ws7- mini tut

*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: Sat, 20 Nov 2010 12:35:54 +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/20/t129025648941t8dw1dkrmn50f.htm/, Retrieved Sat, 20 Nov 2010 13:34:52 +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/20/t129025648941t8dw1dkrmn50f.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 9 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation ConcernoverMistakes[t] = -20.5406175778344 + 1.85205065543305Month[t] + 0.800753683595023Doubtsaboutactions[t] + 0.233892111076654ParentalExpectations[t] + 0.20844977461566ParentalCriticism[t] + 0.571250810176502PersonalStandards[t] -0.108334921483045Organization[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) -20.5406175778344 19.256187 -1.0667 0.287798 0.143899 Month 1.85205065543305 1.896285 0.9767 0.330283 0.165141 Doubtsaboutactions 0.800753683595023 0.130707 6.1263 0 0 ParentalExpectations 0.233892111076654 0.133964 1.7459 0.082844 0.041422 ParentalCriticism 0.20844977461566 0.169517 1.2297 0.22072 0.11036 PersonalStandards 0.571250810176502 0.095975 5.9521 0 0 Organization -0.108334921483045 0.103317 -1.0486 0.296039 0.148019 Multiple Linear Regression - Regression Statistics Multiple R 0.64099331025021 R-squared 0.410872423785522 Adjusted R-squared 0.387617387882318 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 23.3059118943013 0.694088105698661 2 25 20.0305388753727 4.9694611246273 3 17 21.0052757760805 -4.00527577608054 4 18 18.1054640311387 -0.105464031138674 5 18 17.7257754099892 0.274224590010765 6 16 17.8270340131175 -1.82703401311751 7 20 20.9669353500886 -0.96693535008861 8 16 22.5176755215724 -6.51767552157243 9 18 22.3856971696195 -4.38569716961951 10 17 20.5758193389779 -3.57581933897792 11 23 22.419300991453 0.580699008547041 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 17.915478548729 -5.91547854872895 17 21 19.5685684943043 1.4314315056957 18 15 15.3238221486993 -0.323822148699272 19 20 19.788712464159 0.21128753584099 20 31 26.4316241603005 4.56837583969953 21 27 25.3037907067212 1.69620929327879 22 34 27.1245220182142 6.87547798178583 23 21 19.7497407961377 1.25025920386234 24 31 20.9877712435853 10.0122287564147 25 19 19.4202293168146 -0.420229316814631 26 16 20.3090786014135 -4.30907860141352 27 20 21.6269845815146 -1.62698458151457 28 21 18.3570448221492 2.64295517785077 29 22 22.0233101277447 -0.0233101277447478 30 17 19.5537828503148 -2.5537828503148 31 24 20.4827109912344 3.51728900876556 32 25 30.5896852562428 -5.5896852562428 33 26 26.6567312769232 -0.656731276923196 34 25 24.2234250890577 0.77657491094229 35 17 23.045122891964 -6.04512289196399 36 32 27.8972410979597 4.10275890204028 37 33 23.7555615557403 9.24443844425972 38 13 21.9687976203882 -8.96879762038819 39 32 27.9120428867192 4.08795711328084 40 25 25.9293572358744 -0.929357235874388 41 29 27.0874697124605 1.91253028753948 42 22 22.1267767157679 -0.126776715767941 43 18 17.3192233527807 0.68077664721929 44 17 21.7607618394586 -4.76076183945861 45 20 22.3547629906172 -2.35476299061719 46 15 20.5459877648588 -5.54598776485879 47 20 22.2319043156402 -2.23190431564016 48 33 28.2261560042653 4.77384399573473 49 29 22.251296402594 6.748703597406 50 23 26.6319917607078 -3.63199176070779 51 26 23.3339380863289 2.66606191367113 52 18 19.0572045568099 -1.05720455680992 53 20 18.8533677442963 1.14663225570374 54 11 11.8569662519076 -0.856966251907634 55 28 29.1391504790434 -1.13915047904337 56 26 23.4322971143043 2.56770288569571 57 22 22.3892075268383 -0.389207526838317 58 17 20.2377578196325 -3.23775781963248 59 12 15.5903486600183 -3.59034866001828 60 14 20.9635424700856 -6.96354247008556 61 17 20.871071840472 -3.87107184047204 62 21 21.3887032327798 -0.388703232779782 63 19 22.99921279973 -3.99921279972996 64 18 23.2468530520286 -5.24685305202856 65 10 17.9645501066113 -7.96455010661135 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 6.82183431725154 74 29 27.141265190523 1.85873480947699 75 26 21.5186496600315 4.48135033996848 76 23 19.6117121946776 3.38828780532242 77 13 22.7696306151473 -9.76963061514733 78 21 22.7069504145405 -1.70695041454046 79 19 21.5925083983795 -2.59250839837946 80 28 22.9773775695174 5.0226224304826 81 23 25.6717631113499 -2.67176311134988 82 18 13.9193783112761 4.0806216887239 83 21 20.7675259412847 0.232474058715269 84 20 21.8939221074588 -1.89392210745884 85 23 20.0325488218293 2.9674511781707 86 21 20.8145797835144 0.185420216485577 87 21 21.8549708563638 -0.854970856363817 88 15 22.9601236544929 -7.96012365449285 89 28 27.2899074036207 0.710092596379332 90 19 17.6895099895324 1.31049001046759 91 26 21.2437539562323 4.75624604376766 92 10 13.3592202085389 -3.35922020853889 93 16 17.1550179477001 -1.15501794770006 94 22 21.1473255205123 0.852674479487732 95 19 18.9152072305155 0.084792769484549 96 31 28.8638754075329 2.1361245924671 97 31 25.230379417261 5.76962058273899 98 29 24.8223758205617 4.17762417943828 99 19 17.4600071161139 1.53999288388611 100 22 18.9197725283673 3.08022747163268 101 23 22.4440405076684 0.555959492331635 102 15 16.2213007844847 -1.22130078448468 103 20 21.4067774452657 -1.40677744526568 104 18 19.5916965986423 -1.59169659864227 105 23 22.131054995348 0.868945004652024 106 25 20.8917425925028 4.1082574074972 107 21 16.6248088962977 4.37519110370226 108 24 19.5405088901123 4.4594911098877 109 25 25.2842542064876 -0.284254206487617 110 17 19.5415733289438 -2.54157332894384 111 13 14.590920034779 -1.59092003477895 112 28 18.3120659854546 9.68793401454535 113 21 20.3272159802936 0.6727840197064 114 25 28.2402549727791 -3.24025497277912 115 9 21.0230657884603 -12.0230657884603 116 16 17.8649936604446 -1.86499366044457 117 19 21.1927565169446 -2.19275651694458 118 17 19.5105598387774 -2.51055983877741 119 25 24.5423498928072 0.457650107192838 120 20 15.5110220361322 4.48897796386781 121 29 21.7283761640027 7.27162383599735 122 14 19.0319382805427 -5.03193828054266 123 22 26.9807843689066 -4.98078436890656 124 15 15.7566129258641 -0.756612925864104 125 19 25.4706403155968 -6.47064031559685 126 20 21.9365074008663 -1.93650740086628 127 15 17.5261183627754 -2.52611836277545 128 20 21.9219024004867 -1.92190240048673 129 18 20.3175223942078 -2.31752239420782 130 33 25.5749999931078 7.42500000689215 131 22 23.8817153183514 -1.88171531835138 132 16 16.5259552703765 -0.525955270376547 133 17 19.1237948992732 -2.1237948992732 134 16 15.1155579324758 0.884442067524166 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 -2.84873326779222 141 30 24.7935972513542 5.20640274864581 142 30 27.3647178144344 2.63528218556562 143 24 29.8857645038554 -5.88576450385544 144 21 22.0669167791498 -1.06691677914981 145 21 25.4142006337394 -4.41420063373936 146 29 27.4391935427261 1.56080645727388 147 31 23.2617927349301 7.73820726506992 148 20 19.0482023570498 0.951797642950214 149 16 14.1735795557976 1.82642044420244 150 22 19.0032235203552 2.9967764796448 151 20 20.5031787470075 -0.503178747007477 152 28 27.3118012168112 0.688198783188794 153 38 26.583623023071 11.416376976929 