 118 0.298560825064733 0.597121650129466 0.701439174935267 119 0.277492585498295 0.554985170996589 0.722507414501705 120 0.234196464919618 0.468392929839237 0.765803535080382 121 0.206815501025220 0.413631002050439 0.79318449897478 122 0.193640998106029 0.387281996212057 0.806359001893971 123 0.203707035750708 0.407414071501415 0.796292964249292 124 0.21557306164207 0.43114612328414 0.78442693835793 125 0.759576046356307 0.480847907287385 0.240423953643693 126 0.719577004376709 0.560845991246582 0.280422995623291 127 0.668545607218508 0.662908785562983 0.331454392781492 128 0.608419466672766 0.783161066654468 0.391580533327234 129 0.546388615929111 0.907222768141778 0.453611384070889 130 0.483220352105192 0.966440704210384 0.516779647894808 131 0.426179136558455 0.85235827311691 0.573820863441545 132 0.371118385118890 0.742236770237781 0.62888161488111 133 0.310541293707684 0.621082587415368 0.689458706292316 134 0.272087355084123 0.544174710168246 0.727912644915877 135 0.241635801709890 0.483271603419781 0.75836419829011 136 0.219388220396663 0.438776440793326 0.780611779603337 137 0.249962728502138 0.499925457004276 0.750037271497862 138 0.255751740603562 0.511503481207124 0.744248259396438 139 0.271635729409338 0.543271458818677 0.728364270590662 140 0.212861942641384 0.425723885282768 0.787138057358616 141 0.161410114154592 0.322820228309184 0.838589885845408 142 0.117849588184148 0.235699176368296 0.882150411815852 143 0.136569895505685 0.273139791011370 0.863430104494315 144 0.124900008788224 0.249800017576448 0.875099991211776 145 0.108267586464761 0.216535172929523 0.891732413535239 146 0.194919332342784 0.389838664685568 0.805080667657216 147 0.161751844607949 0.323503689215897 0.838248155392051 148 0.0964242597272326 0.192848519454465 0.903575740272767 149 0.0599275475332336 0.119855095066467 0.940072452466766 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 0 0 OK

Charts produced by software:

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Parameters (Session):

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

Parameters (R input):

par1 = 3 ; 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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