Multiple Linear Regression - Celebrity

*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, 14 Dec 2010 15:37:51 +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/14/t1292341008m2qonps61b6dplf.htm/, Retrieved Tue, 14 Dec 2010 16:36:48 +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/14/t1292341008m2qonps61b6dplf.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 «

6 2 1 66 4 1 1 54 5 1 1 82 4 1 1 61 4 1 1 65 6 1 1 77 6 1 1 66 4 2 1 66 4 1 1 66 6 1 1 48 4 1 1 57 6 1 1 80 5 1 1 60 4 1 1 70 6 2 1 85 3 2 1 59 5 1 1 72 6 1 1 70 4 2 1 74 6 2 1 70 2 1 1 51 7 2 1 70 5 1 1 71 2 2 1 72 4 1 1 50 4 2 1 69 6 2 1 73 6 1 1 66 5 2 1 73 6 1 1 58 6 2 1 78 4 1 1 83 6 2 1 76 6 1 1 77 6 1 1 79 2 2 1 71 4 2 1 79 5 1 1 60 3 1 1 73 7 2 1 70 5 1 1 42 3 1 1 74 8 1 1 68 8 1 1 83 5 2 1 62 6 2 1 79 3 2 1 61 5 2 1 86 4 2 1 64 5 1 1 75 5 2 1 59 6 2 1 82 5 1 1 61 6 1 1 69 6 1 1 60 4 2 1 59 8 1 1 81 6 2 1 65 4 2 1 60 6 2 1 60 5 2 1 45 5 1 1 75 6 2 1 84 6 1 1 77 6 2 1 64 6 2 1 54 6 2 1 72 6 1 1 56 7 2 1 67 4 2 1 81 4 1 1 73 3 2 1 67 6 2 1 72 5 1 1 69 5 1 1 71 3 2 1 77 5 1 1 63 4 2 1 49 3 2 1 74 7 1 1 76 4 2 1 65 4 1 1 65 5 2 1 69 6 1 1 71 2 1 1 68 2 2 1 49 6 1 1 86 4 2 1 63 5 2 1 77 6 1 1 52 7 1 1 73 8 1 1 63 6 1 1 54 6 1 1 56 3 1 1 54 7 1 1 61 3 1 1 70 6 2 1 68 4 2 1 63 4 1 1 76 6 1 1 69 6 1 1 71 6 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 Celebrity[t] = + 1.74574549142842 -0.425106954614593Geslacht[t] + 1.74847731530155Leeftijd[t] + 0.0326680938365645Groepsgevoel[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) 1.74574549142842 1.436649 1.2152 0.226325 0.113163 Geslacht -0.425106954614593 0.231544 -1.836 0.068455 0.034227 Leeftijd 1.74847731530155 0.984292 1.7764 0.077812 0.038906 Groepsgevoel 0.0326680938365645 0.011769 2.7759 0.006247 0.003124 Multiple Linear Regression - Regression Statistics Multiple R 0.283951170942918 R-squared 0.080628267479854 Adjusted R-squared 0.0612049210181609 F-TEST (value) 4.15110071989239 F-TEST (DF numerator) 3 F-TEST (DF denominator) 142 p-value 0.00745610775654038 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.35117042995307 Sum Squared Residuals 259.243937370698 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 6 4.80010309071404 1.19989690928596 2 4 4.83319291928986 -0.83319291928986 3 5 5.74789954671366 -0.747899546713665 4 4 5.06186957614581 -1.06186957614581 5 4 5.19254195149207 -1.19254195149207 6 6 5.58455907753084 0.415440922469157 7 6 5.22521004532863 0.774789954671366 8 4 4.80010309071404 -0.80010309071404 9 4 5.22521004532863 -1.22521004532863 10 6 4.63718435627047 1.36281564372953 11 4 4.93119720079955 -0.931197200799553 12 6 5.68256335904054 0.317436640959464 13 5 5.02920148230925 -0.0292014823092468 14 4 5.35588242067489 -1.35588242067489 15 6 5.42079687360877 0.579203126391235 16 3 4.57142643385809 -1.57142643385809 17 5 5.42121860834802 -0.42121860834802 18 6 5.35588242067489 0.644117579325108 19 4 5.06144784140656 -1.06144784140656 20 6 4.9307754660603 1.06922453393970 21 2 4.73518863778017 -2.73518863778017 22 7 4.9307754660603 2.0692245339397 23 5 5.38855051451146 -0.388550514511456 24 2 4.99611165373343 -2.99611165373343 25 4 4.7025205439436 -0.702520543943602 26 4 4.89810737222373 -0.898107372223733 27 6 5.02877974756999 0.971220252430009 28 6 5.22521004532863 0.774789954671366 29 5 5.02877974756999 -0.0287797475699915 30 6 4.96386529463612 1.03613470536388 31 6 5.19212021675281 0.807879783247186 32 4 5.78056764055023 -1.78056764055023 33 6 5.12678402907969 0.873215970920315 34 6 5.58455907753084 0.415440922469157 35 6 5.64989526520397 0.350104734796028 36 2 4.96344355989686 -2.96344355989686 37 4 5.22478831058938 -1.22478831058938 38 5 5.02920148230925 -0.0292014823092468 39 3 5.45388670218458 -2.45388670218458 40 7 4.9307754660603 2.0692245339397 41 5 4.44117579325109 0.558824206748914 42 3 5.48655479602115 -2.48655479602115 43 8 5.29054623300176 2.70945376699824 44 8 5.78056764055023 