154 22 19.2656066671754 2.73439333282455 155 20 25.7191898021532 -5.71918980215319 156 17 18.0567002627514 -1.05670026275139 157 28 24.5408332942379 3.45916670576211 158 22 24.1667158267939 -2.16671582679389 159 31 26.1156597616631 4.88434023833688 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.0970670411834895 0.194134082366979 0.90293295881651 11 0.352393731523613 0.704787463047226 0.647606268476387 12 0.74156617788104 0.51686764423792 0.25843382211896 13 0.754697183161802 0.490605633676396 0.245302816838198 14 0.725448476799823 0.549103046400353 0.274551523200177 15 0.722233126029996 0.555533747940007 0.277766873970004 16 0.652328725037222 0.695342549925555 0.347671274962778 17 0.572386757170525 0.85522648565895 0.427613242829475 18 0.541859497637909 0.916281004724181 0.458140502362091 19 0.474676882035403 0.949353764070806 0.525323117964597 20 0.505842719187052 0.988314561625896 0.494157280812948 21 0.459536798899325 0.91907359779865 0.540463201100675 22 0.461502265611502 0.923004531223005 0.538497734388498 23 0.395967432341982 0.791934864683964 0.604032567658018 24 0.650396796150225 0.69920640769955 0.349603203849775 25 0.581470407835878 0.837059184328244 0.418529592164122 26 0.552969220894671 0.894061558210657 0.447030779105329 27 0.489233382874032 0.978466765748064 0.510766617125968 28 0.439443819618758 0.878887639237515 0.560556180381242 29 0.37559731763361 0.75119463526722 0.624402682366391 30 0.320272790689225 0.640545581378449 0.679727209310775 31 0.362962080075656 0.725924160151311 0.637037919924344 32 0.438468280861995 0.87693656172399 0.561531719138005 33 0.392390874340821 0.784781748681642 0.607609125659179 34 0.386176222459661 0.772352444919323 0.613823777540339 35 0.402482234718931 0.804964469437862 0.597517765281069 36 0.383170354519849 0.766340709039697 0.616829645480152 37 0.539788976030688 0.920422047938623 0.460211023969312 38 0.73767220309543 0.524655593809139 0.26232779690457 39 0.728582167918444 0.542835664163112 0.271417832081556 40 0.686842139765152 0.626315720469696 0.313157860234848 41 0.657662002555667 0.684675994888666 0.342337997444333 42 0.605746714932373 0.788506570135253 0.394253285067627 43 0.556564012347503 0.886871975304993 0.443435987652497 44 0.541855768363441 0.916288463273118 0.458144231636559 45 0.515587584987365 0.96882483002527 0.484412415012635 46 0.546926613300943 0.906146773398114 0.453073386699057 47 0.506503583740718 0.986992832518563 0.493496416259282 48 0.491527670012515 0.98305534002503 0.508472329987485 49 0.54704240948749 0.90591518102502 0.45295759051251 50 0.522499161639749 0.955001676720502 0.477500838360251 51 0.485541443518318 0.971082887036637 0.514458556481682 52 0.436830760327896 0.873661520655792 0.563169239672104 53 0.394804059908957 0.789608119817915 0.605195940091043 54 0.350259686286478 0.700519372572955 0.649740313713522 55 0.311852434600522 0.623704869201044 0.688147565399478 56 0.282223187865935 0.56444637573187 0.717776812134065 57 0.24196537922274 0.483930758445479 0.75803462077726 58 0.220249249740724 0.440498499481447 0.779750750259276 59 0.206116926157581 0.412233852315162 0.79388307384242 60 0.258733346736541 0.517466693473082 0.741266653263459 61 0.252500930293199 0.505001860586398 0.747499069706801 62 0.216592098711134 0.433184197422268 0.783407901288866 63 0.205460321136167 0.410920642272335 0.794539678863833 64 0.246312726510433 0.492625453020866 0.753687273489567 65 0.321923587485327 0.643847174970653 0.678076412514673 66 0.320250691587811 0.640501383175622 0.679749308412189 67 