WS 7 multiple model

*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: Tue, 23 Nov 2010 23:53:07 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/24/t1290556474l4hehj5lt81y5eu.htm/, Retrieved Wed, 24 Nov 2010 00:54:34 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Nov/24/t1290556474l4hehj5lt81y5eu.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 «

1 1 3 2 2 1 1 1 4 4 1 1 2 4 4 2 2 4 4 3 2 1 1 3 2 2 2 4 2 2 2 1 3 3 3 1 1 1 4 3 1 1 1 2 2 2 3 3 3 2 2 5 3 3 1 3 3 3 3 1 1 1 4 3 1 1 2 3 1 1 1 2 2 2 2 2 5 5 3 2 1 2 2 2 1 5 2 3 1 2 1 2 3 3 1 1 2 3 2 2 1 3 3 3 1 2 3 4 4 2 2 3 3 3 2 1 1 1 2 1 1 1 3 2 2 2 4 2 2 1 1 1 4 3 1 1 3 4 3 1 3 2 4 1 2 1 1 1 2 2 1 2 3 3 2 1 1 1 1 1 1 2 4 1 2 1 1 2 2 1 1 1 3 1 1 1 1 3 2 2 1 1 2 2 1 1 3 4 3 2 5 3 1 1 1 1 1 2 3 2 2 3 3 3 1 1 1 3 3 2 1 2 5 3 1 1 3 3 3 1 2 2 4 4 2 1 1 3 3 1 1 1 4 3 1 1 1 2 1 1 1 1 3 2 2 1 1 2 2 1 1 3 3 3 2 2 2 2 1 2 1 2 3 3 1 1 2 4 2 1 3 4 1 1 1 1 3 1 1 2 2 3 1 1 1 2 3 4 4 2 1 1 2 3 2 1 3 2 1 1 1 1 3 4 1 1 1 5 5 2 1 1 3 3 1 1 1 3 2 2 2 3 3 1 2 2 3 3 3 1 1 1 3 3 1 1 1 4 3 1 1 2 4 1 1 1 2 4 3 2 1 1 3 2 1 3 4 4 2 2 1 3 1 1 1 1 2 3 3 1 1 1 3 1 1 1 1 3 2 2 2 3 5 2 2 1 2 4 2 1 1 1 3 3 1 3 3 5 4 1 4 3 4 4 1 1 1 4 1 1 1 2 4 2 1 1 1 4 3 2 2 2 3 2 1 1 2 3 3 1 2 2 4 3 1 1 1 3 3 2 2 2 1 2 1 1 1 4 2 1 1 1 4 3 2 1 3 3 1 2 1 2 3 3 2 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 6 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation gender[t] = + 0.70174011232469 + 0.386108714128475diet[t] -0.020748127759265gewicht[t] -0.104801665118860`bier/wijn`[t] + 0.210110400648029andere[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) 0.70174011232469 0.299755 2.341 0.020503 0.010252 diet 0.386108714128475 0.057088 6.7633 0 0 gewicht -0.020748127759265 0.06057 -0.3425 0.732402 0.366201 `bier/wijn` -0.104801665118860 0.065979 -1.5884 0.114233 0.057117 andere 0.210110400648029 0.10351 2.0299 0.044082 0.022041 Multiple Linear Regression - Regression Statistics Multiple R 0.500475175165715 R-squared 0.250475400957153 Adjusted R-squared 0.231132830659273 F-TEST (value) 12.9494372826246 F-TEST (DF numerator) 4 F-TEST (DF denominator) 155 p-value 4.0344911855783e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.761263121255809 Sum Squared Residuals 89.8258386665411 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1 1.23622191423371 -0.236221914233707 2 1 1.48833564081058 -0.488335640810575 3 1 1.46758751305131 -0.467587513051311 4 2 1.60208957101323 0.397910428986773 5 2 1.17291650463338 0.827083495366623 6 2 1.60158250060292 0.398417499397081 7 2 1.34153064976288 0.658469350237123 8 1 1.27822524016255 -0.278225240162547 9 1 1.27771816975224 -0.277718169752238 10 2 1.9036376773718 0.0963623226282013 11 2 2.46574470498072 -0.46574470498072 12 3 1.69352727672377 1.30647272327623 13 1 0.900561720707553 0.0994382792924467 14 1 1.51702189283301 -0.517021892833015 15 1 1.64307875612145 -0.643078756121448 16 2 2.63435885011022 -0.63435885011022 17 1 1.43296835547342 -0.432968355473419 18 5 1.72713229348104 3.27286770651896 19 1 1.30741856259529 -0.307418562595294 20 1 1.62233062836218 -0.622330628362183 21 1 1.69352727672377 -0.693527276723769 22 2 1.77808788449367 0.221912115506327 23 2 1.9036376773718 0.0963623226282013 24 1 1.06760776910421 -0.0676077691042088 25 1 1.23622191423371 -0.236221914233708 26 2 2.20518578373037 -0.205185783730370 27 1 0.900561720707553 0.0994382792924467 28 1 1.67277914896450 -0.672779148964504 29 3 1.70638416572178 1.29361583427822 30 1 1.27771816975224 -0.277718169752238 31 1 1.51752896324332 -0.517528963243323 32 1 1.17240943422307 -0.172409434223069 33 1 1.70638416572178 -0.706384165721779 34 1 1.04685964134494 -0.0468596413449438 35 1 1.13091317870454 -0.130913178704539 36 1 1.23622191423371 -0.236221914233708 37 1 1.04685964134494 -0.0468596413449438 38 1 1.88288954961253 -0.882889549612534 39 5 1.94462686248002 3.05537313751998 40 1 1.15216837687411 -0.152168376874113 41 2 1.69352727672377 0.306472723276231 42 1 1.13142024911485 -0.131420249114848 43 1 1.26592230707676 -0.265922307076764 44 1 1.69352727672377 -0.693527276723769 45 2 1.39197917036520 0.608020829634802 46 1 0.921309848466818 0.0786901515331817 47 1 0.900561720707553 0.0994382792924467 48 1 1.15166130646380 -0.151661306463804 49 1 1.23622191423371 -0.236221914233708 50 1 1.04685964134494 -0.0468596413449438 51 1 1.9036376773718 -0.903637677371799 52 2 1.74788042124031 0.252119578759691 53 1 1.30741856259529 -0.307418562595294 54 1 1.39147209995489 -0.391472099954889 55 3 2.33073557660850 0.669264423391504 56 1 2.15473726312805 -1.15473726312805 57 2 1.94462686248002 0.0553731375199797 58 2 1.77808788449367 0.221912115506327 59 1 1.15216837687411 -0.152168376874113 60 1 1.92387873472076 -0.923878734720755 61 1 0.816508183347958 0.183491816652042 62 1 0.880320663358597 0.119679336641403 63 1 0.921309848466818 0.0786901515331817 64 1 1.23622191423371 -0.236221914233708 65 2 2.11324100760952 -0.113241007609520 66 2 1.69352727672377 0.306472723276231 67 1 0.921309848466818 0.0786901515331817 68 1 0.900561720707553 0.0994382792924467 69 1 1.49627376507375 -0.49627376507375 70 1 1.49678083548406 -0.496780835484058 71 1 1.02611151358568 -0.0261115135856788 72 3 2.37379992885987 0.62620007114013 73 1 1.94462686248002 -0.94462686248002 74 1 1.30741856259529 -0.307418562595294 75 1 1.13091317870454 -0.130913178704539 76 1 1.23622191423371 -0.236221914233708 77 2 1.96694308697213 0.0330569130278709 78 1 1.39147209995489 -0.391472099954889 79 1 0.921309848466818 0.0786901515331817 80 3 1.54722935608638 1.45277064391362 81 4 1.56797748384564 2.43202251615436 82 1 1.11016505094527 -0.110165050945274 83 1 1.39147209995489 -0.391472099954889 84 1 1.11067212135558 -0.110672121355583 85 2 1.41222022771415 0.587779772285846 86 1 1.30741856259529 -0.307418562595294 87 2 1.28667043483603 0.713329565163971 88 1 1.13142024911485 -0.131420249114848 89 2 1.45371648323268 0.546283516767316 90 1 1.00536338582641 -0.00536338582641377 91 1 1.11067212135558 -0.110672121355583 92 1 2.11324100760952 -1.11324100760952 93 1 1.51752896324332 -0.517528963243323 94 2 1.81907706960189 0.180922930398105 95 1 1.77758081408336 -0.777580814083365 96 4 2.43604431213766 1.56395568786234 97 1 0.921309848466818 0.0786901515331817 98 1 