2.21943235944977 45 5 4.66943071536778 0.330569284632218 46 6 5.22478831058938 0.775211689410622 47 3 4.63676262153122 -1.63676262153122 48 5 5.45346496744533 -0.453464967445329 49 4 4.73476690304091 -0.734766903040911 50 5 5.51922288985771 -0.519222889857714 51 5 4.57142643385809 0.428573566141911 52 6 5.32279259209907 0.677207407900928 53 5 5.06186957614581 -0.0618695761458112 54 6 5.32321432683833 0.676785673161673 55 6 5.02920148230925 0.970798517690753 56 4 4.57142643385809 -0.571426433858089 57 8 5.7152314528771 2.2847685471229 58 6 4.76743499687748 1.23256500312252 59 4 4.60409452769465 -0.604094527694654 60 6 4.60409452769465 1.39590547230535 61 5 4.11407312014619 0.885926879853813 62 5 5.51922288985771 -0.519222889857714 63 6 5.3881287797722 0.6118712202278 64 6 5.58455907753084 0.415440922469157 65 6 4.73476690304091 1.26523309695909 66 6 4.40808596467527 1.59191403532473 67 6 4.99611165373343 1.00388834626657 68 6 4.89852910696299 1.10147089303701 69 7 4.83277118455061 2.16722881544940 70 4 5.29012449826251 -1.29012449826251 71 4 5.45388670218458 -1.45388670218458 72 3 4.8327711845506 -1.83277118455060 73 6 4.99611165373343 1.00388834626657 74 5 5.32321432683833 -0.323214326838327 75 5 5.38855051451146 -0.388550514511456 76 3 5.15945212291625 -2.15945212291625 77 5 5.12720576381894 -0.127205763818940 78 4 4.24474549549244 -0.244745495492444 79 3 5.06144784140656 -2.06144784140656 80 7 5.55189098369428 1.44810901630572 81 4 4.76743499687748 -0.767434996877476 82 4 5.19254195149207 -1.19254195149207 83 5 4.89810737222373 0.101892627776266 84 6 5.38855051451146 0.611449485488544 85 2 5.29054623300176 -3.29054623300176 86 2 4.24474549549244 -2.24474549549244 87 6 5.87857192205992 0.121428077940077 88 4 4.70209880920435 -0.702098809204347 89 5 5.15945212291625 -0.159452122916249 90 6 4.76785673161673 1.23214326838327 91 7 5.45388670218458 1.54611329781542 92 8 5.12720576381894 2.87279423618106 93 6 4.83319291928986 1.16680708071014 94 6 4.89852910696299 1.10147089303701 95 3 4.83319291928986 -1.83319291928986 96 7 5.06186957614581 1.93813042385419 97 3 5.35588242067489 -2.35588242067489 98 6 4.86543927838717 1.13456072161283 99 4 4.70209880920435 -0.702098809204347 100 4 5.55189098369428 -1.55189098369428 101 6 5.32321432683833 0.676785673161673 102 6 5.38855051451146 0.611449485488544 103 6 6.09164882704294 -0.0916488270429445 104 4 4.83319291928986 -0.83319291928986 105 7 6.90835117295706 0.0916488270429442 106 5 5.35588242067489 -0.355882420674891 107 7 5.55189098369428 1.44810901630572 108 4 5.38855051451146 -1.38855051451146 109 6 5.02877974756999 0.971220252430009 110 6 5.7152314528771 0.284768547122899 111 6 4.7025205439436 1.29747945605640 112 5 4.44117579325109 0.558824206748914 113 5 5.22521004532863 -0.225210045328634 114 6 5.58455907753084 0.415440922469157 115 7 5.09453766998238 1.90546233001762 116 4 4.80010309071404 -0.80010309071404 117 4 5.32321432683833 -1.32321432683833 118 8 5.42121860834802 2.57878139165198 119 6 5.2578781391652 0.742121860834802 120 3 4.99653338847268 -1.99653338847268 121 4 5.22521004532863 -1.22521004532863 122 5 5.29054623300176 -0.290546233001762 123 5 4.99611165373343 0.00388834626657304 124 6 5.02877974756999 0.971220252430009 125 8 5.32321432683833 2.67678567316167 126 2 4.93119720079955 -2.93119720079955 127 4 4.44075405851183 -0.440754058511831 128 7 5.42121860834802 1.57878139165198 129 5 5.29054623300176 -0.290546233001762 130 6 5.35546068593564 0.644539314064364 131 6 5.48655479602115 0.513445203978851 132 4 5.42121860834802 -1.42121860834802 133 5 4.80010309071404 0.199896909285960 134 6 5.06186957614581 0.938130423854189 135 6 5.87857192205992 0.121428077940077 136 6 5.29012449826251 0.709875501737493 137 6 5.22478831058938 0.775211689410622 138 5 5.45388670218458 -0.453886702184585 139 5 4.57142643385809 0.428573566141911 140 6 5.1598738576555 0.840126142344495 141 4 5.51922288985771 -1.51922288985771 