w7 3

*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 19:27:20 +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/t1290453992i8ym080pgurf738.htm/, Retrieved Mon, 22 Nov 2010 20:26:44 +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/t1290453992i8ym080pgurf738.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 10 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation ConcernoverMistakes[t] = -20.5406175778355 + 1.85205065543316Month[t] + 0.800753683595022Doubtsaboutactions[t] + 0.233892111076656ParentalExpectations[t] + 0.208449774615658ParentalCriticism[t] + 0.571250810176502PersonalStandards[t] -0.108334921483046Organization[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) -20.5406175778355 19.256187 -1.0667 0.287798 0.143899 Month 1.85205065543316 1.896285 0.9767 0.330283 0.165141 Doubtsaboutactions 0.800753683595022 0.130707 6.1263 0 0 ParentalExpectations 0.233892111076656 0.133964 1.7459 0.082844 0.041422 ParentalCriticism 0.208449774615658 0.169517 1.2297 0.22072 0.11036 PersonalStandards 0.571250810176502 0.095975 5.9521 0 0 Organization -0.108334921483046 0.103317 -1.0486 0.296039 0.148019 Multiple Linear Regression - Regression Statistics Multiple R 0.64099331025021 R-squared 0.410872423785522 Adjusted R-squared 0.387617387882319 F-TEST (value) 17.6681053297763 F-TEST (DF numerator) 6 F-TEST (DF denominator) 152 p-value 1.77635683940025e-15 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.47838506122403 Sum Squared Residuals 3048.50177900237 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 24 23.3059118943018 0.694088105698213 2 25 20.0305388753726 4.96946112462742 3 17 21.0052757760804 -4.00527577608043 4 18 18.1054640311386 -0.105464031138574 5 18 17.7257754099891 0.274224590010878 6 16 17.8270340131174 -1.82703401311742 7 20 20.9669353500886 -0.96693535008862 8 16 22.5176755215724 -6.51767552157242 9 18 22.3856971696195 -4.3856971696195 10 17 20.5758193389779 -3.57581933897792 11 23 22.4193009914529 0.580699008547053 12 30 22.3194415096782 7.6805584903218 13 23 15.2286232932766 7.77137670672336 14 18 19.1104416279066 -1.11044162790658 15 15 21.7839912762424 -6.7839912762424 16 12 17.9154785487290 -5.91547854872898 17 21 19.5685684943043 1.43143150569570 18 15 15.3238221486993 -0.323822148699278 19 20 19.788712464159 0.211287535840979 20 31 26.4316241603005 4.56837583969953 21 27 25.3037907067212 1.69620929327880 22 34 27.1245220182142 6.87547798178583 23 21 19.7497407961377 1.25025920386235 24 31 20.9877712435853 10.0122287564147 25 19 19.4202293168146 -0.420229316814625 26 16 20.3090786014135 -4.30907860141353 27 20 21.6269845815146 -1.62698458151458 28 21 18.3570448221492 2.64295517785077 29 22 22.0233101277448 -0.0233101277447521 30 17 19.5537828503148 -2.55378285031481 31 24 20.4827109912344 3.51728900876556 32 25 30.5896852562428 -5.58968525624278 33 26 26.6567312769232 -0.656731276923215 34 25 24.2234250890577 0.776574910942284 35 17 23.045122891964 -6.045122891964 36 32 27.8972410979597 4.10275890204028 37 33 23.7555615557403 9.2444384442597 38 13 21.9687976203882 -8.96879762038819 39 32 27.9120428867192 4.08795711328085 40 25 25.9293572358744 -0.92935723587439 41 29 27.0874697124605 1.91253028753948 42 22 22.1267767157679 -0.126776715767942 43 18 17.3192233527807 0.680776647219285 44 17 21.7607618394586 -4.76076183945862 45 20 22.3547629906172 -2.35476299061719 46 15 20.5459877648588 -5.54598776485879 47 20 22.2319043156402 -2.23190431564015 48 33 28.2261560042653 4.77384399573473 49 29 22.251296402594 6.748703597406 50 23 26.6319917607078 -3.63199176070780 51 26 23.3339380863289 2.66606191367114 52 18 19.0572045568099 -1.05720455680992 53 20 18.8533677442963 1.14663225570374 54 11 11.8569662519076 -0.856966251907644 55 28 29.1391504790434 -1.13915047904336 56 26 23.4322971143043 2.56770288569571 57 22 22.3892075268383 -0.389207526838316 58 17 20.2377578196325 -3.23775781963249 59 12 15.5903486600183 -3.59034866001829 60 14 20.9635424700856 -6.96354247008556 61 17 20.8710718404720 -3.87107184047204 62 21 21.3887032327798 -0.388703232779788 63 19 22.9992127997300 -3.99921279972996 64 18 23.2468530520286 -5.24685305202855 65 10 17.9645501066114 -7.96455010661136 66 29 24.3941848397898 4.60581516021023 67 31 18.5938095723147 12.4061904276853 68 19 23.0436062933947 -4.04360629339472 69 9 20.1246989779693 -11.1246989779693 70 20 22.6014881321463 -2.60148813214627 71 28 17.6934201313888 10.3065798686112 72 19 18.2554133704473 0.744586629552658 73 30 23.1781656827485 6.82183431725153 74 29 27.141265190523 1.85873480947699 75 26 21.5186496600315 4.48135033996846 76 23 19.6117121946776 3.38828780532242 77 13 22.7696306151473 -9.76963061514733 78 21 22.7069504145405 -1.70695041454046 79 19 21.5925083983795 -2.59250839837945 80 28 22.9773775695174 5.0226224304826 81 23 25.6717631113499 -2.67176311134987 82 18 13.9193783112761 4.0806216887239 83 21 20.7675259412847 0.232474058715270 84 20 21.8939221074588 -1.89392210745884 85 23 20.0325488218293 2.96745117817070 86 21 20.8145797835144 0.185420216485572 87 21 21.8549708563638 -0.854970856363823 88 15 22.9601236544928 -7.96012365449284 89 28 27.2899074036207 0.710092596379342 90 19 17.6895099895324 1.31049001046759 91 26 21.2437539562323 4.75624604376766 92 10 13.3592202085389 -3.35922020853889 93 16 17.1550179477001 -1.15501794770007 94 22 21.1473255205123 0.852674479487721 95 19 18.9152072305154 0.0847927694845566 96 31 28.8638754075329 2.13612459246710 97 31 25.230379417261 5.769620582739 98 29 24.8223758205617 4.17762417943829 99 19 17.4600071161139 1.53999288388611 100 22 18.9197725283673 3.08022747163268 101 23 22.4440405076684 0.555959492331634 102 15 16.2213007844847 -1.22130078448470 103 20 21.4067774452657 -1.40677744526569 104 18 19.5916965986423 -1.59169659864227 105 23 22.1310549953480 0.868945004652039 106 25 20.8917425925028 4.10825740749719 107 21 16.6248088962977 4.37519110370226 108 24 19.5405088901123 4.4594911098877 109 25 25.2842542064876 -0.28425420648761 110 17 19.5415733289438 -2.54157332894384 111 13 14.5909200347790 -1.59092003477896 112 28 18.3120659854546 9.68793401454535 113 21 20.3272159802936 0.672784019706407 114 25 28.2402549727791 -3.24025497277913 115 9 21.0230657884603 -12.0230657884603 116 16 17.8649936604446 -1.86499366044458 117 19 21.1927565169446 -2.19275651694460 118 17 19.5105598387774 -2.51055983877741 119 25 24.5423498928072 0.457650107192842 120 20 15.5110220361322 4.4889779638678 121 29 21.7283761640027 7.27162383599734 122 14 19.0319382805427 -5.03193828054267 123 22 26.9807843689066 -4.98078436890655 124 15 15.7566129258641 -0.756612925864103 125 19 25.4706403155968 -6.47064031559684 126 20 21.9365074008663 -1.93650740086629 127 15 17.5261183627755 -2.52611836277546 128 20 21.9219024004867 -1.92190240048672 129 18 20.3175223942078 -2.31752239420782 130 33 25.5749999931078 7.42500000689216 131 22 23.8817153183514 -1.88171531835137 132 16 16.5259552703765 -0.525955270376549 133 17 19.1237948992732 -2.12379489927320 134 16 15.1155579324758 0.884442067524158 135 21 17.1100391110055 3.88996088899452 136 26 27.6211796228832 -1.62117962288317 137 18 21.1341578075379 -3.13415780753787 138 18 23.0934916296008 -5.0934916296008 139 17 18.4649006478306 -1.46490064783056 140 22 24.8487332677922 -2.84873326779221 141 30 24.7935972513542 