*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 19:18:29 +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/t129045343264ps8n03nxhw61b.htm/, Retrieved Mon, 22 Nov 2010 20:17:24 +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/t129045343264ps8n03nxhw61b.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 «

5 1 6 5 7 2 1 6 2 3 6 3 6 6 3 6 2 4 4 6 6 3 2 6 2 5 2 7 3 3 5 2 6 5 1 6 2 5 3 2 6 4 6 5 5 5 4 7 4 1 5 1 7 1 6 5 2 4 6 1 6 1 1 6 1 5 3 6 6 2 5 2 4 4 1 6 5 5 6 3 6 2 5 5 2 4 5 6 3 2 5 4 4 5 2 5 2 6 4 2 5 2 3 5 2 6 5 3 6 2 5 1 5 3 1 7 4 5 4 3 6 1 5 5 2 6 3 5 4 3 6 2 5 5 4 6 2 2 6 5 4 1 6 7 2 5 3 7 2 5 6 4 2 4 5 4 3 3 6 1 5 5 6 5 6 5 2 5 5 3 5 1 7 5 4 7 2 5 6 6 7 5 6 6 5 6 1 5 1 5 7 3 3 4 3 6 2 7 2 3 5 3 5 3 5 6 2 5 4 2 4 2 6 5 2 6 3 2 4 3 5 3 7 4 5 5 5 3 3 2 6 3 6 4 4 6 2 7 6 5 5 1 5 4 2 6 6 4 5 2 5 6 6 4 5 5 3 7 5 6 5 5 2 6 6 6 4 2 6 5 6 3 2 4 4 5 2 5 4 3 7 7 2 6 7 6 2 5 4 7 5 2 6 2 5 5 2 2 6 2 6 2 4 5 6 5 3 6 6 6 5 5 4 6 4 6 2 3 5 5 6 5 3 5 2 3 2 3 5 6 5 1 6 5 3 5 3 6 3 2 6 4 5 4 2 5 2 3 1 5 5 4 3 5 3 4 4 2 2 4 5 3 3 6 5 5 2 3 5 7 2 1 5 2 2 6 5 3 6 5 6 2 5 5 6 6 4 2 6 4 6 4 5 3 3 5 4 6 4 6 5 2 6 4 5 6 2 5 4 2 5 2 2 4 5 5 2 6 5 3 6 3 7 2 6 3 5 5 3 5 6 1 5 5 1 3 2 2 6 5 5 2 5 5 2 5 2 6 6 1 6 5 5 3 4 5 2 5 4 2 6 1 4 4 3 6 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 11 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Quiet_Outgoing[t] = + 6.61747451124493 -0.219240011126145Use_hands[t] -0.12159210020993Hand_on_hips[t] -0.279781546959985individual[t] + 0.12278254111577Cry[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) 6.61747451124493 0.695947 9.5086 0 0 Use_hands -0.219240011126145 0.117568 -1.8648 0.064053 0.032026 Hand_on_hips -0.12159210020993 0.09218 -1.3191 0.189042 0.094521 individual -0.279781546959985 0.091435 -3.0599 0.0026 0.0013 Cry 0.12278254111577 0.071567 1.7156 0.088177 0.044089 Multiple Linear Regression - Regression Statistics Multiple R 0.355223107204072 R-squared 0.126183455891716 Adjusted R-squared 0.104200649750627 F-TEST (value) 5.74009774192846 F-TEST (DF numerator) 4 F-TEST (DF denominator) 159 p-value 0.000242967852694509 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.58435955092612 Sum Squared Residuals 399.121034671118 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 6 4.86025240841482 1.13974759158517 2 6 5.86618691821005 0.133813081789946 3 6 3.62691648544567 2.37308351455433 4 4 4.67641930292288 -0.676419302922883 5 2 3.50413394432991 -1.50413394432991 6 7 4.8070932376617 2.19290676233830 7 6 4.00196506151019 1.99803493848981 8 5 4.46507068541979 0.534929314580211 9 6 4.03067101442727 1.96932898557273 10 7 4.03856240805032 2.96143759194968 11 7 5.85659605513891 1.14340394486109 12 4 3.72218351455021 0.277816485449791 13 1 3.62453560363399 -2.62453560363399 14 6 3.72337395545605 2.27662604454395 15 4 4.28174660847018 -0.281746608470179 16 5 3.38373228502581 1.61626771497419 17 5 3.90550759149982 1.09449240850018 18 6 4.53877440704229 1.46122559295771 19 4 3.88156340220610 0.118436597793896 20 6 4.40452914958595 1.59547085041405 21 3 4.12474760262596 -1.12474760262596 22 3 3.26094974391004 -0.260949743910044 23 5 4.68312025564009 0.316879744359906 24 5 3.84564746802957 1.15435253197043 25 5 4.02709969170975 0.972900308290251 26 5 4.18647957936564 0.813520420634356 27 5 4.15107267373136 0.848927326268641 28 2 3.99407366788714 -1.99407366788714 29 6 3.90601662004207 2.09398337995793 30 7 5.2108477666433 1.7891522333567 31 2 4.31045256138725 -2.31045256138725 32 3 3.81983142546642 -0.819831425466424 33 6 4.25110146645925 1.74889853354075 34 5 4.24753014374173 0.752469856258266 35 7 4.49190478506743 2.50809521493257 36 5 3.89761619787677 1.10238380212323 37 6 3.41005735613121 2.58994264386879 38 5 5.514573502897 -0.514573502896999 39 3 3.9672395682395 -0.967239568239499 40 7 4.86763477349554 2.13236522650446 41 5 4.93106621968331 0.0689337803166858 42 5 4.1852891384598 