2.03813973533371 -1.03813973533371 99 1 1.00536338582641 -0.00536338582641377 100 4 1.32027545159330 2.67972454840670 101 1 1.69352727672377 -0.693527276723769 102 1 1.88238247920223 -0.882382479202225 103 1 1.56797748384564 -0.567977483845644 104 4 2.37379992885987 1.62620007114013 105 1 1.51752896324332 -0.517528963243323 106 1 0.921309848466818 0.0786901515331817 107 2 1.41222022771415 0.587779772285846 108 1 0.775011927829428 0.224988072170572 109 2 1.28667043483603 0.713329565163971 110 3 1.9036376773718 1.09636232262820 111 4 2.71684429073697 1.28315570926303 112 1 1.39197917036520 -0.391979170365198 113 1 0.921309848466818 0.0786901515331817 114 1 1.25697004199297 -0.256970041992973 115 1 1.18186876971717 -0.181868769717168 116 2 1.77758081408336 0.222419185916635 117 1 0.879813592948288 0.120186407051712 118 1 1.77758081408336 -0.777580814083365 119 1 0.795760055588693 0.204239944411307 120 1 1.02611151358568 -0.0261115135856788 121 1 1.11016505094527 -0.110165050945274 122 1 0.900561720707553 0.0994382792924467 123 1 1.67277914896450 -0.672779148964504 124 4 1.86163435144296 2.13836564855704 125 1 1.13142024911485 -0.131420249114848 126 1 1.56797748384564 -0.567977483845644 127 2 1.39197917036520 0.608020829634802 128 3 1.9036376773718 1.09636232262820 129 1 1.51752896324332 -0.517528963243323 130 1 1.49678083548406 -0.496780835484058 131 3 2.65510697786948 0.344893022130515 132 2 2.05888786309298 -0.0588878630929797 133 1 0.879813592948288 0.120186407051712 134 1 1.11016505094527 -0.110165050945274 135 1 1.98769121473139 -0.987691214731394 136 1 1.83982519736116 -0.83982519736116 137 1 1.18186876971717 -0.181868769717168 138 1 1.00536338582641 -0.00536338582641377 139 1 1.00587045623672 -0.00587045623672217 140 1 2.05888786309298 -1.05888786309298 141 1 1.77758081408336 -0.777580814083365 142 3 1.88288954961253 1.11711045038747 143 1 2.11324100760952 -1.11324100760952 144 2 1.69352727672377 0.306472723276231 145 1 1.56797748384564 -0.567977483845644 146 1 1.39147209995489 -0.391472099954889 147 1 1.29952732383404 -0.299527323834039 148 2 1.76862854899957 0.231371451000426 149 1 1.32027545159330 -0.320275451593304 150 1 1.30741856259529 -0.307418562595294 151 1 0.795760055588693 0.204239944411307 152 1 2.2684911933307 -1.2684911933307 153 1 0.900561720707553 0.0994382792924467 154 1 0.921309848466818 0.0786901515331817 155 1 1.11067212135558 -0.110672121355583 156 1 1.9036376773718 -0.903637677371799 157 3 1.18186876971717 1.81813123028283 158 1 1.00536338582641 -0.00536338582641377 159 1 1.00536338582641 -0.00536338582641377 160 1 1.56797748384564 -0.567977483845644 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.372850472327764 0.745700944655527 0.627149527672236 9 0.230389497957080 0.460778995914159 0.76961050204292 10 0.126777541103682 0.253555082207364 0.873222458896318 11 0.0749769429010032 0.149953885802006 0.925023057098997 12 0.0660545947730924 0.132109189546185 0.933945405226908 13 0.240950042358202 0.481900084716405 0.759049957641798 14 0.210491109294606 0.420982218589213 0.789508890705394 15 0.161315310645782 0.322630621291565 0.838684689354218 16 0.125221377831062 