5.20640274864582 142 30 27.3647178144344 2.63528218556562 143 24 29.8857645038554 -5.88576450385543 144 21 22.0669167791498 -1.06691677914980 145 21 25.4142006337394 -4.41420063373936 146 29 27.4391935427261 1.56080645727388 147 31 23.2617927349301 7.7382072650699 148 20 19.0482023570498 0.95179764295021 149 16 14.1735795557976 1.82642044420244 150 22 19.0032235203552 2.99677647964479 151 20 20.5031787470075 -0.503178747007479 152 28 27.3118012168112 0.688198783188795 153 38 26.583623023071 11.416376976929 154 22 19.2656066671755 2.73439333282454 155 20 25.7191898021532 -5.71918980215318 156 17 18.0567002627514 -1.05670026275140 157 28 24.5408332942379 3.45916670576212 158 22 24.1667158267939 -2.16671582679388 159 31 26.1156597616631 4.8843402383369 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.0970670411834892 0.194134082366978 0.90293295881651 11 0.352393731523614 0.704787463047228 0.647606268476386 12 0.74156617788104 0.516867644237921 0.258433822118961 13 0.754697183161802 0.490605633676397 0.245302816838198 14 0.725448476799824 0.549103046400353 0.274551523200176 15 0.722233126029996 0.555533747940007 0.277766873970004 16 0.652328725037223 0.695342549925554 0.347671274962777 17 0.572386757170525 0.85522648565895 0.427613242829475 18 0.541859497637909 0.916281004724183 0.458140502362091 19 0.474676882035404 0.949353764070808 0.525323117964596 20 0.505842719187053 0.988314561625894 0.494157280812947 21 0.459536798899327 0.919073597798655 0.540463201100673 22 0.461502265611502 0.923004531223005 0.538497734388498 23 0.395967432341981 0.791934864683963 0.604032567658019 24 0.650396796150225 0.699206407699549 0.349603203849775 25 0.581470407835877 0.837059184328246 0.418529592164123 26 0.55296922089467 0.894061558210659 0.447030779105330 27 0.489233382874032 0.978466765748064 0.510766617125968 28 0.439443819618758 0.878887639237516 0.560556180381242 29 0.375597317633609 0.751194635267219 0.624402682366391 30 0.320272790689224 0.640545581378449 0.679727209310776 31 0.362962080075657 0.725924160151314 0.637037919924343 32 0.438468280861996 0.876936561723991 0.561531719138004 33 0.392390874340821 0.784781748681642 0.607609125659179 34 0.386176222459661 0.772352444919322 0.613823777540339 35 0.402482234718931 0.804964469437862 0.597517765281069 36 0.383170354519848 0.766340709039697 0.616829645480152 37 0.539788976030688 0.920422047938623 0.460211023969312 38 0.737672203095431 0.524655593809138 0.262327796904569 39 0.728582167918444 0.542835664163112 0.271417832081556 40 0.686842139765152 0.626315720469695 0.313157860234848 41 0.657662002555665 0.684675994888669 0.342337997444335 42 0.605746714932371 0.788506570135258 0.394253285067629 43 0.556564012347508 0.886871975304983 0.443435987652492 44 0.54185576836344 0.91628846327312 0.45814423163656 45 0.515587584987365 0.96882483002527 0.484412415012635 46 0.546926613300941 0.906146773398119 0.453073386699059 47 0.506503583740721 0.986992832518558 0.493496416259279 48 0.491527670012513 0.983055340025027 0.508472329987487 49 0.547042409487488 0.905915181025023 0.452957590512512 50 0.522499161639747 0.955001676720506 0.477500838360253 51 0.485541443518318 0.971082887036636 0.514458556481682 52 0.436830760327895 0.87366152065579 0.563169239672105 53 0.394804059908958 0.789608119817916 0.605195940091042 54 0.350259686286477 0.700519372572954 0.649740313713523 55 0.311852434600522 0.623704869201044 0.688147565399478 56 0.282223187865935 0.56444637573187 0.717776812134065 57 0.241965379222739 0.483930758445478 0.758034620777261 58 0.220249249740723 0.440498499481446 0.779750750259277 59 0.206116926157581 0.412233852315163 