0.814710861540196 43 6 4.34398761375211 1.65601238624789 44 2 4.18647957936564 -2.18647957936564 45 7 4.65128467272333 2.34871532727667 46 3 4.31953439591614 -1.31953439591614 47 6 4.30926212048141 1.69073787951859 48 7 3.99407366788714 3.00592633211286 49 5 4.52612124979588 0.473878750204121 50 4 3.4191391906601 0.580860809339901 51 6 4.28650837209354 1.71349162790646 52 7 4.49428566687911 2.50571433312089 53 2 3.97131991949927 -1.97131991949927 54 2 3.75088946746728 -1.75088946746728 55 2 4.30926212048141 -2.30926212048141 56 5 4.52731169070172 0.472688309298281 57 2 3.41243823794289 -1.41243823794289 58 5 4.79920184403865 0.200798155961346 59 6 5.33243986685323 0.66756013314677 60 2 3.84496605566598 -1.84496605566598 61 4 4.3966377559629 -0.396637755962899 62 6 4.21450411991913 1.78549588008087 63 4 3.72575483726773 0.274245162732271 64 3 4.27385521484713 -1.27385521484713 65 3 3.54073129087003 -0.54073129087003 66 3 5.05435778934133 -2.05435778934133 67 6 4.36912224395166 1.63087775604834 68 6 4.56271859633600 1.43728140366400 69 5 3.94210493803994 1.05789506196006 70 3 5.61222141381321 -2.61222141381321 71 3 4.00434594332187 -1.00434594332187 72 2 5.18571313644374 -3.18571313644374 73 3 4.09172157880336 -1.09172157880336 74 3 4.73866030820481 -1.73866030820481 75 5 5.74340437709428 -0.743404377094284 76 3 3.62929736725735 -0.629297367257354 77 5 4.3966377559629 0.603362244037101 78 2 3.62810692635151 -1.62810692635151 79 5 4.3446690261157 0.655330973884301 80 6 4.65247511362917 1.34752488637083 81 6 4.77287677293326 1.22712322706674 82 5 4.1852891384598 0.814710861540196 83 2 4.77287677293326 -2.77287677293326 84 6 4.24753014374173 1.75246985625827 85 7 5.11439029663292 1.88560970336708 86 5 5.12636204151574 -0.126362041515745 87 5 3.90431715059398 1.09568284940602 88 2 4.65179370126558 -2.65179370126558 89 5 4.12474760262596 0.875252397374036 90 6 3.72218351455021 2.27781648544979 91 5 4.34585946702154 0.654140532978461 92 5 4.40452914958595 0.595470850414051 93 4 4.4296637797855 -0.429663779785504 94 5 4.8334183087671 0.166581691232901 95 4 4.79801140313281 -0.798011403132814 96 2 3.72524580872548 -1.72524580872548 97 3 4.65009423181749 -1.65009423181749 98 5 5.89251198931545 -0.89251198931545 99 2 4.06301562588628 -2.06301562588628 100 2 4.250592437917 -2.25059243791700 101 4 4.02709969170975 -0.0270996917097486 102 3 4.02829013261559 -1.02829013261559 103 5 4.49309522597327 0.506904774026726 104 5 4.46557971396204 0.534420286037961 105 2 4.1852891384598 -2.18528913845980 106 5 4.98922687370547 0.0107731262945261 107 2 4.02709969170975 -2.02709969170975 108 6 5.05384876079908 0.946151239200916 109 2 3.38373228502581 -1.38373228502581 110 1 3.62572604453983 -2.62572604453983 111 6 5.9004033829385 0.0995966170615005 112 2 3.10156985625415 -1.10156985625415 113 3 4.00196506151019 -1.00196506151019 114 5 3.84734693747766 1.15265306252234 115 4 4.1606635368025 -0.160663536802499 116 4 3.57664722504656 0.423352774953436 117 6 4.46388024451395 1.53611975548605 118 2 4.21331367901329 -2.21331367901329 119 7 3.71429212092716 3.28570787907284 120 2 4.18579816700205 -2.18579816700205 121 5 4.02829013261559 0.971709867384411 122 3 4.71233523709942 -1.71233523709942 123 3 4.19845132424846 -1.19845132424846 124 5 3.90550759149982 1.09449240850018 125 5 4.2853179311877 0.7146820688123 126 2 3.72218351455021 -1.72218351455021 127 4 3.96962045005118 0.0303795499488210 128 3 3.62453560363399 -0.624535603633994 129 2 3.5041339443299 -1.50413394432990 130 6 4.40452914958595 1.59547085041405 131 6 5.0777929500928 0.922207049907201 132 3 3.90550759149982 -0.905507591499819 133 2 3.56399406780015 -1.56399406780015 134 6 5.46481327104014 0.53518672895986 135 6 4.65009423181749 1.34990576818251 136 2 5.89370243022129 -3.89370243022129 137 5 4.31045256138725 0.689547438612746 138 6 4.53088301341924 1.46911698658076 139 5 5.17544086100901 -0.175440861009014 140 3 4.71971760218022 -1.71971760218022 141 7 5.27359780046748 1.72640219953252 142 5 4.24633970283589 0.753660297164106 143 4 4.40452914958595 -0.404529149585949 144 5 4.23963875011868 0.760361249881316 145 3 3.90550759149982 -0.905507591499819 146 2 5.24387379046590 -3.24387379046590 147 5 4.61587776708904 0.384122232910956 148 6 3.91747933638264 2.08252066361736 149 5 4.12474760262596 0.875252397374036 150 2 3.78510593219573 -1.78510593219573 151 3 3.78629637310157 -0.78629637310157 152 2 4.1522631146372 -2.1522631146372 153 6 4.31045256138725 1.68954743861275 154 6 3.59389046162307 2.40610953837693 155 2 3.96774859678175 -1.96774859678175 156 2 3.44189293904797 -1.44189293904797 157 3 3.7485085856556 -0.748508585655604 158 4 4.58666278562972 -0.586662785629719 159 6 4.02829013261559 1.97170986738441 160 2 3.89880663878261 -1.89880663878261 161 7 6.12202427587633 0.877975724123675 162 2 3.87129112677137 -1.87129112677137 163 2 4.53088301341924 -2.53088301341924 164 4 4.02709969170975 -0.0270996917097486 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.831574919901535 0.33685016019693 0.168425080098465 9 0.784832889087759 0.430334221824482 0.215167110912241 10 0.722832551008664 0.554334897982673 0.277167448991336 11 0.621968932331056 0.756062135337888 0.378031067668944 12 0.510296671600538 0.979406656798923 0.489703328399462 13 0.525488877857824 0.949022244284352 0.474511122142176 14 0.475200925524693 0.950401851049386 0.524799074475307 15 0.400356987219674 0.800713974439348 0.599643012780326 16 0.377720688489368 0.755441376978736 0.622279311510632 17 0.336166697539795 0.67233339507959 0.663833302460205 18 0.364082450366821 0.728164900733642 0.635917549633179 19 0.3537903680249 0.7075807360498 0.6462096319751 20 0.318223921225748 0.636447842451497 0.681776078774252 21 0.317536410112866 0.635072820225732 0.682463589887134 22 0.332363877705241 0.664727755410482 0.667636122294759 23 0.268146287536188 0.536292575072375 0.731853712463812 24 0.215319686682552 0.430639373365104 0.784680313317448 25 0.193670981261980 0.387341962523961 0.80632901873802 26 0.151070472421058 0.302140944842116 0.848929527578942 27 0.116675169581211 0.233350339162423 0.883324830418789 28 0.165299967010771 0.330599934021542 0.834700032989229 29 0.218051734109290 0.436103468218581 0.78194826589071 30 0.185022043009863 0.370044086019725 0.814977956990137 31 0.359607288194708 0.719214576389416 0.640392711805292 32 0.368091378861615 0.73618275772323 0.631908621138385 33 0.332781836654337 0.665563673308674 0.667218163345663 34 0.283855236043857 0.567710472087715 0.716144763956143 35 0.341167639057166 0.682335278114333 0.658832360942834 36 0.309102372542463 0.618204745084925 0.690897627457537 37 0.343198269302264 0.686396538604528 0.656801730697736 38 0.312353927911291 0.624707855822582 0.687646072088709 39 0.294690868361724 0.589381736723448 0.705309131638276 40 0.318685145401957 0.637370290803914 0.681314854598043 41 0.288508411241337 0.577016822482673 0.711491588758663 42 0.250909184699103 0.501818369398206 0.749090815300897 43 0.231985934297053 0.463971868594106 0.768014065702947 44 0.322309522064438 0.644619044128877 0.677690477935562 45 0.333089045018488 0.666178090036977 0.666910954981512 46 0.374142852328572 0.748285704657144 0.625857147671428 47 0.363418447665852 0.726836895331704 0.636581552334148 48 0.445432513287153 0.890865026574306 0.554567486712847 49 0.398903409523817 0.797806819047633 0.601096590476183 50 0.355782158197247 0.711564316394494 0.644217841802753 51 0.336002124651723 0.672004249303446 0.663997875348277 52 0.352373581594744 0.704747163189488 0.647626418405256 53 0.501738173692062 0.996523652615875 0.498261826307938 54 0.557420171903556 0.885159656192888 0.442579828096444 55 0.64348489740819 0.71303020518362 0.35651510259181 56 0.601978919913148 0.796042160173703 0.398021080086852 57 0.601709666463514 0.796580667072971 0.398290333536486 58 0.558455468267283 