0.250442755662125 0.874778622168938 17 0.100781283127513 0.201562566255026 0.899218716872487 18 0.97190862802897 0.0561827439420604 0.0280913719710302 19 0.959061091103284 0.081877817793433 0.0409389088967165 20 0.962280810097008 0.0754383798059847 0.0377191899029923 21 0.952144831440003 0.0957103371199939 0.0478551685599969 22 0.937384072264918 0.125231855470164 0.0626159277350821 23 0.914153331686928 0.171693336626144 0.0858466683130722 24 0.884460902093125 0.231078195813751 0.115539097906875 25 0.86738527690754 0.265229446184918 0.132614723092459 26 0.829676977598188 0.340646044803624 0.170323022401812 27 0.785680416854681 0.428639166290638 0.214319583145319 28 0.765505158882 0.468989682236001 0.234494841118000 29 0.763428089119456 0.473143821761088 0.236571910880544 30 0.722599696507283 0.554800606985434 0.277400303492717 31 0.692130446528255 0.61573910694349 0.307869553471745 32 0.644713435735389 0.710573128529223 0.355286564264611 33 0.723082670849432 0.553834658301137 0.276917329150568 34 0.672082144828331 0.655835710343338 0.327917855171669 35 0.629703198126238 0.740593603747524 0.370296801873762 36 0.587939640897233 0.824120718205535 0.412060359102767 37 0.531926022583308 0.936147954833385 0.468073977416692 38 0.54588162642378 0.90823674715244 0.45411837357622 39 0.962510015741464 0.074979968517072 0.037489984258536 40 0.950455361856541 0.099089276286918 0.049544638143459 41 0.939942346756326 0.120115306487349 0.0600576532436744 42 0.92302499096873 0.153950018062539 0.0769750090312697 43 0.90358369074918 0.192832618501640 0.0964163092508202 44 0.893906857833983 0.212186284332033 0.106093142166017 45 0.901878713511965 0.196242572976069 0.0981212864880347 46 0.880989667060821 0.238020665878358 0.119010332939179 47 0.857580059406034 0.284839881187933 0.142419940593966 48 0.83492186935992 0.330156261280159 0.165078130640079 49 0.807217992428034 0.385564015143932 0.192782007571966 50 0.771329438977094 0.457341122045812 0.228670561022906 51 0.784803207607945 0.430393584784109 0.215196792392055 52 0.7506769776744 0.498646044651201 0.249323022325600 53 0.713185475339879 0.573629049320242 0.286814524660121 54 0.681213353545554 0.637573292908893 0.318786646454446 55 0.657552556668853 0.684894886662294 0.342447443331147 56 0.749819275670088 0.500361448659823 0.250180724329912 57 0.712031335095969 0.575937329808062 0.287968664904031 58 0.680135462923782 0.639729074152435 0.319864537076218 59 0.637416540406855 0.72516691918629 0.362583459593145 60 0.665705829301673 0.668588341396653 0.334294170698327 61 0.629679206643715 0.74064158671257 0.370320793356285 62 0.590764876363419 0.818470247273162 0.409235123636581 63 0.544929505635675 0.91014098872865 0.455070494364325 64 0.503912435659341 0.992175128681319 0.496087564340659 65 0.459092165139151 0.918184330278303 0.540907834860849 66 0.422360130880353 0.844720261760705 0.577639869119647 67 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0.737478884724156 0.525042230551687 0.262521115275844 103 0.720662238172576 0.558675523654848 0.279337761827424 104 0.851667598466835 0.29666480306633 0.148332401533165 105 0.834351777181422 