142 6 5.29054623300176 0.709453766998237 143 3 5.81323573438679 -2.81323573438679 144 6 5.29054623300176 0.709453766998237 145 8 5.29054623300176 2.70945376699824 146 4 5.32321432683833 -1.32321432683833 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.350541819859076 0.701083639718152 0.649458180140924 8 0.389762174166225 0.77952434833245 0.610237825833775 9 0.292558759695661 0.585117519391321 0.70744124030434 10 0.408075725763312 0.816151451526623 0.591924274236688 11 0.327517256633402 0.655034513266804 0.672482743366598 12 0.265088307946824 0.530176615893649 0.734911692053176 13 0.185458714739603 0.370917429479205 0.814541285260397 14 0.164220294790543 0.328440589581086 0.835779705209457 15 0.114842166701418 0.229684333402835 0.885157833298582 16 0.15496427768452 0.30992855536904 0.84503572231548 17 0.106815116732809 0.213630233465617 0.893184883267191 18 0.0912400465156263 0.182480093031253 0.908759953484374 19 0.0756017495852126 0.151203499170425 0.924398250414787 20 0.0788975224468117 0.157795044893623 0.921102477553188 21 0.159765077902800 0.319530155805601 0.8402349220972 22 0.239712318070735 0.47942463614147 0.760287681929265 23 0.186453297027853 0.372906594055707 0.813546702972146 24 0.455793706113002 0.911587412226005 0.544206293886998 25 0.392577979782238 0.785155959564476 0.607422020217762 26 0.3454946447843 0.6909892895686 0.6545053552157 27 0.32586663521396 0.65173327042792 0.67413336478604 28 0.309430147256869 0.618860294513737 0.690569852743132 29 0.254518122803208 0.509036245606417 0.745481877196792 30 0.267125822505582 0.534251645011165 0.732874177494418 31 0.233946017556938 0.467892035113875 0.766053982443062 32 0.267865393313795 0.535730786627589 0.732134606686205 33 0.239375699638036 0.478751399276072 0.760624300361964 34 0.204167808485375 0.40833561697075 0.795832191514625 35 0.169227539886036 0.338455079772071 0.830772460113964 36 0.343127941029992 0.686255882059984 0.656872058970008 37 0.328407767933843 0.656815535867687 0.671592232066156 38 0.281542192022647 0.563084384045295 0.718457807977353 39 0.376305660323062 0.752611320646125 0.623694339676938 40 0.468052111432657 0.936104222865314 0.531947888567343 41 0.437129830193532 0.874259660387063 0.562870169806468 42 0.528926090598822 0.942147818802357 0.471073909401178 43 0.712803697477817 0.574392605044366 0.287196302522183 44 0.791660479955411 0.416679040089178 0.208339520044589 45 0.756022233772462 0.487955532455076 0.243977766227538 46 0.727401262166906 0.545197475666188 0.272598737833094 47 0.737682651952426 0.524634696095149 0.262317348047574 48 0.698759759549987 0.602480480900025 0.301240240450013 49 0.662589684691112 0.674820630617775 0.337410315308888 50 0.619993359939896 0.760013280120208 0.380006640060104 51 0.579688656250424 0.840622687499151 0.420311343749576 52 0.542299457517146 0.915401084965707 0.457700542482853 53 0.493245184208077 0.986490368416154 0.506754815791923 54 0.459560281366276 0.919120562732551 0.540439718633724 55 0.44196825841833 0.88393651683666 0.55803174158167 56 0.399554090359915 0.79910818071983 0.600445909640085 57 0.486304397459584 0.972608794919167 0.513695602540416 58 0.482527161338453 0.965054322676905 0.517472838661547 59 0.441162131944100 0.882324263888199 0.5588378680559 60 0.448955441077489 0.897910882154978 0.551044558922511 61 0.425139131101547 0.850278262203095 0.574860868898453 62 0.38369918557045 0.7673983711409 0.61630081442955 63 0.346452409249450 0.692904818498901 0.65354759075055 64 0.306772324252273 0.613544648504545 0.693227675747727 65 0.301054535736590 0.602109071473179 0.69894546426341 66 0.316828049833043 0.633656099666085 0.683171950166957 67 0.297369304289112 0.594738608578224 0.702630695710888 68 0.284271837553513 0.568543675107026 0.715728162446487 69 0.351500696769802 0.703001393539604 0.648499303230198 70 0.346164785648287 0.692329571296573 0.653835214351713 71 0.350587941884248 0.701175883768496 0.649412058115752 72 