0.883089063465433 0.441544531732717 59 0.518733644670788 0.962532710658424 0.481266355329212 60 0.566173474454029 0.867653051091942 0.433826525545971 61 0.528934337876689 0.942131324246623 0.471065662123311 62 0.525988482130094 0.948023035739813 0.474011517869906 63 0.482668799485665 0.96533759897133 0.517331200514335 64 0.47784416973148 0.95568833946296 0.52215583026852 65 0.438385743666496 0.876771487332992 0.561614256333504 66 0.528228210993104 0.943543578013792 0.471771789006896 67 0.523817613162026 0.952364773675948 0.476182386837974 68 0.511029825812001 0.977940348375997 0.488970174187999 69 0.486624454799359 0.973248909598717 0.513375545200641 70 0.592514456146446 0.814971087707109 0.407485543853554 71 0.573576651794957 0.852846696410085 0.426423348205043 72 0.712119796126562 0.575760407746877 0.287880203873438 73 0.695892736704694 0.608214526590612 0.304107263295306 74 0.707020165865313 0.585959668269375 0.292979834134687 75 0.677942509306412 0.644114981387176 0.322057490693588 76 0.642604237311285 0.71479152537743 0.357395762688715 77 0.605195503323946 0.789608993352108 0.394804496676054 78 0.609653906351862 0.780692187296277 0.390346093648138 79 0.57523887954566 0.84952224090868 0.42476112045434 80 0.56678871995631 0.86642256008738 0.43321128004369 81 0.549364883168808 0.901270233662384 0.450635116831192 82 0.518780874222635 0.96243825155473 0.481219125777365 83 0.612896408312641 0.774207183374719 0.387103591687359 84 0.62335421044169 0.753291579116621 0.376645789558310 85 0.651624878225901 0.696750243548198 0.348375121774099 86 0.608355383758547 0.783289232482906 0.391644616241453 87 0.590854910449019 0.818290179101961 0.409145089550981 88 0.676250894583448 0.647498210833103 0.323749105416551 89 0.651214399839254 0.697571200321492 0.348785600160746 90 0.705396776072704 0.589206447854593 0.294603223927296 91 0.681937435101271 0.636125129797458 0.318062564898729 92 0.654440279010577 0.691119441978846 0.345559720989423 93 0.619767184728349 0.760465630543302 0.380232815271651 94 0.580366955540278 0.839266088919444 0.419633044459722 95 0.546051934564032 0.907896130871935 0.453948065435968 96 0.543366715390917 0.913266569218167 0.456633284609083 97 0.544474631276729 0.911050737446541 0.455525368723271 98 0.512046416986292 0.975907166027415 0.487953583013708 99 0.548242653561863 0.903514692876273 0.451757346438137 100 0.576829454652422 0.846341090695156 0.423170545347578 101 0.537391038548177 0.925217922903646 0.462608961451823 102 0.508284115382413 0.983431769235175 0.491715884617587 103 0.466029281313202 0.932058562626404 0.533970718686798 104 0.428538097972856 0.857076195945711 0.571461902027144 105 0.457732288617259 0.915464577234518 0.542267711382741 106 0.41690520154178 0.83381040308356 0.58309479845822 107 0.433587116569585 0.86717423313917 0.566412883430415 108 0.405654837902095 0.811309675804191 0.594345162097905 109 0.389514642819117 0.779029285638234 0.610485357180883 110 0.455906237480357 0.911812474960714 0.544093762519643 111 0.415133768730378 0.830267537460757 0.584866231269622 112 0.387512586451385 0.77502517290277 0.612487413548615 113 0.35348912960386 0.70697825920772 0.64651087039614 114 0.329255296499057 0.658510592998115 0.670744703500943 115 0.285517230230214 0.571034460460429 0.714482769769786 116 0.247789040206947 0.495578080413893 0.752210959793054 117 0.278325981961714 0.556651963923427 0.721674018038286 118 0.326188603420183 0.652377206840366 0.673811396579817 119 0.47047495204128 0.94094990408256 0.52952504795872 120 0.510196115026302 0.979607769947396 0.489803884973698 121 0.492453859595817 0.984907719191633 0.507546140404183 122 0.51058514222899 0.97882971554202 0.48941485777101 123 0.503261016749916 0.993477966500168 0.496738983250084 124 0.502097459359854 0.995805081280292 0.497902540640146 125 0.461348314172334 0.922696628344667 0.538651685827666 126 0.464827870234187 0.929655740468374 0.535172129765813 127 0.422269003842121 