0.642022353647022 0.715955292705957 0.357977646352978 68 0.637200125177771 0.725599749644458 0.362799874822229 69 0.826601996467501 0.346796007064998 0.173398003532499 70 0.80682196869816 0.38635606260368 0.19317803130184 71 0.91941616535811 0.16116766928378 0.0805838346418899 72 0.900975203372913 0.198049593254174 0.0990247966270871 73 0.925424652008946 0.149150695982109 0.0745753479910544 74 0.911830087824751 0.176339824350498 0.0881699121752488 75 0.9135333219134 0.172933356173201 0.0864666780866007 76 0.90561310271734 0.18877379456532 0.09438689728266 77 0.96270940944963 0.0745811811007387 0.0372905905503694 78 0.953936011198765 0.0921279776024698 0.0460639888012349 79 0.945625289630345 0.108749420739309 0.0543747103696547 80 0.948571897875523 0.102856204248953 0.0514281021244765 81 0.93991598590597 0.120168028188062 0.060084014094031 82 0.938515674949129 0.122968650101742 0.0614843250508711 83 0.923338531636765 0.153322936726469 0.0766614683632347 84 0.90882303667518 0.182353926649642 0.0911769633248208 85 0.898561577993249 0.202876844013503 0.101438422006751 86 0.881092730286874 0.237814539426252 0.118907269713126 87 0.857342437349598 0.285315125300804 0.142657562650402 88 0.90926576745457 0.181468465090858 0.090734232545429 89 0.89123239772767 0.217535204544659 0.108767602272329 90 0.86929129928284 0.26141740143432 0.13070870071716 91 0.871560538888932 0.256878922222136 0.128439461111068 92 0.860819427534191 0.278361144931617 0.139180572465808 93 0.83722872999342 0.325542540013161 0.162771270006581 94 0.807972735470318 0.384054529059365 0.192027264529682 95 0.773282094046861 0.453435811906278 0.226717905953139 96 0.742048450518324 0.515903098963353 0.257951549481676 97 0.76213180222418 0.475736395551639 0.237868197775819 98 0.75606062493066 0.48787875013868 0.24393937506934 99 0.719275137739432 0.561449724521135 0.280724862260568 100 0.693707554077025 0.61258489184595 0.306292445922975 101 0.649913424931442 0.700173150137116 0.350086575068558 102 0.610430415302474 0.779139169395052 0.389569584697526 103 0.570858211782588 0.858283576434824 0.429141788217412 104 0.528404565927372 0.943190868145256 0.471595434072628 105 0.481823890237933 0.963647780475867 0.518176109762067 106 0.462415919556108 0.924831839112216 0.537584080443892 107 0.457827969709526 0.915655939419052 0.542172030290474 108 0.467894611120811 0.935789222241623 0.532105388879189 109 0.418048471439917 0.836096942879833 0.581951528560083 110 0.394670984620237 0.789341969240474 0.605329015379763 111 0.355851640083466 0.711703280166932 0.644148359916534 112 0.575223439591522 0.849553120816956 0.424776560408478 113 0.567343870842545 0.86531225831491 0.432656129157455 114 0.539389429135854 0.921221141728293 0.460610570864147 115 0.757170297981753 0.485659404036495 0.242829702018248 116 0.732994701241804 0.534010597516392 0.267005298758196 117 0.720593949600064 0.558812100799873 0.279406050399936 118 0.691732727760039 0.616534544479922 0.308267272239961 119 0.640849052583183 0.718301894833635 0.359150947416817 120 0.649751710913054 0.700496578173892 0.350248289086946 121 0.702047164437652 0.595905671124696 0.297952835562348 122 0.70661974625626 0.586760507487482 0.293380253743741 123 0.712383551522526 0.575232896954948 0.287616448477474 124 0.659179031869672 0.681641936260657 0.340820968130328 125 0.705189245566204 0.589621508867593 0.294810754433796 126 0.675340670345474 0.649318659309052 0.324659329654526 127 0.663677575151714 0.672644849696572 0.336322424848286 128 0.627186208535114 0.745627582929772 