0.793883073842419 60 0.258733346736539 0.517466693473079 0.74126665326346 61 0.252500930293199 0.505001860586397 0.747499069706801 62 0.216592098711133 0.433184197422265 0.783407901288867 63 0.205460321136168 0.410920642272335 0.794539678863832 64 0.246312726510432 0.492625453020864 0.753687273489568 65 0.321923587485327 0.643847174970654 0.678076412514673 66 0.320250691587816 0.640501383175632 0.679749308412184 67 0.642022353647023 0.715955292705953 0.357977646352977 68 0.63720012517777 0.72559974964446 0.36279987482223 69 0.826601996467502 0.346796007064996 0.173398003532498 70 0.806821968698155 0.386356062603690 0.193178031301845 71 0.91941616535811 0.16116766928378 0.08058383464189 72 0.900975203372913 0.198049593254174 0.0990247966270872 73 0.925424652008945 0.149150695982111 0.0745753479910554 74 0.91183008782475 0.176339824350499 0.0881699121752496 75 0.913533321913399 0.172933356173203 0.0864666780866014 76 0.90561310271734 0.188773794565321 0.0943868972826603 77 0.96270940944963 0.0745811811007389 0.0372905905503694 78 0.953936011198765 0.0921279776024695 0.0460639888012347 79 0.945625289630345 0.10874942073931 0.054374710369655 80 0.948571897875523 0.102856204248954 0.0514281021244768 81 0.93991598590597 0.120168028188062 0.060084014094031 82 0.938515674949129 0.122968650101742 0.061484325050871 83 0.923338531636765 0.153322936726471 0.0766614683632353 84 0.90882303667518 0.18235392664964 0.09117696332482 85 0.898561577993249 0.202876844013503 0.101438422006751 86 0.881092730286874 0.237814539426252 0.118907269713126 87 0.857342437349597 0.285315125300805 0.142657562650403 88 0.90926576745457 0.181468465090858 0.0907342325454292 89 0.89123239772767 0.217535204544659 0.108767602272329 90 0.86929129928284 0.261417401434320 0.130708700717160 91 0.871560538888933 0.256878922222135 0.128439461111067 92 0.860819427534191 0.278361144931617 0.139180572465809 93 0.837228729993419 0.325542540013162 0.162771270006581 94 0.807972735470317 0.384054529059365 0.192027264529683 95 0.773282094046861 0.453435811906278 0.226717905953139 96 0.742048450518324 0.515903098963352 0.257951549481676 97 0.762131802224181 0.475736395551638 0.237868197775819 98 0.75606062493066 0.48787875013868 0.24393937506934 99 0.719275137739433 0.561449724521133 0.280724862260567 100 0.693707554077025 0.612584891845951 0.306292445922975 101 0.649913424931441 0.700173150137117 0.350086575068559 102 0.610430415302474 0.779139169395052 0.389569584697526 103 0.570858211782588 0.858283576434824 0.429141788217412 104 0.528404565927371 0.943190868145257 0.471595434072629 105 0.481823890237935 0.96364778047587 0.518176109762065 106 0.462415919556106 0.924831839112212 0.537584080443894 107 0.457827969709525 0.91565593941905 0.542172030290475 108 0.467894611120809 0.935789222241619 0.53210538887919 109 0.418048471439914 0.83609694287983 0.581951528560086 110 0.394670984620236 0.789341969240473 0.605329015379764 111 0.355851640083466 0.711703280166933 0.644148359916534 112 0.575223439591521 0.849553120816957 0.424776560408479 113 0.567343870842545 0.86531225831491 0.432656129157455 114 0.539389429135855 0.921221141728289 0.460610570864145 115 0.757170297981752 0.485659404036497 0.242829702018248 116 0.732994701241805 0.53401059751639 0.267005298758195 117 0.720593949600064 0.558812100799872 0.279406050399936 118 0.691732727760039 0.616534544479923 0.308267272239961 119 0.640849052583183 0.718301894833634 0.359150947416817 120 0.649751710913055 0.700496578173889 0.350248289086945 121 0.702047164437651 0.595905671124697 0.297952835562349 122 0.706619746256259 0.586760507487482 0.293380253743741 123 0.712383551522526 0.575232896954948 