0.385060521815786 0.770121043631572 0.614939478184214 73 0.365900705823037 0.731801411646075 0.634099294176963 74 0.323735865359084 0.647471730718169 0.676264134640916 75 0.28473825627831 0.56947651255662 0.71526174372169 76 0.345046378735861 0.690092757471722 0.654953621264139 77 0.302034699977697 0.604069399955393 0.697965300022303 78 0.262650377624799 0.525300755249597 0.737349622375201 79 0.313351760873542 0.626703521747084 0.686648239126458 80 0.318127222093641 0.636254444187283 0.681872777906358 81 0.289101910851424 0.578203821702847 0.710898089148576 82 0.278779623260303 0.557559246520606 0.721220376739697 83 0.239513029919045 0.47902605983809 0.760486970080955 84 0.210203673856254 0.420407347712508 0.789796326143746 85 0.411582875792503 0.823165751585006 0.588417124207497 86 0.502068571398665 0.99586285720267 0.497931428601335 87 0.453473286886377 0.906946573772754 0.546526713113623 88 0.424144559804010 0.848289119608019 0.57585544019599 89 0.378900200484623 0.757800400969247 0.621099799515377 90 0.366738368012212 0.733476736024424 0.633261631987788 91 0.377984568940372 0.755969137880745 0.622015431059628 92 0.540635160715388 0.918729678569223 0.459364839284612 93 0.52555197199977 0.948896056000461 0.474448028000231 94 0.50826535647092 0.98346928705816 0.49173464352908 95 0.548571512326088 0.902856975347824 0.451428487673912 96 0.598815218717325 0.80236956256535 0.401184781282675 97 0.695279179015602 0.609441641968795 0.304720820984398 98 0.672486753775117 0.655026492449766 0.327513246224883 99 0.645632039656688 0.708735920686624 0.354367960343312 100 0.663856179668 0.672287640664 0.336143820332 101 0.625157011230574 0.749685977538851 0.374842988769426 102 0.582801544080083 0.834396911839835 0.417198455919917 103 0.52978795863936 0.94042408272128 0.47021204136064 104 0.497613169886668 0.995226339773335 0.502386830113332 105 0.443579199095099 0.887158398190198 0.556420800904901 106 0.394716859786608 0.789433719573215 0.605283140213392 107 0.397785874710215 0.79557174942043 0.602214125289785 108 0.401040651786498 0.802081303572996 0.598959348213502 109 0.364226132126239 0.728452264252478 0.635773867873761 110 0.314253886788142 0.628507773576284 0.685746113211858 111 0.303245797837416 0.606491595674833 0.696754202162584 112 0.265371999135385 0.530743998270771 0.734628000864615 113 0.220889149760843 0.441778299521685 0.779110850239157 114 0.182101838939637 0.364203677879275 0.817898161060363 115 0.223117982926042 0.446235965852083 0.776882017073958 116 0.201015748250832 0.402031496501665 0.798984251749168 117 0.193387675859368 0.386775351718736 0.806612324140632 118 0.311323602171400 0.622647204342799 0.6886763978286 119 0.278325802475793 0.556651604951585 0.721674197524207 120 0.317818758439730 0.635637516879461 0.68218124156027 121 0.301038714761154 0.602077429522308 0.698961285238846 122 0.247454663627055 0.494909327254111 0.752545336372945 123 0.200399696936040 0.400799393872079 0.79960030306396 124 0.164029343021459 0.328058686042918 0.835970656978541 125 0.305952338049694 0.611904676099387 0.694047661950306 126 0.585702518450305 0.82859496309939 0.414297481549695 127 0.609219645300778 0.781560709398443 0.390780354699222 128 0.649082985508672 0.701834028982655 0.350917014491327 129 0.575383650589322 0.849232698821357 0.424616349410678 130 0.514029995408192 0.971940009183615 0.485970004591808 131 0.451479707971572 0.902959415943145 0.548520292028428 132 0.445373139352597 0.890746278705194 0.554626860647403 133 0.379143501037691 0.758287002075381 0.62085649896231 134 0.292071559315751 0.584143118631503 0.707928440684249 135 0.278335159211270 0.556670318422541 0.72166484078873 136 0.23932773374835 0.4786554674967 0.76067226625165 137 0.477723775448662 0.955447550897324 0.522276224551338 138 0.339940362239668 0.679880724479337 0.660059637760332 139 0.209226968766293 0.418453937532586 0.790773031233707 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:

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292341008m2qonps61b6dplf/10gvec1292341060.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292341008m2qonps61b6dplf/10gvec1292341060.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292341008m2qonps61b6dplf/12lz41292341060.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292341008m2qonps61b6dplf/12lz41292341060.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292341008m2qonps61b6dplf/22lz41292341060.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292341008m2qonps61b6dplf/22lz41292341060.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292341008m2qonps61b6dplf/32lz41292341060.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292341008m2qonps61b6dplf/32lz41292341060.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292341008m2qonps61b6dplf/4duy71292341060.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292341008m2qonps61b6dplf/4duy71292341060.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292341008m2qonps61b6dplf/5duy71292341060.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292341008m2qonps61b6dplf/5duy71292341060.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292341008m2qonps61b6dplf/6duy71292341060.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292341008m2qonps61b6dplf/6duy71292341060.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292341008m2qonps61b6dplf/754xs1292341060.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292341008m2qonps61b6dplf/754xs1292341060.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292341008m2qonps61b6dplf/8gvec1292341060.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292341008m2qonps61b6dplf/8gvec1292341060.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292341008m2qonps61b6dplf/9gvec1292341060.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292341008m2qonps61b6dplf/9gvec1292341060.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

Copyright

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

Software written by Ed van Stee & Patrick Wessa

Disclaimer

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

Privacy Policy

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

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

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

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

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

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

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

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

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

FreeStatistics.org is powered by