0.331296445637157 0.165648222818578 106 0.801513800481517 0.396972399036965 0.198486199518483 107 0.788813430318796 0.422373139362407 0.211186569681204 108 0.753131574577066 0.493736850845868 0.246868425422934 109 0.749597541491348 0.500804917017304 0.250402458508652 110 0.800010636025206 0.399978727949589 0.199989363974795 111 0.911265745122122 0.177468509755756 0.0887342548778778 112 0.897757811811666 0.204484376376669 0.102242188188334 113 0.873157901658807 0.253684196682385 0.126842098341193 114 0.844322020926387 0.311355958147225 0.155677979073613 115 0.81339121785484 0.373217564290321 0.186608782145161 116 0.787885136465984 0.424229727068032 0.212114863534016 117 0.747186492605758 0.505627014788485 0.252813507394242 118 0.73342842691294 0.533143146174121 0.266571573087060 119 0.687707528232702 0.624584943534596 0.312292471767298 120 0.638589022997726 0.722821954004549 0.361410977002274 121 0.585538388146157 0.828923223707685 0.414461611853843 122 0.530544184537598 0.938911630924804 0.469455815462402 123 0.507921655019615 0.98415668996077 0.492078344980385 124 0.941869118738234 0.116261762523532 0.0581308812617658 125 0.927330676751071 0.145338646497858 0.072669323248929 126 0.919503625497442 0.160992749005117 0.0804963745025584 127 0.900562997599889 0.198874004800222 0.099437002400111 128 0.947635406924999 0.104729186150003 0.0523645930750014 129 0.93885868552524 0.122282628949521 0.0611413144747605 130 0.929950038440704 0.140099923118593 0.0700499615592965 131 0.9499013805777 0.100197238844598 0.0500986194222991 132 0.945870917758432 0.108258164483136 0.0541290822415681 133 0.923899950228005 0.152200099543991 0.0761000497719954 134 0.897890339663206 0.204219320673588 0.102109660336794 135 0.88254125027142 0.234917499457159 0.117458749728580 136 0.864565659646532 0.270868680706935 0.135434340353468 137 0.828902826699828 0.342194346600345 0.171097173300172 138 0.77655429593583 0.446891408128339 0.223445704064170 139 0.755468023124235 0.48906395375153 0.244531976875765 140 0.723834708874179 0.552330582251642 0.276165291125821 141 0.66211746765662 0.67576506468676 0.33788253234338 142 0.879631895088903 0.240736209822194 0.120368104911097 143 0.84305478881685 0.313890422366299 0.156945211183150 144 0.825993340429701 0.348013319140598 0.174006659570299 145 0.77020202111779 0.459595957764419 0.229797978882209 146 0.687730493467928 0.624539013064143 0.312269506532072 147 0.601524482944391 0.796951034111218 0.398475517055609 148 0.664229777614781 0.671540444770438 0.335770222385219 149 0.575238859139975 0.84952228172005 0.424761140860025 150 0.446284297749142 0.892568595498283 0.553715702250858 151 0.390963609482503 0.781927218965007 0.609036390517497 152 0.288173613439050 0.576347226878099 0.71182638656095 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 6 0.0413793103448276 OK

Charts produced by software:

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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') }

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Software written by Ed van Stee & Patrick Wessa

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