0.287616448477474 124 0.659179031869674 0.681641936260653 0.340820968130326 125 0.705189245566205 0.58962150886759 0.294810754433795 126 0.675340670345473 0.649318659309054 0.324659329654527 127 0.663677575151714 0.672644849696571 0.336322424848286 128 0.627186208535115 0.74562758292977 0.372813791464885 129 0.599799802557953 0.800400394884094 0.400200197442047 130 0.670663902252595 0.65867219549481 0.329336097747405 131 0.61297507122568 0.774049857548639 0.387024928774320 132 0.548631249414214 0.902737501171572 0.451368750585786 133 0.546504527371684 0.906990945256632 0.453495472628316 134 0.485063485107997 0.970126970215995 0.514936514892003 135 0.442524241104513 0.885048482209025 0.557475758895487 136 0.41362928955152 0.82725857910304 0.58637071044848 137 0.417216958397858 0.834433916795716 0.582783041602142 138 0.457091421954854 0.914182843909709 0.542908578045145 139 0.389298744105466 0.778597488210933 0.610701255894534 140 0.317194646521706 0.634389293043413 0.682805353478294 141 0.419416128333852 0.838832256667705 0.580583871666148 142 0.365062614194955 0.730125228389909 0.634937385805045 143 0.296876857386553 0.593753714773106 0.703123142613447 144 0.222858355679169 0.445716711358338 0.777141644320831 145 0.266401707577565 0.53280341515513 0.733598292422435 146 0.183729503285999 0.367459006571999 0.816270496714 147 0.238719425674762 0.477438851349525 0.761280574325238 148 0.147579571272891 0.295159142545783 0.852420428727109 149 0.0789849424412949 0.157969884882590 0.921015057558705 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 0.0142857142857143 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/22/t1290453992i8ym080pgurf738/10are81290454029.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290453992i8ym080pgurf738/10are81290454029.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290453992i8ym080pgurf738/1wzyy1290454029.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290453992i8ym080pgurf738/1wzyy1290454029.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290453992i8ym080pgurf738/2wzyy1290454029.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290453992i8ym080pgurf738/2wzyy1290454029.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290453992i8ym080pgurf738/3wzyy1290454029.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290453992i8ym080pgurf738/3wzyy1290454029.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290453992i8ym080pgurf738/4wzyy1290454029.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290453992i8ym080pgurf738/4wzyy1290454029.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290453992i8ym080pgurf738/5p9xk1290454029.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290453992i8ym080pgurf738/5p9xk1290454029.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290453992i8ym080pgurf738/6p9xk1290454029.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290453992i8ym080pgurf738/6p9xk1290454029.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290453992i8ym080pgurf738/70ixn1290454029.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290453992i8ym080pgurf738/70ixn1290454029.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290453992i8ym080pgurf738/80ixn1290454029.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290453992i8ym080pgurf738/80ixn1290454029.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290453992i8ym080pgurf738/9are81290454029.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290453992i8ym080pgurf738/9are81290454029.ps (open in new window)

Parameters (Session):

par1 = 4 ; 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') }

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