0.372813791464886 129 0.599799802557954 0.800400394884093 0.400200197442046 130 0.670663902252595 0.65867219549481 0.329336097747405 131 0.612975071225681 0.774049857548638 0.387024928774319 132 0.548631249414214 0.902737501171572 0.451368750585786 133 0.546504527371684 0.906990945256631 0.453495472628316 134 0.485063485107997 0.970126970215993 0.514936514892003 135 0.442524241104513 0.885048482209025 0.557475758895487 136 0.413629289551521 0.827258579103043 0.586370710448479 137 0.417216958397859 0.834433916795718 0.582783041602141 138 0.457091421954855 0.91418284390971 0.542908578045145 139 0.389298744105467 0.778597488210934 0.610701255894533 140 0.317194646521706 0.634389293043412 0.682805353478294 141 0.419416128333852 0.838832256667704 0.580583871666148 142 0.365062614194954 0.730125228389908 0.634937385805046 143 0.296876857386553 0.593753714773106 0.703123142613447 144 0.222858355679168 0.445716711358336 0.777141644320832 145 0.266401707577565 0.53280341515513 0.733598292422435 146 0.183729503285999 0.367459006571999 0.816270496714 147 0.238719425674762 0.477438851349524 0.761280574325238 148 0.147579571272891 0.295159142545783 0.852420428727109 149 0.0789849424412947 0.157969884882589 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/20/t129025648941t8dw1dkrmn50f/10vshg1290256541.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/20/t129025648941t8dw1dkrmn50f/10vshg1290256541.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/20/t129025648941t8dw1dkrmn50f/1prk41290256541.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/20/t129025648941t8dw1dkrmn50f/1prk41290256541.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/20/t129025648941t8dw1dkrmn50f/2prk41290256541.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/20/t129025648941t8dw1dkrmn50f/2prk41290256541.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/20/t129025648941t8dw1dkrmn50f/3hijp1290256541.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/20/t129025648941t8dw1dkrmn50f/3hijp1290256541.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/20/t129025648941t8dw1dkrmn50f/4hijp1290256541.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/20/t129025648941t8dw1dkrmn50f/4hijp1290256541.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/20/t129025648941t8dw1dkrmn50f/5hijp1290256541.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/20/t129025648941t8dw1dkrmn50f/5hijp1290256541.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/20/t129025648941t8dw1dkrmn50f/6s9ia1290256541.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/20/t129025648941t8dw1dkrmn50f/6s9ia1290256541.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/20/t129025648941t8dw1dkrmn50f/73jiv1290256541.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/20/t129025648941t8dw1dkrmn50f/73jiv1290256541.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/20/t129025648941t8dw1dkrmn50f/83jiv1290256541.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/20/t129025648941t8dw1dkrmn50f/83jiv1290256541.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/20/t129025648941t8dw1dkrmn50f/93jiv1290256541.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/20/t129025648941t8dw1dkrmn50f/93jiv1290256541.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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

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

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

Software written by Ed van Stee & Patrick Wessa

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