0.844538007684242 0.577730996157879 128 0.371649583752259 0.743299167504518 0.628350416247741 129 0.356806792572043 0.713613585144086 0.643193207427957 130 0.388141971072940 0.776283942145881 0.611858028927060 131 0.418224623335169 0.836449246670338 0.581775376664831 132 0.367104798950526 0.734209597901052 0.632895201049474 133 0.414486884797262 0.828973769594524 0.585513115202738 134 0.357756022890656 0.715512045781312 0.642243977109344 135 0.376861659153839 0.753723318307677 0.623138340846161 136 0.645714834214369 0.708570331571263 0.354285165785631 137 0.638379697756113 0.723240604487773 0.361620302243887 138 0.673320287901376 0.653359424197247 0.326679712098624 139 0.620788819664864 0.758422360670273 0.379211180335136 140 0.566281328410461 0.867437343179079 0.433718671589539 141 0.512602857976491 0.974794284047017 0.487397142023509 142 0.450289745690266 0.900579491380532 0.549710254309734 143 0.37867730122754 0.75735460245508 0.62132269877246 144 0.330620056307101 0.661240112614202 0.669379943692899 145 0.268153222551356 0.536306445102712 0.731846777448644 146 0.296018661398473 0.592037322796946 0.703981338601527 147 0.239248064528523 0.478496129057046 0.760751935471477 148 0.206275707332275 0.412551414664549 0.793724292667725 149 0.171705281044453 0.343410562088906 0.828294718955547 150 0.143473191724185 0.28694638344837 0.856526808275815 151 0.0985175093580316 0.197035018716063 0.901482490641968 152 0.0915470409585369 0.183094081917074 0.908452959041463 153 0.124989099515612 0.249978199031223 0.875010900484388 154 0.521243853527871 0.957512292944259 0.478756146472129 155 0.498666845471318 0.997333690942635 0.501333154528682 156 0.35459951737429 0.70919903474858 0.64540048262571 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/Nov/22/t129045343264ps8n03nxhw61b/102t9p1290453497.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t129045343264ps8n03nxhw61b/102t9p1290453497.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t129045343264ps8n03nxhw61b/1escd1290453497.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t129045343264ps8n03nxhw61b/1escd1290453497.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t129045343264ps8n03nxhw61b/2ojcg1290453497.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t129045343264ps8n03nxhw61b/2ojcg1290453497.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t129045343264ps8n03nxhw61b/3ojcg1290453497.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t129045343264ps8n03nxhw61b/3ojcg1290453497.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t129045343264ps8n03nxhw61b/4ojcg1290453497.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t129045343264ps8n03nxhw61b/4ojcg1290453497.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t129045343264ps8n03nxhw61b/5zstj1290453497.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t129045343264ps8n03nxhw61b/5zstj1290453497.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t129045343264ps8n03nxhw61b/6zstj1290453497.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t129045343264ps8n03nxhw61b/6zstj1290453497.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t129045343264ps8n03nxhw61b/7ajsm1290453497.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t129045343264ps8n03nxhw61b/7ajsm1290453497.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t129045343264ps8n03nxhw61b/8ajsm1290453497.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t129045343264ps8n03nxhw61b/8ajsm1290453497.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t129045343264ps8n03nxhw61b/92t9p1290453497.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t129045343264ps8n03nxhw61b/92t9p1290453497.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

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

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

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

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